3 citations · 5 across the 16 of their papers we have counts for
10 papers · 1 filter
Derivation Depth Exposes Reasoning Failures in Tool-Augmented Music Analysis
Rupali Rajesh, Sethuraman T, Savya Khosla +4
Audio-language models remain unreliable at extracting basic musical properties from audio, motivating pipelines that place a music information retrieval (MIR) tool between the audi…
Render to Reason: Novel-View Semantic Prediction Improves Spatial Understanding in VLMs
Yuqun Wu, Yao Xiao, Chuhang Zou +2
Recent works augment Vision-Language Models with geometry features from pretrained 3D models, expecting that the geometric signal will boost spatial reasoning. However, we find tha…
SAM-V: Geometry-Aware Segment Anything for Multi-View Instance Segmentation
Jiangshan Gong, Yuqun Wu, Qiqian Fu +4
Consistent multi-view object segmentation is critical for 3D perception and robotics, yet remains challenging under severe viewpoint and occlusion changes. Existing methods typical…
ViTAL-X: Video-Text Alignment with Cross-Modal Temporal Edits
Sethuraman T, Savya Khosla, Onkar Kishor Susladkar +6
Video-text models adapted from image-text architectures (e.g., CLIP) frequently exhibit temporal blindness, the inability to perceive fundamental cues like order, direction, and mo…
Fine-Tuning VLAs with Self-Demonstrated Generative Control for Multi-Task Manipulation
Prachi Garg, Steve Xing, Prahit Yaugand +2
State-of-the-art vision-language-action (VLA) models such as exhibit strong semantic understanding, instruction following and task behavior. However, when deployed on new…
ReToken: Improving Long-Context VLMs with Visual Retrieval Token
Yao Xiao, Reuben Tan, Zhen Zhu +3
Long visual contexts challenge vision-language models: performance degrades as the number of distractors grows, and processing all tokens at once can exceed GPU memory limits. We p…
Decomposing Queries into Tool Calls for Long-Video Keyframe Retrieval
Michal Shlapentokh-Rothman, Prachi Garg, Yu-Xiong Wang +1
Keyframe selection is a direct way to provide verifiable visual evidence for long-video question answering (QA). Queries differ in what they require, and finding the right frames d…
T-REN: Learning Text-Aligned Region Tokens Improves Dense Vision-Language Alignment and Scalability
Savya Khosla, Sethuraman T, Aryan Chadha +2
Despite recent progress, vision-language encoders struggle with two core limitations: (1) weak alignment between language and dense vision features, which hurts tasks like open-voc…
Why Fine-Tuning Encourages Hallucinations and How to Fix It
Guy Kaplan, Zorik Gekhman, Zhen Zhu +5
Large language models are prone to hallucinating factually incorrect statements. A key source of these errors is exposure to new factual information through supervised fine-tuning…
Stress Tests REVEAL Fragile Temporal and Visual Grounding in Video-Language Models
Sethuraman T, Savya Khosla, Aditi Tiwari +12
Video-Language Models (VidLMs) achieve strong benchmark scores, yet these scores often hide whether models use the video at all. We show that VidLM failures follow two pathways: so…