6 papers
Visuals Lie, Consistency Speaks: Disentangling Spatial Attention from Reliability in Vision-Language Models
Logan Mann, Yi Xia, Ajit Saravanan +6
Multimodal Foundation Models are increasingly used as reasoning agents, making reliability, knowing when a model may hallucinate, critical. A common intuition, which we call the At…
Where Reliability Lives in Vision-Language Models: A Mechanistic Study of Attention, Hidden States, and Causal Circuits
Logan Mann, Ajit Saravanan, Ishan Dave +4
A pervasive intuition holds that vision-language models (VLMs) are most trustworthy when their attention maps look sharp: concentrated attention on the queried region should imply…
Privacy Beyond Pixels: Latent Anonymization for Privacy-Preserving Video Understanding
Joseph Fioresi, Ishan Rajendrakumar Dave, Mubarak Shah
We introduce a novel formulation of visual privacy preservation for video foundation models that operates entirely in the latent space. While spatio-temporal features learned by fo…
From Play to Replay: Composed Video Retrieval for Temporally Fine-Grained Videos
Animesh Gupta, Jay Parmar, Ishan Rajendrakumar Dave +1
Composed Video Retrieval (CoVR) retrieves a target video given a query video and a modification text describing the intended change. Existing CoVR benchmarks emphasize appearance s…
GT-Loc: Unifying When and Where in Images Through a Joint Embedding Space
David G. Shatwell, Ishan Rajendrakumar Dave, Sirnam Swetha +1
Timestamp prediction aims to determine when an image was captured using only visual information, supporting applications such as metadata correction, retrieval, and digital forensi…
ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition
Joseph Fioresi, Ishan Rajendrakumar Dave, Mubarak Shah
Bias in machine learning models can lead to unfair decision making, and while it has been well-studied in the image and text domains, it remains underexplored in action recognition…