Kecheng Zheng

Kecheng Zheng is currently a researcher at Ant Research working with Yujun Shen. My research focuses on computer vision and deep learning, particularly on multi-modal learning.

From Nov. 2018 to Jan. 2019, I was under the supervision of Wei Wei who is a research scientist in GOOGLE cloud.

From Sep. 2019 to May. 2020, I was an intern at JD AI Lab, working with Wu Liu.

From Jul. 2020 to Jan. 2021, I worked at Intelligent Multimedia Group (IMG) in MSRA as a research intern, under the supervision of Cuiling Lan.

From Mar. 2022 to Jul. 2022, I worked at Ant Research as a research intern working with Deli Zhao.

Email: zkechengzk@gmail.com / Google Scholar / Github

News

[10/2023] Looking for self-motivated interns in multi-modal learning. Please drop your CV to me if interested.

[03/2024] Two paper accepted by CVPR 2024~

[10/2023] Two paper accepted by NeurIPS 2023.

[08/2023] Two paper accepted by ICCV 2023.

[04/2023] Two paper accepted by ICML 2023.

[02/2023] Two paper accepted by CVPR 2023.

[09/2022] Two paper accepted by NeurIPS 2022.

[05/2022] One paper accepted by ICML 2022.

Research

I'm interested in computer vision, representation learning and multi-modal learning.

	CoDeF: Content Deformation Fields for Temporally Consistent Video Processing Hao Ouyang, Qiuyu Wang, Yuxi Xiao, Qingyan Bai, Juntao Zhang, Kecheng Zheng, Xiaowei Zhou, Qifeng Chen, Yujun Shen Arxiv, 2023* Arxiv / Project / Code
	Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase Qiuyu Wang, Zifan Shi, Kecheng Zheng, Yinghao Xu, Sida Peng, Yujun Shen NeurIPS, 2023* ArXiv / Code
	Cones 2: Customizable Image Synthesis with Multiple Subjects Zhiheng Liu, Yifei Zhang, Yujun Shen, Kecheng Zheng, Kai Zhu, Ruili Feng, Yu Liu, Deli Zhao, Jingren Zhou, Yang Cao NeurIPS, 2023 ArXiv / Project / Code
	Cones: Concept Neurons in Diffusion Models for Customized Generation. Zhiheng Liu, Ruili Feng, Kai Zhu, Yifei Zhang, Kecheng Zheng, Yu Liu, Deli Zhao, Jingren Zhou, Yang Cao ICML, 2023 Oral Presentation! ArXiv / Code
	RLEG: Vision-Language Representation Learning with Diffusion-based Embedding Generation. Liming Zhao, Kecheng Zheng, Yun Zheng, Deli Zhao, Jingren Zhou, ICML, 2023 ICML
	Self-Organizing Pathway Expansion for Non-Exemplar Class-Incremental Learning Kai Zhu, Kecheng Zheng, Ruili Feng, Deli Zhao, Yang Cao, Zheng-jun Zha ICCV, 2023 Arvix(coming soon) / Code(coming soon)
	Regularized Mask Tuning: Uncovering Hidden Knowledge in Pre-trained Vision-Language Models Kecheng Zheng, Wei Wu, Ruili Feng Kai Zhu, Jiawei Liu, Deli Zhao, Zheng-jun Zha, Wei Chen, Yujun Shen ICCV, 2023 Arvix / Project / Code
	Neural Dependencies Emerging from Learning Massive Categories Ruili Feng, Kecheng Zheng, Kai Zhu, Yujun Shen, Jian Zhao, Yukun Huang, Deli Zhao, Jingren Zhou, Michael Jordan, Zheng-Jun Zha CVPR, 2023 arvix
	Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection Fan Lu, Kai Zhu, Wei Zhai, Kecheng Zheng, Yang Cao CVPR, 2023 arvix / Code
	Rank Diminishing in Deep Neural Networks Ruili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael I. Jordan, Zheng-jun Zha NeurIPS, 2022 NeurIPS / Code
	Uncertainty-Aware Hierarchical Refinement for Incremental Implicitly-Refined Classification Jian Yang, Kai Zhu, Kecheng Zheng, Yang Cao NeurIPS, 2022 NeurIPS
	Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification Zizheng Yang, Xin Jin, Kecheng Zheng, Feng Zhao CVPR, 2022 ArXiv / Code / bibtex We design an Unsupervised Pre-training framework for ReID based on the contrastive learning (CL) pipeline, dubbed UP-ReID.
	Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization Xin Jin, Tianyu He, Kecheng Zheng, Zhiheng Ying, Xu Shen, Zhen Huang , Ruoyu Feng , Jianqiang Huang , Xian-Sheng Hua , Zhibo Chen CVPR, 2022 ArXiv / Code / bibtex We focus on handling well the CC-ReID problem under a more challenging setting, i.e., just from a single image, which enables high-efficiency and latency-free pedestrian identify for real-time surveillance applications.
	Calibrated Feature Decomposition for Generalizable Person Re-Identification Kecheng Zheng, Jiawei Liu, Wei Wu, Liang Li, Zheng-jun Zha Arxiv, 2022 ArXiv / Code / bibtex We propose a simple yet effective Calibrated Feature Decomposition (CFD) module that focuses on improving the generalization capacity for person re-identification through a more judicious feature decomposition and reinforcement strategy.
	Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification Jiawei Liu, Zhipeng Huang, Kecheng Zheng, Liang Li, Zheng-jun Zha AAAI, 2022 Arxiv We propose a novel Debiased Batch Normalization via Gaussian Process approach (GDNorm) for generalizable person re-identification, which models the feature statistic estimation from BN layers as a dynamically self-refining Gaussian process to alleviate the bias to unseen domain for improving the generalization.
	Modality-Adaptive Mixup and Invariant Decomposition for RGB-Infrared Person Re-Identification Zhipeng Huang, Jiawei Liu, Kecheng Zheng, Liang Li, Zheng-jun Zha AAAI, 2022 arxiv We propose a novel modality-adaptive mixup and invariant decomposition (MID) approach for RGB-infrared person re-identification towards learning modality-invariant and discriminative representations.
	Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification Kecheng Zheng, Cuiling Lan, Jiawei Liu, Wenjun Zeng, Zhizheng Zhang, Zheng-jun Zha ACM MM, 2021 ArXiv / bibtex We propose a network named Pose-Guided Feature Learning with Knowledge Distillation (PGFL-KD), where the pose information is exploited to regularize the learning of semantics aligned features but is discarded in testing.
	Group-aware Label Transfer for Domain Adaptive Person Re-identification Kecheng Zheng, Wu Liu, Lingxiao He, Jiebo Luo, Tao Mei, Zheng-jun Zha CVPR, 2021 ArXiv / Code / bibtex We propose a Group-aware Label Transfer (GLT) algorithm, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning.
	Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos Jiawei Liu, Zheng-jun Zha, Wei Wu, Kecheng Zheng, Qibin Sun CVPR, 2021 Oral Presentation! ArXiv / bibtex We propose a novel Spatial-Temporal Correlation and Topology Learning framework (CTL) to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation.
	Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification Zhizheng Zhang , Cuiling Lan, Wenjun Zeng, Quanzeng You , Zicheng Liu , Kecheng Zheng, Zhibo Chen ArXiv, 2021 ArXiv / code(coming soon) / bibtex We propose a Disentanglement-based Cross-Domain Feature Augmentation (DCDFA) strategy, where the augmented features characterize well the target and source domain data distributions while inheriting reliable identity labels.
	Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification Kecheng Zheng, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, Zheng-jun Zha AAAI, 2021 ArXiv / Code / bibtex We propose to estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels.
	Memory Enhanced Embedding Learning for Cross-Modal Video-Text Retrieval Rui Zhao, Kecheng Zheng, Zheng-jun Zha, Hongtao Xie, Jiebo Luo Equal Contribution ArXiv*, 2020 ArXiv / bibtex We propose a novel memory enhanced embedding learning (MEEL) method for video-text retrieval.
	Hierarchical Gumbel Attention Network for Text-based Person Search Kecheng Zheng, Wu Liu, Jiawei Liu, Tao Mei, Zheng-jun Zha ACM MM, 2020 Acm / bibtex We propose a novel hierarchical Gumbel attention network for text-based person search via Gumbel top-k re-parameterization algorithm.
	Abstract Reasoning with Distracting Features Kecheng Zheng, Wei Wei, Zheng-jun Zha NeurIPS, 2019 ArXiv / Code / bibtex We first illustrate that one of the main challenges in such a reasoning task is the presence of distracting features, which requires the learning algorithm to leverage counterevidence and to reject any of the false hypotheses in order to learn the true patterns.
	STACKED CONVOLUTIONAL DEEP ENCODING NETWORK FOR VIDEO-TEXT RETRIEVAL Rui Zhao, Kecheng Zheng, Zheng-jun Zha Equal Contribution ICME, 2020* ArXiv / bibtex We propose a stacked convolutional deep encoding network for video-text retrieval task, which considers to simultaneously encode long-range and short-range dependency in the videos and texts.
	LA-Net: Layout-Aware Dense Network for Monocular Depth Estimation Kecheng Zheng, Zheng-jun Zha, Yang Cao, Xuejin Chen, Feng Wu ACM MM, 2018 Oral Presentation! Acm / bibtex We propose a novel Layout-Aware Convolutional Neural Network (LA-Net) for accurate monocular depth estimation by simultaneously perceiving scene layout and local depth details.