159 citations · 540 across the 15 of their papers we have counts for
15 papers
TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement
Carl Doersch, Yi Yang, Mel Vecerik +5
We present a novel model for Tracking Any Point (TAP) that effectively tracks any queried point on any physical surface throughout a video sequence. Our approach employs two stages…
Perception Test: A Diagnostic Benchmark for Multimodal Video Models
Viorica Pătrăucean, Lucas Smaira, Ankush Gupta +21
We propose a novel multimodal video benchmark - the Perception Test - to evaluate the perception and reasoning skills of pre-trained multimodal models (e.g. Flamingo, SeViLA, or GP…
SuS-X: Training-Free Name-Only Transfer of Vision-Language Models
Vishaal Udandarao, Ankush Gupta, Samuel Albanie
Contrastive Language-Image Pre-training (CLIP) has emerged as a simple yet effective way to train large-scale vision-language models. CLIP demonstrates impressive zero-shot classif…
TAP-Vid: A Benchmark for Tracking Any Point in a Video
Carl Doersch, Ankush Gupta, Larisa Markeeva +6
Generic motion understanding from video involves not only tracking objects, but also perceiving how their surfaces deform and move. This information is useful to make inferences ab…
Is an Object-Centric Video Representation Beneficial for Transfer?
Chuhan Zhang, Ankush Gupta, Andrew Zisserman
The objective of this work is to learn an object-centric video representation, with the aim of improving transferability to novel tasks, i.e., tasks different from the pre-training…
Temporal Query Networks for Fine-grained Video Understanding
Chuhan Zhang, Ankush Gupta, Andrew Zisserman
Our objective in this work is fine-grained classification of actions in untrimmed videos, where the actions may be temporally extended or may span only a few frames of the video. W…