Search Results for author: Sheng Tang

Found 14 papers, 7 papers with code

Topology-Preserving Adversarial Training

no code implementations29 Nov 2023 Xiaoyue Mi, Fan Tang, Yepeng Weng, Danding Wang, Juan Cao, Sheng Tang, Peng Li, Yang Liu

Despite the effectiveness in improving the robustness of neural networks, adversarial training has suffered from the natural accuracy degradation problem, i. e., accuracy on natural samples has reduced significantly.

Dance Your Latents: Consistent Dance Generation through Spatial-temporal Subspace Attention Guided by Motion Flow

no code implementations20 Oct 2023 Haipeng Fang, Zhihao Sun, Ziyao Huang, Fan Tang, Juan Cao, Sheng Tang

The advancement of generative AI has extended to the realm of Human Dance Generation, demonstrating superior generative capacities.

Progressive Open Space Expansion for Open-Set Model Attribution

1 code implementation CVPR 2023 Tianyun Yang, Danding Wang, Fan Tang, Xinying Zhao, Juan Cao, Sheng Tang

In this study, we focus on a challenging task, namely Open-Set Model Attribution (OSMA), to simultaneously attribute images to known models and identify those from unknown ones.

Attribute Open Set Learning

Learning to Disentangle GAN Fingerprint for Fake Image Attribution

no code implementations16 Jun 2021 Tianyun Yang, Juan Cao, Qiang Sheng, Lei LI, Jiaqi Ji, Xirong Li, Sheng Tang

Adopting a multi-task framework, we propose a GAN Fingerprint Disentangling Network (GFD-Net) to simultaneously disentangle the fingerprint from GAN-generated images and produce a content-irrelevant representation for fake image attribution.

Fake Image Attribution Open-Ended Question Answering

The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation

1 code implementation ECCV 2020 Tao Wang, Yu Li, Bingyi Kang, Junnan Li, Junhao Liew, Sheng Tang, Steven Hoi, Jiashi Feng

Specifically, we systematically investigate performance drop of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the recent long-tail LVIS dataset, and unveil that a major cause is the inaccurate classification of object proposals.

General Classification Instance Segmentation +4

Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax

2 code implementations CVPR 2020 Yu Li, Tao Wang, Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, Jiashi Feng

Solving long-tail large vocabulary object detection with deep learning based models is a challenging and demanding task, which is however under-explored. In this work, we provide the first systematic analysis on the underperformance of state-of-the-art models in front of long-tail distribution.

Image Classification Instance Segmentation +5

Asymmetric GAN for Unpaired Image-to-image Translation

no code implementations25 Dec 2019 Yu Li, Sheng Tang, Rui Zhang, Yongdong Zhang, Jintao Li, Shuicheng Yan

While in situations where two domains are asymmetric in complexity, i. e., the amount of information between two domains is different, these approaches pose problems of poor generation quality, mapping ambiguity, and model sensitivity.

Image-to-Image Translation Translation

Classification Calibration for Long-tail Instance Segmentation

1 code implementation29 Oct 2019 Tao Wang, Yu Li, Bingyi Kang, Junnan Li, Jun Hao Liew, Sheng Tang, Steven Hoi, Jiashi Feng

In this report, we investigate the performance drop phenomenon of state-of-the-art two-stage instance segmentation models when processing extreme long-tail training data based on the LVIS [5] dataset, and find a major cause is the inaccurate classification of object proposals.

Classification General Classification +3

Consensus Feature Network for Scene Parsing

no code implementations29 Jul 2019 Tianyi Wu, Sheng Tang, Rui Zhang, Guodong Guo, Yongdong Zhang

However, classification networks are dominated by the discriminative portion, so directly applying classification networks to scene parsing will result in inconsistent parsing predictions within one instance and among instances of the same category.

General Classification Scene Parsing

Tree-structured Kronecker Convolutional Network for Semantic Segmentation

no code implementations12 Dec 2018 Tianyi Wu, Sheng Tang, Rui Zhang, Juan Cao, Jintao Li

Therefore, it can capture partial information and enlarge the receptive field of filters simultaneously without introducing extra parameters.

Semantic Segmentation

CGNet: A Light-weight Context Guided Network for Semantic Segmentation

4 code implementations20 Nov 2018 Tianyi Wu, Sheng Tang, Rui Zhang, Yongdong Zhang

To tackle this problem, we propose a novel Context Guided Network (CGNet), which is a light-weight and efficient network for semantic segmentation.

Segmentation Semantic Segmentation

Scale-Adaptive Convolutions for Scene Parsing

no code implementations ICCV 2017 Rui Zhang, Sheng Tang, Yongdong Zhang, Jintao Li, Shuicheng Yan

Through adding a new scale regression layer, we can dynamically infer the position-adaptive scale coefficients which are adopted to resize the convolutional patches.

regression Scene Parsing

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