Search Results for author: Qi Fan

Found 5 papers, 4 papers with code

Few-Shot Video Object Detection

1 code implementation30 Apr 2021 Qi Fan, Chi-Keung Tang, Yu-Wing Tai

We introduce Few-Shot Video Object Detection (FSVOD) with three important contributions: 1) a large-scale video dataset FSVOD-500 comprising of 500 classes with class-balanced videos in each category for few-shot learning; 2) a novel Tube Proposal Network (TPN) to generate high-quality video tube proposals to aggregate feature representation for the target video object; 3) a strategically improved Temporal Matching Network (TMN+) to match representative query tube features and supports with better discriminative ability.

Few-Shot Video Object Detection Video Object Detection

Group Collaborative Learning for Co-Salient Object Detection

1 code implementation CVPR 2021 Qi Fan, Deng-Ping Fan, Huazhu Fu, Chi Keung Tang, Ling Shao, Yu-Wing Tai

We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module; 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module conditioning the inconsistent consensus.

Co-Salient Object Detection Salient Object Detection

Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector

3 code implementations CVPR 2020 Qi Fan, Wei Zhuo, Chi-Keung Tang, Yu-Wing Tai

To train our network, we contribute a new dataset that contains 1000 categories of various objects with high-quality annotations.

Ranked #7 on Few-Shot Object Detection on MS-COCO (10-shot) (using extra training data)

Few-Shot Object Detection

Real-Time Influence Maximization on Dynamic Social Streams

no code implementations6 Feb 2017 Yanhao Wang, Qi Fan, Yuchen Li, Kian-Lee Tan

Influence maximization (IM), which selects a set of $k$ users (called seeds) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications such as viral marketing and network monitoring.

Social and Information Networks Data Structures and Algorithms

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