1 citations · 2 across the 6 of their papers we have counts for
6 papers
Solving Instance Detection from an Open-World Perspective
Qianqian Shen, Yunhan Zhao, Nahyun Kwon +3
Instance detection (InsDet) aims to localize specific object instances within a novel scene imagery based on given visual references. Technically, it requires proposal detection to…
Roadside Monocular 3D Detection Prompted by 2D Detection
Yechi Ma, Yanan Li, Wei Hua +1
Roadside monocular 3D detection requires detecting objects of predefined classes in an RGB frame and predicting their 3D attributes, such as bird's-eye-view (BEV) locations. It has…
The Neglected Tails in Vision-Language Models
Shubham Parashar, Zhiqiu Lin, Tian Liu +5
Vision-language models (VLMs) excel in zero-shot recognition but their performance varies greatly across different visual concepts. For example, although CLIP achieves impressive a…
Long-Tailed 3D Detection via Multi-Modal Fusion
Yechi Ma, Neehar Peri, Achal Dave +3
Contemporary autonomous vehicle (AV) benchmarks have significantly advanced multimodal (LiDAR+RGB) 3D detection. However, despite the naturally long-tailed distribution of object c…
A High-Resolution Dataset for Instance Detection with Multi-View Instance Capture
Qianqian Shen, Yunhan Zhao, Nahyun Kwon +3
Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene.…
Prompting Scientific Names for Zero-Shot Species Recognition
Shubham Parashar, Zhiqiu Lin, Yanan Li +1
Trained on web-scale image-text pairs, Vision-Language Models (VLMs) such as CLIP can recognize images of common objects in a zero-shot fashion. However, it is underexplored how to…