6 papers
FSP-DETR: Few-Shot Prototypical Parasitic Ova Detection
Shubham Trehan, Udhav Ramachandran, Akash Rao +2
Object detection in biomedical settings is fundamentally constrained by the scarcity of labeled data and the frequent emergence of novel or rare categories. We present FSP-DETR, a…
Self-supervised Multi-actor Social Activity Understanding in Streaming Videos
Shubham Trehan, Sathyanarayanan N. Aakur
This work addresses the problem of Social Activity Recognition (SAR), a critical component in real-world tasks like surveillance and assistive robotics. Unlike traditional event un…
ALGO: Object-Grounded Visual Commonsense Reasoning for Open-World Egocentric Action Recognition
Sanjoy Kundu, Shubham Trehan, Sathyanarayanan N. Aakur
Learning to infer labels in an open world, i.e., in an environment where the target "labels" are unknown, is an important characteristic for achieving autonomy. Foundation models p…
ProtoKD: Learning from Extremely Scarce Data for Parasite Ova Recognition
Shubham Trehan, Udhav Ramachandran, Ruth Scimeca +1
Developing reliable computational frameworks for early parasite detection, particularly at the ova (or egg) stage is crucial for advancing healthcare and effectively managing poten…
Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoning
Sanjoy Kundu, Shubham Trehan, Sathyanarayanan N. Aakur
Learning to infer labels in an open world, i.e., in an environment where the target ``labels'' are unknown, is an important characteristic for achieving autonomy. Foundation models…
Towards Active Vision for Action Localization with Reactive Control and Predictive Learning
Shubham Trehan, Sathyanarayanan N. Aakur
Visual event perception tasks such as action localization have primarily focused on supervised learning settings under a static observer, i.e., the camera is static and cannot be c…