96 citations · 99 across the 7 of their papers we have counts for
7 papers · 1 filter
Adapting MLLMs for Nuanced Video Retrieval
Piyush Bagad, Andrew Zisserman
Our objective is to build an embedding model that captures the nuanced relationship between a search query and candidate videos. We cover three aspects of nuanced retrieval: (i) te…
Chirality in Action: Time-Aware Video Representation Learning by Latent Straightening
Piyush Bagad, Andrew Zisserman
Our objective is to develop compact video representations that are sensitive to visual change over time. To measure such time-sensitivity, we introduce a new task: chiral action re…
SEVERE++: Evaluating Benchmark Sensitivity in Generalization of Video Representation Learning
Fida Mohammad Thoker, Letian Jiang, Chen Zhao +4
Continued advances in self-supervised learning have led to significant progress in video representation learning, offering a scalable alternative to supervised approaches by removi…
The Sound of Water: Inferring Physical Properties from Pouring Liquids
Piyush Bagad, Makarand Tapaswi, Cees G. M. Snoek +1
We study the connection between audio-visual observations and the underlying physics of a mundane yet intriguing everyday activity: pouring liquids. Given only the sound of liquid…
NurtureNet: A Multi-task Video-based Approach for Newborn Anthropometry
Yash Khandelwal, Mayur Arvind, Sriram Kumar +17
Malnutrition among newborns is a top public health concern in developing countries. Identification and subsequent growth monitoring are key to successful interventions. However, th…
Test of Time: Instilling Video-Language Models with a Sense of Time
Piyush Bagad, Makarand Tapaswi, Cees G. M. Snoek
Modelling and understanding time remains a challenge in contemporary video understanding models. With language emerging as a key driver towards powerful generalization, it is imper…
How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?
Fida Mohammad Thoker, Hazel Doughty, Piyush Bagad +1
Despite the recent success of video self-supervised learning models, there is much still to be understood about their generalization capability. In this paper, we investigate how s…