Search Results for author:
Found 4 papers, 2 papers with code
VideoAgent: Long-form Video Understanding with Large Language Model as Agent
Long-form video understanding represents a significant challenge within computer vision, demanding a model capable of reasoning over long multi-modal sequences.
Ranked #1 on
Zero-Shot Video Question Answer
on NExT-QA
Open World Object Detection in the Era of Foundation Models
We exploit the inherent connection between classes in application-driven datasets and introduce a novel method, Foundation Object detection Model for the Open world, or FOMO, which identifies unknown objects based on their shared attributes with the base known objects.
LOVM: Language-Only Vision Model Selection
As the number of open-source VLM variants increases, there is a need for an efficient model selection strategy that does not require access to a curated evaluation dataset.
PROB: Probabilistic Objectness for Open World Object Detection
The resulting Probabilistic Objectness transformer-based open-world detector, PROB, integrates our framework into traditional object detection models, adapting them for the open-world setting.
