Search Results for author: Shizhen Zhao

Found 7 papers, 6 papers with code

Granularity Matters in Long-Tail Learning

no code implementations21 Oct 2024 Shizhen Zhao, Xin Wen, Jiahui Liu, Chuofan Ma, Chunfeng Yuan, Xiaojuan Qi

To prevent the overwhelming presence of auxiliary classes from disrupting training, we introduce a neighbor-silencing loss that encourages the model to focus on class discrimination within the target dataset.

Long-tail Learning Representation Learning

Can OOD Object Detectors Learn from Foundation Models?

1 code implementation8 Sep 2024 Jiahui Liu, Xin Wen, Shizhen Zhao, Yingxian Chen, Xiaojuan Qi

Out-of-distribution (OOD) object detection is a challenging task due to the absence of open-set OOD data.

Object object-detection +2

Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection

1 code implementation11 Oct 2022 Shizhen Zhao, Xiaojuan Qi

Most existing 3D point cloud object detection approaches heavily rely on large amounts of labeled training data.

Object object-detection +2

Weakly Supervised Text-Based Person Re-Identification

1 code implementation ICCV 2021 Shizhen Zhao, Changxin Gao, Yuanjie Shao, Wei-Shi Zheng, Nong Sang

Specifically, to alleviate the intra-class variations, a clustering method is utilized to generate pseudo labels for both visual and textual instances.

Clustering Person Re-Identification +1

Devil's in the Details: Aligning Visual Clues for Conditional Embedding in Person Re-Identification

1 code implementation11 Sep 2020 Fufu Yu, Xinyang Jiang, Yifei Gong, Shizhen Zhao, Xiaowei Guo, Wei-Shi Zheng, Feng Zheng, Xing Sun

Secondly, the Conditional Feature Embedding requires the overall feature of a query image to be dynamically adjusted based on the gallery image it matches, while most of the existing methods ignore the reference images.

Person Re-Identification

GTNet: Generative Transfer Network for Zero-Shot Object Detection

1 code implementation19 Jan 2020 Shizhen Zhao, Changxin Gao, Yuanjie Shao, Lerenhan Li, Changqian Yu, Zhong Ji, Nong Sang

FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively.

Generative Adversarial Network Object +3

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