Publications (20)
Learning Person Re-identification Models from Videos with Weak Supervision
Xueping Wang, Sujoy Paul, Dripta S. Raychaudhuri +3
Most person re-identification methods, being supervised techniques, suffer from the burden of massive annotation requirement. Unsupervised methods overcome this need for labeled da…
Reducing Oracle Feedback with Vision-Language Embeddings for Preference-Based RL
Udita Ghosh, Dripta S. Raychaudhuri, Jiachen Li +2
Preference-based reinforcement learning can learn effective reward functions from comparisons, but its scalability is constrained by the high cost of oracle feedback. Lightweight v…
Robust Offline Imitation Learning from Diverse Auxiliary Data
Udita Ghosh, Dripta S. Raychaudhuri, Jiachen Li +2
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 a…
CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions
Sk Miraj Ahmed, Fahim Faisal Niloy, Xiangyu Chang +3
Adapting to dynamic data distributions is a practical yet challenging task. One effective strategy is to use a model ensemble, which leverages the diverse expertise of different mo…
Open-World Dynamic Prompt and Continual Visual Representation Learning
Youngeun Kim, Jun Fang, Qin Zhang +7
The open world is inherently dynamic, characterized by ever-evolving concepts and distributions. Continual learning (CL) in this dynamic open-world environment presents a significa…
Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning
Udita Ghosh, Dripta S. Raychaudhuri, Jiachen Li +2
Preference-based reinforcement learning (RL) offers a promising approach for aligning policies with human intent but is often constrained by the high cost of human feedback. In thi…