73 citations · 126 across the 33 of their papers we have counts for
6 papers · 1 filter
Symbolic Graph Inference for Compound Scene Understanding
FNU Aryan, Simon Stepputtis, Sarthak Bhagat +4
Scene understanding is a fundamental capability needed in many domains, ranging from question-answering to robotics. Unlike recent end-to-end approaches that must explicitly learn…
Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation
Zifu Wan, Pingping Zhang, Yuhao Wang +4
Multi-modal semantic segmentation significantly enhances AI agents' perception and scene understanding, especially under adverse conditions like low-light or overexposed environmen…
HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation
Ce Zhang, Simon Stepputtis, Joseph Campbell +2
Being able to understand visual scenes is a precursor for many downstream tasks, including autonomous driving, robotics, and other vision-based approaches. A common approach enabli…
Enhancing Vision-Language Few-Shot Adaptation with Negative Learning
Ce Zhang, Simon Stepputtis, Katia Sycara +1
Large-scale pre-trained Vision-Language Models (VLMs) have exhibited impressive zero-shot performance and transferability, allowing them to adapt to downstream tasks in a data-effi…
Knowledge-Guided Short-Context Action Anticipation in Human-Centric Videos
Sarthak Bhagat, Simon Stepputtis, Joseph Campbell +1
This work focuses on anticipating long-term human actions, particularly using short video segments, which can speed up editing workflows through improved suggestions while fosterin…
Sample-Efficient Learning of Novel Visual Concepts
Sarthak Bhagat, Simon Stepputtis, Joseph Campbell +1
Despite the advances made in visual object recognition, state-of-the-art deep learning models struggle to effectively recognize novel objects in a few-shot setting where only a lim…