5 papers · 1 filter
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…
Gen2Balance: Generative Balancing for Long-Tailed Video Action Recognition
Prajwal Gatti, Simon Jenni, Fabian Caba Heilbron +1
We address the problem of training on long-tailed data for video action recognition. We propose to augment the training set using a text-to-video generative model, conditioned on d…
The Indra Representation Hypothesis for Multimodal Alignment
Jianglin Lu, Hailing Wang, Kuo Yang +3
Recent studies have uncovered an interesting phenomenon: unimodal foundation models tend to learn convergent representations, regardless of differences in architecture, training ob…
Seeing Through Words: Controlling Visual Retrieval Quality with Language Models
Jianglin Lu, Simon Jenni, Kushal Kafle +3
Text-to-image retrieval is a fundamental task in vision-language learning, yet in real-world scenarios it is often challenged by short and underspecified user queries. Such queries…
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…