Mubarak Shah

@conference{Zhu2023,

title = {ReFormer: Unified retrieval and ranking Transformer for Place Recognition},

author = {Sijie Zhu and Linjie Yang and Chen Chen and Mubarak Shah and Xiaohui Shen and Heng Wang},

year  = {2023},

date = {2023-06-18},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Gupta2023b,

title = {Class Prototypes based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos},

author = {Rohit Gupta and Anirban Roy and Sujeong Kim and Claire Christensen and Todd Grindal and Sarah Nixon Gerard and Madeline Cincebeaux and Ajay Divakaran and Mubarak Shah},

year  = {2023},

date = {2023-06-18},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Dave2023,

title = {TimeBalance: Temporally-Invariant and Temporally-Distinctive Video Representations for Semi-Supervised Action Recognition},

author = {Ishan Rajendrakumar Dave and Mamshad Nayeem Rizve and Chen Chen and Mubarak Shah},

year  = {2023},

date = {2023-06-18},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Rizve2023,

title = {PivoTAL: Prior-Driven Supervision for Weakly-Supervised Temporal Action Localization},

author = {Mamshad Nayeem Rizve and Gaurav Mittal and Ye Yu and Matthew Hall and Sandra Sajeev and Mubarak Shah and Mei Chen},

year  = {2023},

date = {2023-06-18},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Urooj2023,

title = {Learning to Predict Situation Hyper-Graphs for Video Question Answering},

author = {Aisha Urooj and Hilde Kuehne and Bo Wu and Kim Chheu and Walid Bousselham and Chuang Gan and Niels Lobo and Mubarak Shah},

year  = {2023},

date = {2023-06-18},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Bhunia2023,

title = {Person Image Synthesis via Denoising Diffusion Model},

author = {Ankan Kumar Bhunia and Salman Khan and Hisham Cholakkal and Rao Muhammad Anwer and Jorma Tapio Laaksonen and Mubarak Shah and Fahad Khan},

year  = {2023},

date = {2023-06-18},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Wasim2023,

title = {Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting},

author = {Syed Talal Wasim and Muzammal Naseer and Salman Khan and Fahad Khan and Mubarak Shah},

year  = {2023},

date = {2023-06-18},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Clark2023,

title = {Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes},

author = {Brandon Eric Clark and Alec Kerrigan and Parth Parag Kulkarni and Vicente Vivanco Cepeda and Mubarak Shah},

year  = {2023},

date = {2023-06-18},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Kini2023,

title = {3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds},

author = {Jyoti Kini and Ajmal Mian and Mubarak Shah},

url = {https://arxiv.org/pdf/2211.00746.pdf},

year  = {2023},

date = {2023-05-29},

urldate = {2023-05-29},

booktitle = {IEEE International Conference on Robotics and Automation},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Sangam2023,

title = {TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos},

author = {Tushar Sangam and Ishan Rajendrakumar Dave and Waqas Sultani and Mubarak Shah},

url = {https://arxiv.org/pdf/2210.08423.pdf},

year  = {2023},

date = {2023-05-29},

booktitle = {IEEE International Conference on Robotics and Automation},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Beetham2023,

title = {Dual Student Networks for Data-Free Model Stealing},

author = {James Beetham and Navid Kardan and Ajmal Mian and Mubarak Shah},

year  = {2023},

date = {2023-05-01},

urldate = {2023-05-01},

booktitle = {Dual Student Networks for Data-Free Model Stealing },

publisher = {Eleventh International Conference on Learning Representations (ICLR)},

abstract = {Data-free model stealing aims to replicate a target model without direct access to either the training data or the target model. To accomplish this, existing methods use a generator to produce samples in order to train a student model to match the target model outputs. To this end, the two main challenges are estimating gradients of the target model without access to its parameters, and generating a diverse set of images that thoroughly explores the input space. We propose a Dual Student method where two students are symmetrically trained in order to provide the generator a criterion to generate samples that the two students disagree on. On one hand, disagreement on a sample implies at least one student has classified the sample incorrectly when compared with the target model. This push towards disagreeing samples implicitly encourages exploring a more diverse region of input space. On the other hand, our method utilizes gradients of student models to indirectly estimate gradients of the target model. We show that this novel training objective for the generator network is equivalent to optimizing a lower bound on the generator’s loss if we had access to the target model gradients. In other words, our method alters the standard data-free model stealing paradigm by substituting the target model with a separate student model, thereby creating a lower bound which can be directly optimized without additional target model queries or separate synthetic datasets. We show that our new optimization framework provides more accurate gradient estimation of the target model and better accuracies on benchmark classification datasets. Additionally, our approach balances improved query efficiency with training computation cost. Finally, we demonstrate that our method serves as a better proxy model for transfer-based adversarial attacks than existing data-free model stealing methods.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {Re-calibrating Feature Attributions for Model Interpretation},

author = {Peiyu Yang and Naveed Akhtar and Zeyi Wen and Mubarak Shah and Ajmal Mian},

year  = {2023},

date = {2023-05-01},

urldate = {2023-05-01},

booktitle = {Re-calibrating Feature Attributions for Model Interpretation},

publisher = {Eleventh International Conference on Learning Representations (ICLR), notable top 25%},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Vahidian2023,

title = {Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces},

author = {Saeed Vahidian and Mahdi Morafah and Weijia Wang and Vyacheslav Kungurtsev and Chen Chen and Mubarak Shah and Bill Lin},

url = {https://arxiv.org/abs/2209.10526},

year  = {2023},

date = {2023-02-07},

urldate = {2023-02-07},

publisher = {37th AAAI Conference on Artificial Intelligence},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Gupta2023,

title = {Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility},

author = {Rohit Gupta and Naveed Akhtar and Ajmal Mian and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/2207.10862.pdf},

year  = {2023},

date = {2023-02-07},

publisher = {37th AAAI Conference on Artificial Intelligence},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Xu2022,

title = {Don’t Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation},

author = {Ziwei Xu and Yogesh Rawat and Yongkang Wong and Mohan Kankanhalli and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/ziwei_neurips2022.pdf

https://diff-tl.github.io/

https://github.com/ZiweiXU/DTL-action-segmentation},

year  = {2022},

date = {2022-11-09},

urldate = {2022-11-09},

publisher = {36th Conference on Neural Information Processing Systems (NeurIPS 2022)},

abstract = {We propose Differentiable Temporal Logic (DTL), a model-agnostic framework that introduces temporal constraints to deep networks. DTL treats the outputs of a network as a truth assignment of a temporal logic formula, and computes a temporal logic loss reflecting the consistency between the output and the constraints. We propose a comprehensive set of constraints, which are implicit in data annotations, and incorporate them with deep networks via DTL. We evaluate the effectiveness of DTL on the temporal action segmentation task and observe improved performance and reduced logical errors in the output of different task models. Furthermore, we provide an extensive analysis to visualize the desirable effects of DTL.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Khan2022,

title = {Weakly Supervised Grounding for VQA in Vision-Language Transformers},

author = {Aisha Urooj Khan and Hilde Kuehne and Chuang Gan and Niels Da Vitoria Lobo and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/1011.pdf

https://www.crcv.ucf.edu/wp-content/uploads/2018/11/1011-supp.pdf

https://github.com/aurooj/WSG-VQA-VLTransformers

https://youtu.be/dekmVb6lq3I},

year  = {2022},

date = {2022-10-23},

urldate = {2022-10-23},

booktitle = {European Conference on Computer Vision},

abstract = {Transformers for visual-language representation learning have been getting a lot of interest and shown tremendous performance on visual question answering (VQA) and grounding. However, most systems that show good performance of those tasks still rely on pre-trained object

detectors during training, which limits their applicability to the object classes available for those detectors. To mitigate this limitation, this paper

focuses on the problem of weakly supervised grounding in the context of visual question answering in transformers. Our approach leverages

capsules by transforming each visual token into a capsule representation in the visual encoder; it then uses activations from language self-attention layers as a text-guided selection module to mask those capsules before they are forwarded to the next layer. We evaluate our approach on the challenging GQA as well as VQA-HAT dataset for VQA grounding. Our experiments show that: while removing the information of masked

objects from standard transformer architectures leads to a significant drop in performance, the integration of capsules significantly improves the grounding ability of such systems and provides new state-of-the-art results compared to other approaches in the field.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Rizve2022b,

title = {Towards Realistic Semi-Supervised Learning},

author = {Mamshad Nayeem Rizve and Navid Kardan and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/7402.pdf

https://www.crcv.ucf.edu/wp-content/uploads/2018/11/7402-supp.pdf

https://github.com/nayeemrizve/TRSSL

https://youtu.be/mE7GeQ35WyY},

year  = {2022},

date = {2022-10-23},

urldate = {2022-10-23},

booktitle = {European Conference on Computer Vision},

abstract = {Deep learning is pushing the state-of-the-art in many computer

vision applications. However, it relies on large annotated data

repositories, and capturing the unconstrained nature of the real-world

data is yet to be solved. Semi-supervised learning (SSL) complements

the annotated training data with a large corpus of unlabeled data to

reduce annotation cost. The standard SSL approach assumes unlabeled

data are from the same distribution as annotated data. Recently, a more

realistic SSL problem, called open-world SSL, is introduced, where the

unannotated data might contain samples from unknown classes. In this paper, we propose a novel pseudo-label based approach to tackle SSL in

open-world setting. At the core of our method, we utilize sample uncertainty and incorporate prior knowledge about class distribution to  generate reliable class-distribution-aware pseudo-labels for unlabeled data belonging to both known and unknown classes. Our extensive experimentation showcases the effectiveness of our approach on several benchmark datasets, where it substantially outperforms the existing state-of-the art on seven diverse datasets including CIFAR-100 (∼17%), ImageNet-100 (∼5%), and Tiny ImageNet (∼9%). We also highlight the flexibility of our approach in solving novel class discovery task, demonstrate its stability in dealing with imbalanced data, and complement our approach with a technique to estimate the number of novel classes. Code: https://github.com/nayeemrizve/TRSSL},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Rizve2022,

title = {OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning},

author = {Mamshad Nayeem Rizve and Navid Kardan and Salman Khan and Fahad Shahbaz Khan and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/6665.pdf

https://www.crcv.ucf.edu/wp-content/uploads/2018/11/6665-supp.pdf

https://github.com/nayeemrizve/OpenLDN

https://youtu.be/p2lYqvklcjA},

year  = {2022},

date = {2022-10-23},

urldate = {2022-10-23},

booktitle = {European Conference on Computer Vision},

abstract = {Semi-supervised learning (SSL) is one of the dominant approaches to address the annotation bottleneck of supervised learning. Recent SSL methods can effectively leverage a large repository of unlabeled data to improve performance while relying on a small set of labeled data. One common assumption in most SSL methods is that the labeled and unlabeled data are from the same data distribution. However, this is hardly the case in many real-world scenarios, which limits their applicability. In this work, instead, we attempt to solve the challenging open-world SSL problem that does not make such an assumption. In the open-world SSL problem, the objective is to recognize samples of known classes, and simultaneously detect and cluster samples belonging to novel classes present in unlabeled data. This work introduces OpenLDN that utilizes a pairwise similarity loss to discover novel classes. Using a bi-level optimization rule this pairwise similarity loss exploits the information available in the labeled set to implicitly cluster novel class samples, while simultaneously recognizing samples from known classes. After discovering novel  classes, OpenLDN transforms the open-world SSL problem into a standard SSL problem to achieve additional performance gains using existing SSL methods. Our extensive experiments demonstrate that OpenLDN outperforms the current state-of-the-art methods on multiple popular classification benchmarks while providing a better accuracy/training time trade-off. Code: https://github.com/nayeemrizve/OpenLDN},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Vyas2022,

title = {GAMa: Cross-view Video Geo-localization},

author = {Shruti Vyas and Chen Chen and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/1512.pdf

https://www.crcv.ucf.edu/wp-content/uploads/2018/11/1512-supp.pdf

https://youtu.be/KSHuer_VXJo},

year  = {2022},

date = {2022-10-23},

urldate = {2022-10-23},

booktitle = {European Conference on Computer Vision},

abstract = {The existing work in cross-view geo-localization is based on images where a ground panorama is matched to an aerial image. In this work, we focus on ground videos instead of images which provides ad-ditional contextual cues which are important for this task. There are no existing datasets for this problem, therefore we propose GAMa dataset, a large-scale dataset with ground videos and corresponding aerial images. We also propose a novel approach to solve this problem. At clip-level, a short video clip is matched with corresponding aerial image and is later used to get video-level geo-localization of a long video. Moreover, we propose a hierarchical approach to further improve the clip-level geo-localization. On this challenging dataset, with unaligned images and lim-ited field of view, our proposed method achieves a Top-1 recall rate of 19.4% and 45.1% @1.0mile. Code & dataset are available at this link.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Kumar2022,

title = {Self Supervised Learning for Multiple Object Tracking in 3D Point Clouds},

author = {Aakash Kumar and Jyoti Kini and Ajmal Mian and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/camera_ready_paper.pdf},

year  = {2022},

date = {2022-10-23},

urldate = {2022-10-23},

booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems},

abstract = {Multiple object tracking in 3D point clouds has applications in mobile robots and autonomous driving. This is a challenging problem due to the sparse nature of the point clouds and the added difficulty of annotation in 3D for supervised learning. To overcome these challenges, we propose a neural network architecture that learns effective object features and their affinities in a self supervised fashion for multiple object tracking in 3D point clouds captured with LiDAR sensors. For self supervision, we use two approaches. First, we generate two augmented LiDAR frames from a single real frame by applying translation, rotation and cutout to the objects. Second, we synthesize a LiDAR frame using CAD models or primitive geometric shapes and then apply the above three augmentations to them. Hence, the ground truth object locations and associations are known in both frames for self supervision. This removes the need to annotate object associations in real data, and additionally the need for training data collection and annotation for object detection in synthetic data. To the best of our knowledge, this is the first self supervised multiple object tracking method for 3D data. Our model achieves state of the art results.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Pillai2023,

title = {DEEPSAR: Vessel Detection In SAR Imagery With Noisy Labels},

author = {Manu S Pillai and Abhijeet Bhattacharya and Tanmay Baweja and Rohit Gupta and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/ICIP_Submission.pdf},

year  = {2022},

date = {2022-10-16},

urldate = {2023-10-08},

publisher = {IEEE International Conference on Image Processing},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Kini2022b,

title = {Tag-Based Attention Guided Bottom-Up Approach for Video Instance Segmentation},

author = {Jyoti Kini and Mubarak Shah },

url = {https://arxiv.org/pdf/2204.10765.pdf},

year  = {2022},

date = {2022-08-21},

urldate = {2022-08-21},

booktitle = {26th International Conference on Pattern Recognition},

issue = {arxiv:2204.10765},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection},

author = {Nicolae-Catalin Ristea and Neelu Madan and Radu Tudor Ionescu and Kamal Nasrollahi and Fahad Shahbaz Khan and Thomas B. Moeslund and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/SSPCAB_camera-arxiv.pdf},

year  = {2022},

date = {2022-06-19},

urldate = {2022-06-19},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization},

author = {Sijie Zhu and Mubarak Shah and Chen Chen},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11695.pdf

https://www.crcv.ucf.edu/wp-content/uploads/2018/11/11695-supp.pdf},

year  = {2022},

date = {2022-06-19},

urldate = {2022-06-19},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {PSTR: End-to-End One-Step Person Search With Transformers},

author = {Jiale Cao and Yenwai Pang and Rao Muhammad Anwer and Hisham Cholakkal and Jin Xie and Mubarak Shah and Fahad Shahbaz Khan},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/05237-2.pdf

https://github.com/JialeCao001/PSTR},

year  = {2022},

date = {2022-06-19},

urldate = {2022-06-19},

publisher = {IEEE Computer Vision and Pattern Recognition},

abstract = {We propose a novel one-step transformer-based person search framework, PSTR, that jointly performs person detection and re-identification (re-id) in a single architecture. PSTR comprises a person search-specialized (PSS) module that contains a detection encoder-decoder for person detection along with a discriminative re-id decoder for person re-id. The discriminative re-id decoder utilizes a multi-level supervision scheme with a shared decoder for

discriminative re-id feature learning and also comprises a part attention block to encode relationship between different parts of a person. We further introduce a simple multi-scale scheme to support re-id across person instances at different scales. PSTR jointly achieves the diverse objectives of object-level recognition (detection) and instance-level matching (re-id). To the best of our knowledge, we are the first to propose an end-to-end one-step

transformer-based person search framework. Experiments are performed on two popular benchmarks: CUHK-SYSU and PRW. Our extensive ablations reveal the merits of the proposed contributions. Further, the proposed PSTR sets a new state-of-the-art on both benchmarks. On the challenging

PRW benchmark, PSTR achieves a mean average precision (mAP) score of 56.5%. The source code is available at https://github.com/JialeCao001/PSTR.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {OW-DETR: Open-world Detection Transformer},

author = {Akshita Gupta and Sanath Narayan and K J Joseph and Salman Khan and Fahad Shahbaz Khan and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/03815.pdf

https://www.crcv.ucf.edu/wp-content/uploads/2018/11/03815-supp.pdf

https://github.com/akshitac8/OW-DETR.},

year  = {2022},

date = {2022-06-19},

urldate = {2022-06-19},

publisher = {IEEE Computer Vision and Pattern Recognition},

abstract = {Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while  simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard object detection, the OWOD setting poses significant challenges for generating quality candidate proposals on potentially unknown objects, separating the unknown objects from the background and detecting diverse unknown objects. Here, we introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. The proposed OW-DETR comprises three dedicated components

namely, attention-driven pseudo-labeling, novelty classification and objectness scoring to explicitly address the aforementioned OWOD challenges. Our OW-DETR explicitly encodes multi-scale contextual information, possesses less inductive bias, enables knowledge transfer from known classes to the unknown class and can better discriminate between unknown objects and background. Comprehensive experiments are performed on two benchmarks: MS-COCO and PASCAL VOC. The extensive ablations reveal the merits of our proposed contributions. Further, our model outperforms the recently introduced OWOD approach, ORE, with absolute gains ranging from 1.8% to 3.3% in terms of unknown recall on MS-COCO. In the case of incremental

object detection, OW-DETR outperforms the state-of-the art for all settings on PASCAL VOC. Our code is available at https://github.com/akshitac8/OW-DETR.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection},

author = {Andra Acsintoae and Andrei Florescu and Mariana-Iuliana Georgescu and Tudor Mare and Paul Sumedrea and Radu Tudor Ionescu and Fahad Shahbaz Khan and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/04315.pdf

https://github.com/lilygeorgescu/UBnormal},

year  = {2022},

date = {2022-06-19},

urldate = {2022-06-19},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning},

author = {Nazmul Karim and Mamshad Nayeem Rizve and Nazanin Rahnavard and Ajmal Mian and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/07363.pdf

https://www.crcv.ucf.edu/wp-content/uploads/2018/11/07363-supp.pdf

https://github.com/nazmul-karim170/unicon-noisy-label},

year  = {2022},

date = {2022-06-19},

urldate = {2022-06-19},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{nokey,

title = {SPAct: Self-supervised Privacy Preservation for Action Recognition},

author = {Ishan Rajendrakumar Dave and Chen Chen and Mubarak Shah},

url = {https://arxiv.org/pdf/2203.15205.pdf

https://github.com/DAVEISHAN/SPAct

https://www.youtube.com/watch?v=_PAlMT7ozts},

year  = {2022},

date = {2022-06-19},

urldate = {2022-06-19},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Modi2022,

title = {Video Action Detection: Analysing Limitations and Challenges},

author = {Rajat Modi and Aayush Jung Rana and Akash Kumar and Praveen Tirupattur and Shruti Vyas and Yogesh Singh Rawat and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/2204.07892.pdf},

year  = {2022},

date = {2022-04-17},

urldate = {2022-04-17},

publisher = {IEEE Computer Vision and Pattern Recognition},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Pestana2022,

title = {Transferable 3D Adversarial Textures using End-to-end Optimization},

author = {Camilo Pestana and Naveed Akhtar and Nazanin Rahnavard and Mubarak Shah and Ajmal Mian},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/WACV_2022_Deceptive_Textures-1.pdf},

doi = {10.1109/WACV51458.2022.00080},

year  = {2022},

date = {2022-02-15},

urldate = {2022-02-15},

pages = {727-736},

publisher = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},

abstract = {Deep visual models are known to be vulnerable to adversarial attacks. The last few years have seen numerous techniques to compute adversarial inputs for these models. However, there are still under-explored avenues in this critical research direction. Among those is the estimation of adversarial textures for 3D models in an end-to-end optimization scheme. In this paper, we propose such a scheme to generate adversarial textures for 3D models that are highly transferable and invariant to different camera views and lighting conditions. Our method makes use of neural rendering with explicit control over the model texture and background. We ensure transferability of the adversarial textures by employing an ensemble of robust and non-robust models. Our technique utilizes 3D models as a proxy to simulate closer to real-life conditions, in contrast to conventional use of 2D images for adversarial attacks. We show the efficacy of our method with extensive experiments.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Kardan2022,

title = {Self-Joint Supervised Learning},

author = {Navid Kardan and Mitchell Hill and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Self_joint_ICLR-002.pdf

https://github.com/ndkn/Self-joint-Learning},

year  = {2022},

date = {2022-01-20},

urldate = {2022-01-20},

publisher = {International Conference on Learning Representations (ICLR)},

abstract = {Supervised learning is a fundamental framework used to train machine learning systems. A supervised learning problem is often formulated using an i.i.d. as-sumption that restricts model attention to a single relevant signal at a time when predicting. This contrasts with the human ability to actively use related samples as reference when making decisions. We hypothesize that the restriction to a single signal for each prediction in the standard i.i.d. framework contributes to well-known drawbacks of supervised learning: making overconfident predictions and vulnerability to overfitting, adversarial attacks, and out-of-distribution data. To address these limitations, we propose a new supervised learning paradigm called self-joint learning that generalizes the standard approach by modeling the joint conditional distribution of two observed samples, where each sample is an im-age and its label. Rather than assuming samples are independent, our models explicitly learn the sample-to-sample relation of conditional independence. Our framework can naturally incorporate auxiliary unlabeled data to further improve the performance. Experiments on benchmark image datasets show our method offers significant improvement over standard supervised learning in terms of ac-curacy, robustness against adversarial attacks, out-of-distribution detection, and overconfidence mitigation. Code: github.com/ndkn/Self-joint-Learning},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Kerrigan2021,

title = {Reformulating Zero-shot Action Recognition for Multi-label Actions},

author = {Alec Kerrigan and Kevin Duarte and Yogesh Singh Rawat and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/reformulating_zero_shot_action2.pdf

https://www.crcv.ucf.edu/wp-content/uploads/2018/11/ZSL-Supp.pdf

https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Reformulating-Zero-shot-Action-Recognition-forMulti-label-Actions.pptx},

year  = {2021},

date = {2021-12-06},

urldate = {2021-12-06},

booktitle = {Thirty-fifth Conference on Neural Information Processing Systems},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Rajasegaran2020,

title = {Self-supervised Knowledge Distillation for Few-shot Learning},

author = {Jathushan Rajasegaran and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2020/07/Publications_Self-supervised-Knowledge-Distillation-for-Few-shot-Learning.pdf

https://bmvc2021-virtualconference.com/conference/papers/paper_0820.html

https://github.com/brjathu/SKD},

year  = {2021},

date = {2021-11-22},

urldate = {2021-11-22},

booktitle = {British Machine Vision Conference, Nov 22-25},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Rajasegaran2021,

title = {Meta-learning the Learning Trends Shared Across Tasks},

author = {Jathushan Rajasegaran and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Mubarak Shah},

url = {https://www.bmvc2021-virtualconference.com/conference/papers/paper_0874.html},

year  = {2021},

date = {2021-11-22},

urldate = {2021-11-22},

booktitle = {British Machine Vision Conference, Nov 22-25},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{NarayanICCV2021,

title = {Discriminative Region-based Multi-Label Zero-Shot Learning},

author = {Sanath Narayan and Akshita Gupta and Salman Khan and Fahad Shahbaz Khan and Ling Shao and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/02617.pdf

https://www.crcv.ucf.edu/wp-content/uploads/2018/11/02617-supp.pdf},

year  = {2021},

date = {2021-10-11},

urldate = {2021-10-11},

booktitle = {International Conference on Computer Vision},

abstract = {Multi-label zero-shot learning (ZSL) is a more realistic counter-part of standard single-label ZSL since several objects can co-exist in a natural image.  However, the occurrence of multiple objects complicates the reasoning and re-quires region-specific processing of visual features to pre-serve their contextual cues.  We note that the best existing multi-label ZSL method takes a shared approach towards attending  to  region  features  with  a  common  set  of  attention  maps  for  all  the  classes.   Such  shared  maps  lead  to diffused attention, which does not discriminatively focus on relevant  locations  when  the  number  of  classes  are  large. Moreover, mapping spatially-pooled visual features to the class semantics leads to  inter-class  feature entanglement, thus  hampering  the  classification.   Here,  we  propose  an alternate approach towards region-based discriminability-preserving  multi-label  zero-shot  classification. Our  approach maintains the spatial resolution to preserve region-level characteristics and utilizes a bi-level attention module (BiAM) to enrich the features by incorporating both region and scene context information.   The enriched region-level features are then mapped to the class semantics and only their class predictions are spatially pooled to obtain image-level predictions,  thereby keeping the multi-class features

disentangled.  Our approach sets a new state of the art on two  large-scale  multi-label  zero-shot  benchmarks:   NUS-WIDE  and  Open  Images.   On  NUS-WIDE,  our  approach achieves an absolute gain of 6.9% mAP for ZSL, compared to the best published results.   Source code is available at https://github.com/akshitac8/BiAM.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ZaeemzadehICCV2021,

title = {Face Image Retrieval with Attribute Manipulation},

author = {Alireza Zaeemzadeh and Shabnam Ghadar and Baldo Faieta and Zhe Lin and Nazanin Rahnavard and Mubarak Shah and Ratheesh Kalarot},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/06328.pdf

https://www.crcv.ucf.edu/wp-content/uploads/2018/11/06328-supp.pdf},

year  = {2021},

date = {2021-10-11},

urldate = {2021-10-11},

booktitle = {International Conference on Computer Vision},

abstract = {Current face image retrieval solutions are limited, since they treat different facial attributes the same and cannot incorporate

user’s preference for a subset of attributes in their search criteria. This paper introduces a new face image retrieval framework, where the input face query is augmented by both an adjustment vector that specifies the desired modifications

to the facial attributes, and a preference vector that assigns different levels of importance to different attributes. For example, a user can ask for retrieving images similar to a query image, but with a different hair color, and no preference for absence/presence of eyeglasses in the results. To achieve this, we propose to disentangle the semantics, corresponding to various attributes, by learning a set of sparse and orthogonal basis vectors in the latent space of StyleGAN. Such basis vectors are then employed to decompose the dissimilarity between face images in terms of dissimilarity between their attributes, assign preference to the attributes, and adjust the attributes in the query. Enforcing sparsity on the basis vectors helps us to disentangle the latent space and adjust each attribute independently from other attributes, while enforcing orthogonality facilitates preference assignment and the dissimilarity decomposition. The effectiveness of our approach is illustrated by achieving state-of-the-art results for the face image retrieval task. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{BhuniaICCV2021,

title = {Handwriting Transformers},

author = {Ankan Kumar Bhunia and Salman Khan and Hisham Cholakkal and Rao Muhammad Anwer and Fahad Shahbaz Khan and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Handwriting_Generation_ICCV21.pdf

https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Handwriting_Generation_ICCV21_supp.pdf},

year  = {2021},

date = {2021-10-11},

urldate = {2021-10-11},

booktitle = {International Conference on Computer Vision},

abstract = {We propose a novel transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement as well as global and local writing style patterns. The proposed HWT captures the long and short range relationships within the style examples through a self-attention mechanism, thereby encoding both global and local style patterns. Further, the proposed transformer-based HWT comprises an encoder-decoder attention that enables style-content entanglement by gathering the style representation of each query character. To the best of our knowledge, we are the first to introduce a transformer-based generative network for styled handwritten text generation.



Our proposed HWT generates realistic styled handwritten text images and significantly outperforms the state-of-the-art demonstrated through extensive qualitative, quantitative and human-based evaluations. The proposed HWT can handle arbitrary length of text and any desired writing style in a few-shot setting. Further, our HWT generalizes well to the challenging scenario where both words and writing style are unseen during training, generating realistic styled handwritten text images.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{RegmiICCV2021,

title = {Video Geo-Localization Employing Geo-Temporal Feature Learning and GPS Trajectory Smoothing},

author = {Krishna Regmi and Mubarak Shah},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/video_geolocalization_ICCV2021.pdf

https://github.com/kregmi/VTE},

year  = {2021},

date = {2021-10-11},

urldate = {2021-10-11},

booktitle = {International Conference on Computer Vision},

abstract = {In this paper, we address the problem of video geolocalization by proposing a Geo-Temporal Feature Learning (GTFL) Network to simultaneously learn the discriminative features for the query video frames and the gallery images for estimating the geo-spatial trajectory of a query video. Based on a transformer encoder architecture, our

GTFL model encodes query and gallery data separately, via two dedicated branches. The proposed GPS Loss and Clip Triplet Loss exploit the geographical and temporal proximity between the frames and the clips to jointly learn the query and the gallery features. We also propose a deep learning approach to trajectory smoothing by predicting the outliers in the estimated GPS positions and learning the offsets to smooth the trajectory. We build a large dataset from four different regions of USA; New York, San Francisco, Berkeley and Bay Area using BDD driving videos as query, and by collecting corresponding Google StreetView (GSV) Images for gallery. Extensive

evaluations of proposed method on this new dataset are provided. Code and dataset details is publicly available at

https://github.com/kregmi/VTE.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Shiraz2021,

title = {Novel View Video Prediction using Dual Representation},

author = {Sarah Shiraz and Krishna Regmi and Shruti Vyas and Yogesh Singh Rawat and Mubarak Shah

},

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Projects_Novel-View-Video-Prediction-using-dual-Representation.pdf

https://www.crcv.ucf.edu/research/projects/novel-view-video-prediction-using-dual-representation/},

year  = {2021},

date = {2021-09-19},

urldate = {2021-09-19},

booktitle = {IEEE International Conference on Image Processing},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Swetha2021,

title = {Unsupervised Discriminative Embedding for Sub-Action Learning in Complex Activities},

author = {Sirnam Swetha and Hilde Kuehne and Yogesh Singh Rawat and Mubarak Shah },

url = {https://www.crcv.ucf.edu/wp-content/uploads/2018/11/Unsupervised-Discriminative-Embedding-for-Sub-Action-Learning-in-Complex-Activities.pdf},

year  = {2021},

date = {2021-09-19},

urldate = {2021-09-19},

booktitle = {IEEE International Conference on Image Processing},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}