Zobrazeno 1 - 10
of 2 281
pro vyhledávání: '"Chowdhury, A K"'
Existing DeepFake detection techniques primarily focus on facial manipulations, such as face-swapping or lip-syncing. However, advancements in text-to-video (T2V) and image-to-video (I2V) generative models now allow fully AI-generated synthetic conte
Externí odkaz:
http://arxiv.org/abs/2412.12278
Autor:
Bachu, Saketh, Shayegani, Erfan, Chakraborty, Trishna, Lal, Rohit, Dutta, Arindam, Song, Chengyu, Dong, Yue, Abu-Ghazaleh, Nael, Roy-Chowdhury, Amit K.
Vision-language models (VLMs) have improved significantly in multi-modal tasks, but their more complex architecture makes their safety alignment more challenging than the alignment of large language models (LLMs). In this paper, we reveal an unfair d
Externí odkaz:
http://arxiv.org/abs/2411.04291
Egocentric vision captures the scene from the point of view of the camera wearer while exocentric vision captures the overall scene context. Jointly modeling ego and exo views is crucial to developing next-generation AI agents. The community has rega
Externí odkaz:
http://arxiv.org/abs/2410.20621
Autor:
Ghosh, Udita, Raychaudhuri, Dripta S., Li, Jiachen, Karydis, Konstantinos, Roy-Chowdhury, Amit K.
Offline imitation learning enables learning a policy solely from a set of expert demonstrations, without any environment interaction. To alleviate the issue of distribution shift arising due to the small amount of expert data, recent works incorporat
Externí odkaz:
http://arxiv.org/abs/2410.03626
In this paper, we develop an embodied AI system for human-in-the-loop navigation with a wheeled mobile robot. We propose a direct yet effective method of monitoring the robot's current plan to detect changes in the environment that impact the intende
Externí odkaz:
http://arxiv.org/abs/2409.19459
Reinforcement Learning (RL) has enabled social robots to generate trajectories without human-designed rules or interventions, which makes it more effective than hard-coded systems for generalizing to complex real-world scenarios. However, social navi
Externí odkaz:
http://arxiv.org/abs/2407.17460
Autor:
Dutta, Arindam, Lal, Rohit, Garg, Yash, Ta, Calvin-Khang, Raychaudhuri, Dripta S., Cruz, Hannah Dela, Roy-Chowdhury, Amit K.
Existing algorithms for human body part segmentation have shown promising results on challenging datasets, primarily relying on end-to-end supervision. However, these algorithms exhibit severe performance drops in the face of domain shifts, leading t
Externí odkaz:
http://arxiv.org/abs/2407.03549
Autor:
Chakraborty, Trishna, Shayegani, Erfan, Cai, Zikui, Abu-Ghazaleh, Nael, Asif, M. Salman, Dong, Yue, Roy-Chowdhury, Amit K., Song, Chengyu
Recent studies reveal that integrating new modalities into Large Language Models (LLMs), such as Vision-Language Models (VLMs), creates a new attack surface that bypasses existing safety training techniques like Supervised Fine-tuning (SFT) and Reinf
Externí odkaz:
http://arxiv.org/abs/2406.02575
Autor:
Chang, Xiangyu, Ahmed, Sk Miraj, Krishnamurthy, Srikanth V., Guler, Basak, Swami, Ananthram, Oymak, Samet, Roy-Chowdhury, Amit K.
The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data. An overarching challenge to this date is client heterogeneity, which may arise not only from variations in
Externí odkaz:
http://arxiv.org/abs/2402.08769
Autor:
Chang, Xiangyu, Ahmed, Sk Miraj, Krishnamurthy, Srikanth V., Guler, Basak, Swami, Ananthram, Oymak, Samet, Roy-Chowdhury, Amit K.
Parameter-efficient tuning (PET) methods such as LoRA, Adapter, and Visual Prompt Tuning (VPT) have found success in enabling adaptation to new domains by tuning small modules within a transformer model. However, the number of domains encountered dur
Externí odkaz:
http://arxiv.org/abs/2401.04130