46 citations · 133 across the 11 of their papers we have counts for
11 papers
Prompt-Free Universal Region Proposal Network
Qihong Tang, Changhan Liu, Shaofeng Zhang +3
Identifying potential objects is critical for object recognition and analysis across various computer vision applications. Existing methods typically localize potential objects by…
Stable Segment Anything Model
Qi Fan, Xin Tao, Lei Ke +6
The Segment Anything Model (SAM) achieves remarkable promptable segmentation given high-quality prompts which, however, often require good skills to specify. To make SAM robust to…
UniBoost: Unsupervised Unimodal Pre-training for Boosting Zero-shot Vision-Language Tasks
Yanan Sun, Zihan Zhong, Qi Fan +2
Large-scale joint training of multimodal models, e.g., CLIP, have demonstrated great performance in many vision-language tasks. However, image-text pairs for pre-training are restr…
Rethinking Local Perception in Lightweight Vision Transformer
Qihang Fan, Huaibo Huang, Jiyang Guan +1
Vision Transformers (ViTs) have been shown to be effective in various vision tasks. However, resizing them to a mobile-friendly size leads to significant performance degradation. T…
Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts
Qi Fan, Mattia Segu, Yu-Wing Tai +4
Improving model's generalizability against domain shifts is crucial, especially for safety-critical applications such as autonomous driving. Real-world domain styles can vary subst…
Self-Support Few-Shot Semantic Segmentation
Qi Fan, Wenjie Pei, Yu-Wing Tai +1
Existing few-shot segmentation methods have achieved great progress based on the support-query matching framework. But they still heavily suffer from the limited coverage of intra-…
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
Peng Zheng, Huazhu Fu, Deng-Ping Fan +5
In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in na…
Group Collaborative Learning for Co-Salient Object Detection
Qi Fan, Deng-Ping Fan, Huazhu Fu +3
We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations a…
Few-Shot Video Object Detection
Qi Fan, Chi-Keung Tang, Yu-Wing Tai
We introduce Few-Shot Video Object Detection (FSVOD) with three contributions to real-world visual learning challenge in our highly diverse and dynamic world: 1) a large-scale vide…
Commonality-Parsing Network across Shape and Appearance for Partially Supervised Instance Segmentation
Qi Fan, Lei Ke, Wenjie Pei +2
Partially supervised instance segmentation aims to perform learning on limited mask-annotated categories of data thus eliminating expensive and exhaustive mask annotation. The lear…
Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector
Qi Fan, Wei Zhuo, Chi-Keung Tang +1
Conventional methods for object detection typically require a substantial amount of training data and preparing such high-quality training data is very labor-intensive. In this pap…