Three Papers Accepted in ICLR 2023

@conference{Yang2023,

title = {AIM: Adapting Image Models for Efficient Video Understanding},

author = {Taojiannan Yang and Yi Zhu and Yusheng Xie and Aston Zhang and Chen Chen and Mu Li},

year  = {2023},

date = {2023-05-01},

urldate = {2023-05-01},

booktitle = {AIM: Adapting Image Models for Efficient Video Understanding},

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

abstract = {Recent vision transformer based video models mostly follow the ``image pre-training then finetuning" paradigm and have achieved great success on multiple video benchmarks. However, full finetuning such a video model could be computationally expensive and unnecessary, given the pre-trained image transformer models have demonstrated exceptional transferability. In this work, we propose a novel method to Adapt pre-trained Image Models (AIM) for efficient video understanding. By freezing the pre-trained image model and adding a few lightweight Adapters, we introduce spatial adaptation, temporal adaptation and joint adaptation to gradually equip an image model with spatiotemporal reasoning capability. We show that our proposed AIM can achieve competitive or even better performance than prior arts with substantially fewer tunable parameters on four video action recognition benchmarks. Thanks to its simplicity, our method is also generally applicable to different image pre-trained models, which has the potential to leverage more powerful image foundation models in the future. },

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{Beetham2023b,

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

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

url = {https://arxiv.org/abs/2309.10058},

year  = {2023},

date = {2023-05-01},

booktitle = {Eleventh International Conference on Learning Representations},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}