Search Results for author: Chi Su

Found 20 papers, 10 papers with code

Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision

no code implementations ECCV 2020 Xinzhe Han, Shuhui Wang, Chi Su, Weigang Zhang, Qingming Huang, Qi Tian

In this paper, we rethink implicit reasoning process in VQA, and propose a new formulation which maximizes the log-likelihood of joint distribution for the observed question and predicted answer.

Question Answering Visual Question Answering +1

XPose: eXplainable Human Pose Estimation

no code implementations19 Mar 2024 Luyu Qiu, Jianing Li, Lei Wen, Chi Su, Fei Hao, Chen Jason Zhang, Lei Chen

In this paper, we propose XPose, a novel framework that incorporates Explainable AI (XAI) principles into pose estimation.

Computational Efficiency Data Augmentation +1

General Greedy De-bias Learning

1 code implementation20 Dec 2021 Xinzhe Han, Shuhui Wang, Chi Su, Qingming Huang, Qi Tian

Existing de-bias learning frameworks try to capture specific dataset bias by annotations but they fail to handle complicated OOD scenarios.

Image Classification Question Answering +1

Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis

1 code implementation23 Nov 2021 Zhaobo Qi, Shuhui Wang, Chi Su, Li Su, Weigang Zhang, Qingming Huang

Based on TDC, we propose the temporal dynamic concept modeling network (TDCMN) to learn an accurate and complete concept representation for efficient untrimmed video analysis.

Image Categorization

Greedy Gradient Ensemble for Robust Visual Question Answering

1 code implementation ICCV 2021 Xinzhe Han, Shuhui Wang, Chi Su, Qingming Huang, Qi Tian

Language bias is a critical issue in Visual Question Answering (VQA), where models often exploit dataset biases for the final decision without considering the image information.

Question Answering Visual Question Answering

Neural Architecture Search for Joint Human Parsing and Pose Estimation

1 code implementation ICCV 2021 Dan Zeng, Yuhang Huang, Qian Bao, Junjie Zhang, Chi Su, Wu Liu

With the spirit of NAS, we propose to search for an efficient network architecture (NPPNet) to tackle two tasks at the same time.

Human Parsing Neural Architecture Search +1

Label Decoupling Framework for Salient Object Detection

1 code implementation CVPR 2020 Jun Wei, Shuhui Wang, Zhe Wu, Chi Su, Qingming Huang, Qi Tian

Though remarkable progress has been achieved, we observe that the closer the pixel is to the edge, the more difficult it is to be predicted, because edge pixels have a very imbalance distribution.

Object object-detection +3

Correlating Edge, Pose with Parsing

1 code implementation CVPR 2020 Ziwei Zhang, Chi Su, Liang Zheng, Xiaodong Xie

Compared with the existing practice of feature concatenation, we find that uncovering the correlation among the three factors is a superior way of leveraging the pivotal contextual cues provided by edges and poses.

Feature Correlation Human Parsing

Gradually Vanishing Bridge for Adversarial Domain Adaptation

2 code implementations CVPR 2020 Shuhao Cui, Shuhui Wang, Junbao Zhuo, Chi Su, Qingming Huang, Qi Tian

On the discriminator, GVB contributes to enhance the discriminating ability, and balance the adversarial training process.

Unsupervised Domain Adaptation

SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images

1 code implementation2 Jan 2019 Caijing Miao, Lingxi Xie, Fang Wan, Chi Su, Hongye Liu, Jianbin Jiao, Qixiang Ye

In particular, the advantage of CHR is more significant in the scenarios with fewer positive training samples, which demonstrates its potential application in real-world security inspection.

Object Localization

Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning

1 code implementation CVPR 2019 Chen Wei, Lingxi Xie, Xutong Ren, Yingda Xia, Chi Su, Jiaying Liu, Qi Tian, Alan L. Yuille

We consider spatial contexts, for which we solve so-called jigsaw puzzles, i. e., each image is cut into grids and then disordered, and the goal is to recover the correct configuration.

General Classification Image Classification +4

Snapshot Distillation: Teacher-Student Optimization in One Generation

no code implementations CVPR 2019 Chenglin Yang, Lingxi Xie, Chi Su, Alan L. Yuille

Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting.

Image Classification object-detection +2

Generalized Coarse-to-Fine Visual Recognition with Progressive Training

no code implementations29 Nov 2018 Xutong Ren, Lingxi Xie, Chen Wei, Siyuan Qiao, Chi Su, Jiaying Liu, Qi Tian, Elliot K. Fishman, Alan L. Yuille

Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn.

Image Classification Object Localization +1

Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation

no code implementations CVPR 2018 Kai Li, Junliang Xing, Chi Su, Weiming Hu, Yundong Zhang, Stephen Maybank

First, a novel cost-sensitive multi-task loss function is designed to learn transferable aging features by training on the source population.

Age Estimation

Pose-driven Deep Convolutional Model for Person Re-identification

no code implementations ICCV 2017 Chi Su, Jianing Li, Shiliang Zhang, Junliang Xing, Wen Gao, Qi Tian

Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts.

Person Re-Identification

Deep Attributes Driven Multi-Camera Person Re-identification

no code implementations11 May 2016 Chi Su, Shiliang Zhang, Junliang Xing, Wen Gao, Qi Tian

And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data.

Attribute Metric Learning +1

Multi-Task Learning With Low Rank Attribute Embedding for Person Re-Identification

no code implementations ICCV 2015 Chi Su, Fan Yang, Shiliang Zhang, Qi Tian, Larry S. Davis, Wen Gao

Since attributes are generally correlated, we introduce a low rank attribute embedding into the MTL formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered to better describe people.

Attribute Multi-Task Learning +1

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