Search Results for author: De Cheng

Found 23 papers, 9 papers with code

Person Re-Identification by Multi-Channel Parts-Based CNN With Improved Triplet Loss Function

no code implementations CVPR 2016 De Cheng, Yihong Gong, Sanping Zhou, Jinjun Wang, Nanning Zheng

Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of view between cameras.

Person Re-Identification

Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification

no code implementations25 Jul 2017 De Cheng, Yihong Gong, Zhihui Li, Weiwei Shi, Alexander G. Hauptmann, Nanning Zheng

The proposed method can take full advantages of the structured distance relationships among these training samples, with the constructed complete graph.

Person Re-Identification

Complex Event Detection by Identifying Reliable Shots From Untrimmed Videos

no code implementations ICCV 2017 Hehe Fan, Xiaojun Chang, De Cheng, Yi Yang, Dong Xu, Alexander G. Hauptmann

relevant) to the given event class, we formulate this task as a multi-instance learning (MIL) problem by taking each video as a bag and the video shots in each video as instances.

Event Detection

Support-Set Based Cross-Supervision for Video Grounding

no code implementations ICCV 2021 Xinpeng Ding, Nannan Wang, Shiwei Zhang, De Cheng, Xiaomeng Li, Ziyuan Huang, Mingqian Tang, Xinbo Gao

The contrastive objective aims to learn effective representations by contrastive learning, while the caption objective can train a powerful video encoder supervised by texts.

Contrastive Learning Video Grounding

Single Image Dehazing with An Independent Detail-Recovery Network

no code implementations22 Sep 2021 Yan Li, De Cheng, Jiande Sun, Dingwen Zhang, Nannan Wang, Xinbo Gao

In this paper, we propose a single image dehazing method with an independent Detail Recovery Network (DRN), which considers capturing the details from the input image over a separate network and then integrates them into a coarse dehazed image.

Image Dehazing Single Image Dehazing

Text-based Person Search in Full Images via Semantic-Driven Proposal Generation

1 code implementation27 Sep 2021 Shizhou Zhang, De Cheng, Wenlong Luo, Yinghui Xing, Duo Long, Hao Li, Kai Niu, Guoqiang Liang, Yanning Zhang

Finding target persons in full scene images with a query of text description has important practical applications in intelligent video surveillance. However, different from the real-world scenarios where the bounding boxes are not available, existing text-based person retrieval methods mainly focus on the cross modal matching between the query text descriptions and the gallery of cropped pedestrian images.

Person Search Retrieval +3

Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id

no code implementations29 Sep 2021 De Cheng, Jingyu Zhou, Nannan Wang, Xinbo Gao

However, since person Re-Id is an open-set problem, the clustering based methods often leave out lots of outlier instances or group the instances into the wrong clusters, thus they can not make full use of the training samples as a whole.

Clustering Contrastive Learning +3

Robust Region Feature Synthesizer for Zero-Shot Object Detection

1 code implementation CVPR 2022 Peiliang Huang, Junwei Han, De Cheng, Dingwen Zhang

Zero-shot object detection aims at incorporating class semantic vectors to realize the detection of (both seen and) unseen classes given an unconstrained test image.

Generalized Zero-Shot Object Detection Object +2

Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction

no code implementations29 Mar 2022 De Cheng, Yan Li, Dingwen Zhang, Nannan Wang, Xinbo Gao, Jiande Sun

To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios.

Image Dehazing Single Image Dehazing

Hybrid Routing Transformer for Zero-Shot Learning

no code implementations29 Mar 2022 De Cheng, Gerong Wang, Bo wang, Qiang Zhang, Jungong Han, Dingwen Zhang

This design makes the presented transformer model a hybrid of 1) top-down and bottom-up attention pathways and 2) dynamic and static routing pathways.

Attribute Zero-Shot Learning

Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation

no code implementations CVPR 2022 De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama

In label-noise learning, estimating the transition matrix has attracted more and more attention as the matrix plays an important role in building statistically consistent classifiers.

Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model

1 code implementation17 Aug 2022 Yinghui Xing, Qirui Wu, De Cheng, Shizhou Zhang, Guoqiang Liang, Peng Wang, Yanning Zhang

To make the final image feature concentrate more on the target visual concept, a Class-Aware Visual Prompt Tuning (CAVPT) scheme is further proposed in our DPT, where the class-aware visual prompt is generated dynamically by performing the cross attention between text prompts features and image patch token embeddings to encode both the downstream task-related information and visual instance information.

General Knowledge Language Modelling +1

Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization Feature Space

1 code implementation NIPS 2022 De Cheng, Yixiong Ning, Nannan Wang, Xinbo Gao, Heng Yang, Yuxuan Du, Bo Han, Tongliang Liu

We show that the cycle-consistency regularization helps to minimize the volume of the transition matrix T indirectly without exploiting the estimated noisy class posterior, which could further encourage the estimated transition matrix T to converge to its optimal solution.

Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification

no code implementations30 Nov 2022 De Cheng, Haichun Tai, Nannan Wang, Zhen Wang, Xinbo Gao

In this paper, we propose a Neighbour Consistency guided Pseudo Label Refinement (NCPLR) framework, which can be regarded as a transductive form of label propagation under the assumption that the prediction of each example should be similar to its nearest neighbours'.

Clustering Person Retrieval +3

Weakly-Supervised Temporal Action Localization with Bidirectional Semantic Consistency Constraint

1 code implementation25 Apr 2023 Guozhang Li, De Cheng, Xinpeng Ding, Nannan Wang, Jie Li, Xinbo Gao

The proposed Bi-SCC firstly adopts a temporal context augmentation to generate an augmented video that breaks the correlation between positive actions and their co-scene actions in the inter-video; Then, a semantic consistency constraint (SCC) is used to enforce the predictions of the original video and augmented video to be consistent, hence suppressing the co-scene actions.

Weakly-supervised Temporal Action Localization Weakly Supervised Temporal Action Localization

Boosting Weakly-Supervised Temporal Action Localization with Text Information

1 code implementation CVPR 2023 Guozhang Li, De Cheng, Xinpeng Ding, Nannan Wang, Xiaoyu Wang, Xinbo Gao

For the discriminative objective, we propose a Text-Segment Mining (TSM) mechanism, which constructs a text description based on the action class label, and regards the text as the query to mine all class-related segments.

Sentence Weakly-supervised Temporal Action Localization +1

Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement

no code implementations22 May 2023 De Cheng, Xiaojian Huang, Nannan Wang, Lingfeng He, Zhihui Li, Xinbo Gao

Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims at learning modality-invariant features from unlabeled cross-modality dataset, which is crucial for practical applications in video surveillance systems.

Person Re-Identification

Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID

no code implementations22 May 2023 De Cheng, Lingfeng He, Nannan Wang, Shizhou Zhang, Zhen Wang, Xinbo Gao

To this end, we propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters.

Contrastive Learning Person Re-Identification

Ground-to-Aerial Person Search: Benchmark Dataset and Approach

1 code implementation24 Aug 2023 Shizhou Zhang, Qingchun Yang, De Cheng, Yinghui Xing, Guoqiang Liang, Peng Wang, Yanning Zhang

In this work, we construct a large-scale dataset for Ground-to-Aerial Person Search, named G2APS, which contains 31, 770 images of 260, 559 annotated bounding boxes for 2, 644 identities appearing in both of the UAVs and ground surveillance cameras.

Knowledge Distillation Person Search

EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model

no code implementations5 Dec 2023 Guozhang Li, Xinpeng Ding, De Cheng, Jie Li, Nannan Wang, Xinbo Gao

To further clarify the noise of expanded boundaries, we combine mutual learning with a tailored proposal-level contrastive objective to use a learnable approach to harmonize a balance between incomplete yet clean (initial) and comprehensive yet noisy (expanded) boundaries for more precise ones.

Boundary Detection Language Modelling +2

Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models

1 code implementation11 Dec 2023 Yubin Wang, Xinyang Jiang, De Cheng, Dongsheng Li, Cairong Zhao

To address this limitation and prioritize harnessing structured knowledge, this paper advocates for leveraging LLMs to build a graph for each description to model the entities and attributes describing the category, as well as their correlations.

Prompt Engineering

Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID

no code implementations1 Feb 2024 Lingfeng He, De Cheng, Nannan Wang, Xinbo Gao

In response, we introduce a Modality-Unified Label Transfer (MULT) module that simultaneously accounts for both homogeneous and heterogeneous fine-grained instance-level structures, yielding high-quality cross-modality label associations.

Person Re-Identification Pseudo Label +1

Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure

1 code implementation12 Mar 2024 De Cheng, Yanling Ji, Dong Gong, Yan Li, Nannan Wang, Junwei Han, Dingwen Zhang

It considers the characteristics of the image restoration task with multiple degenerations in continual learning, and the knowledge for different degenerations can be shared and accumulated in the unified network structure.

Continual Learning Image Restoration +2

Cannot find the paper you are looking for? You can Submit a new open access paper.