135 citations · 307 across the 31 of their papers we have counts for
7 papers · 1 filter
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization
Yue Zhang, Liqiang Jing, Vibhav Gogate
We introduce a new task called Defeasible Visual Entailment (DVE), where the goal is to allow the modification of the entailment relationship between an image premise and a text hy…
Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation
Rohith Peddi, Saurabh, Ayush Abhay Shrivastava +2
Spatio-Temporal Scene Graphs (STSGs) provide a concise and expressive representation of dynamic scenes by modeling objects and their evolving relationships over time. However, real…
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification
Shivvrat Arya, Yu Xiang, Vibhav Gogate
We present a unified framework called deep dependency networks (DDNs) that combines dependency networks and deep learning architectures for multi-label classification, with a parti…
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models
Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
We propose a self-supervised learning approach for solving the following constrained optimization task in log-linear models or Markov networks. Let and be two log-linear mo…
Grasping Trajectory Optimization with Point Clouds
Yu Xiang, Sai Haneesh Allu, Rohith Peddi +2
We introduce a new trajectory optimization method for robotic grasping based on a point-cloud representation of robots and task spaces. In our method, robots are represented by 3D…
Towards Scene Graph Anticipation
Rohith Peddi, Saksham Singh, Saurabh +2
Spatio-temporal scene graphs represent interactions in a video by decomposing scenes into individual objects and their pair-wise temporal relationships. Long-term anticipation of t…