5 papers
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 +11
This work investigates a fundamental question: Do Video-Language Models (VidLMs) robustly account for video content, temporal sequence, and motion? Our investigation shows that, su…
FRAME: Pre-Training Video Feature Representations via Anticipation and Memory
Sethuraman TV, Savya Khosla, Vignesh Srinivasakumar +5
Dense video prediction tasks, such as object tracking and semantic segmentation, require video encoders that generate temporally consistent, spatially dense features for every fram…