5 citations · 7 across the 5 of their papers we have counts for
5 papers
Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models
Martin Q. Ma, Willis Guo, Aditya Agrawal +4
Large vision-language models (VLMs) have advanced multimodal tasks such as video question answering (QA). However, VLMs face the challenge of selecting frames effectively and effic…
Act2See: Emergent Active Visual Perception for Video Reasoning
Martin Q. Ma, Yuxiao Qu, Aditya Agrawal +4
Vision-Language Models (VLMs) typically rely on static initial frames for video reasoning, restricting their ability to incorporate essential dynamic information as the reasoning p…
CoLoTa: A Dataset for Entity-based Commonsense Reasoning over Long-Tail Knowledge
Armin Toroghi, Willis Guo, Scott Sanner
The rise of Large Language Models (LLMs) has redefined the AI landscape, particularly due to their ability to encode factual and commonsense knowledge, and their outstanding perfor…
CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge
Willis Guo, Armin Toroghi, Scott Sanner
Knowledge graph question answering (KGQA) is a well-established field that seeks to provide factual answers to natural language (NL) questions by leveraging knowledge graphs (KGs).…
Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering
Armin Toroghi, Willis Guo, Mohammad Mahdi Abdollah Pour +1
Knowledge Graph Question Answering (KGQA) methods seek to answer Natural Language questions using the relational information stored in Knowledge Graphs (KGs). With the recent advan…