2026
Campos, Ron; Vayani, Ashmal; Kulkarni, Parth Parag; Gupta, Rohit; Zafar, Aizan; Dutta, Aritra; Shah, Mubarak
GAEA: A Geolocation Aware Conversational Model Conference
The IEEE/CVF Winter conference on Applications of Computer Vision, 2026.
Abstract | Tags: WACV | Links:
@conference{Campos2026,
title = {GAEA: A Geolocation Aware Conversational Model},
author = {Ron Campos and Ashmal Vayani and Parth Parag Kulkarni and Rohit Gupta and Aizan Zafar and Aritra Dutta and Mubarak Shah},
url = {https://ucf-crcv.github.io/GAEA/#bibtex
https://arxiv.org/abs/2503.16423
https://huggingface.co/collections/ucf-crcv/gaea-67d514a61d48eb1708b13a08
https://github.com/UCF-CRCV/GAEA},
year = {2026},
date = {2026-03-06},
urldate = {2026-03-06},
publisher = {The IEEE/CVF Winter conference on Applications of Computer Vision},
abstract = {Image geolocalization, in which an AI model traditionally predicts the precise GPS coordinates of an image, is a challenging task with many downstream applications. However, the user cannot utilize the model to further their knowledge beyond the GPS coordinates; the model lacks an understanding of the location and the conversational ability to communicate with the user. In recent days, with the tremendous progress of large multimodal models (LMMs)---proprietary and open-source---researchers have attempted to geolocalize images via LMMs. However, the issues remain unaddressed; beyond general tasks, for more specialized downstream tasks, such as geolocalization, LMMs struggle. In this work, we propose solving this problem by introducing a conversational model, GAEA, that provides information regarding the location of an image as the user requires. No large-scale dataset enabling the training of such a model exists. Thus, we propose GAEA-1.4M, a comprehensive dataset comprising over 800k images and approximately 1.4M question-answer pairs, constructed by leveraging OpenStreetMap (OSM) attributes and geographical context clues. For quantitative evaluation, we propose a diverse benchmark, GAEA-Bench, comprising 3.5k image-text pairs to evaluate conversational capabilities equipped with diverse question types. We consider 11 state-of-the-art open-source and proprietary LMMs and demonstrate that GAEA significantly outperforms the best open-source model, LLaVA-OneVision, by 18.2% and the best proprietary model, GPT-4o, by 7.2%.},
keywords = {WACV},
pubstate = {published},
tppubtype = {conference}
}
Image geolocalization, in which an AI model traditionally predicts the precise GPS coordinates of an image, is a challenging task with many downstream applications. However, the user cannot utilize the model to further their knowledge beyond the GPS coordinates; the model lacks an understanding of the location and the conversational ability to communicate with the user. In recent days, with the tremendous progress of large multimodal models (LMMs)---proprietary and open-source---researchers have attempted to geolocalize images via LMMs. However, the issues remain unaddressed; beyond general tasks, for more specialized downstream tasks, such as geolocalization, LMMs struggle. In this work, we propose solving this problem by introducing a conversational model, GAEA, that provides information regarding the location of an image as the user requires. No large-scale dataset enabling the training of such a model exists. Thus, we propose GAEA-1.4M, a comprehensive dataset comprising over 800k images and approximately 1.4M question-answer pairs, constructed by leveraging OpenStreetMap (OSM) attributes and geographical context clues. For quantitative evaluation, we propose a diverse benchmark, GAEA-Bench, comprising 3.5k image-text pairs to evaluate conversational capabilities equipped with diverse question types. We consider 11 state-of-the-art open-source and proprietary LMMs and demonstrate that GAEA significantly outperforms the best open-source model, LLaVA-OneVision, by 18.2% and the best proprietary model, GPT-4o, by 7.2%.
2022
Pestana, Camilo; Akhtar, Naveed; Rahnavard, Nazanin; Shah, Mubarak; Mian, Ajmal
Transferable 3D Adversarial Textures using End-to-end Optimization Conference
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022.
Abstract | Tags: Adversarial Attacks, WACV | Links:
@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 = {Adversarial Attacks, WACV},
pubstate = {published},
tppubtype = {conference}
}
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.
2021
Rana, Aayush; Rawat, Yogesh Singh
We don’t Need Thousand Proposals: Single Shot Actor-Action Detection in Videos Conference
IEEE 2021 Winter Conference on Applications of Computer Vision (WACV), 2021.
Tags: WACV | Links:
@conference{Rana2021,
title = {We don’t Need Thousand Proposals: Single Shot Actor-Action Detection in Videos},
author = {Aayush Rana and Yogesh Singh Rawat
},
url = {https://www.crcv.ucf.edu/wp-content/uploads/2020/12/Projects_Single-shot-actor-action-detection-in-videos.pdf
https://www.crcv.ucf.edu/research/projects/we-dont-need-thousand-proposals-single-shot-actor-action-detection-in-videos/
https://youtu.be/GHKpr5VTbV8},
year = {2021},
date = {2021-01-05},
urldate = {2021-01-05},
booktitle = {IEEE 2021 Winter Conference on Applications of Computer Vision (WACV)},
keywords = {WACV},
pubstate = {published},
tppubtype = {conference}
}