32 citations · 33 across the 6 of their papers we have counts for
6 papers
LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-training
Silky Singh, Shripad Deshmukh, Mausoom Sarkar +1
Learning object segmentation in image and video datasets without human supervision is a challenging problem. Humans easily identify moving salient objects in videos using the gesta…
Counterfactual Explanation Policies in RL
Shripad V. Deshmukh, Srivatsan R, Supriti Vijay +2
As Reinforcement Learning (RL) agents are increasingly employed in diverse decision-making problems using reward preferences, it becomes important to ensure that policies learned b…
FODVid: Flow-guided Object Discovery in Videos
Silky Singh, Shripad Deshmukh, Mausoom Sarkar +3
Segmentation of objects in a video is challenging due to the nuances such as motion blurring, parallax, occlusions, changes in illumination, etc. Instead of addressing these nuance…
Explaining RL Decisions with Trajectories
Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy +4
Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems. In the literature, the explanation is often provided by…
One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text
Abhinav Java, Shripad Deshmukh, Milan Aggarwal +3
Active consumption of digital documents has yielded scope for research in various applications, including search. Traditionally, searching within a document has been cast as a text…
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
Nikaash Puri, Sukriti Verma, Piyush Gupta +4
As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by hi…