5 citations · 17 across the 19 of their papers we have counts for
23 papers
PAPT++: Risk-Aware Adversarial Tuning and Generation for Single Domain Generalization
Zhipeng Xu, De Cheng, Xinyang Jiang +5
Single domain generalization (SDG) aims to learn a model from one labeled source domain that generalizes to unseen target domains. A common strategy is to enrich the source distrib…
Symbiosis-Inspired Knowledge Distillation for Incremental Object Detection
Mingyue Zeng, De Cheng, Zhipeng Xu +3
Incremental object detection (IOD) aims to extend detectors to new categories while retaining previously acquired knowledge. Existing methods often adopt a class incremental learni…
Dual-Branch Cross-Projection Debiasing through Diffusion-based Disentanglement
Xiangqian Zhao, Xinyang Jiang, Zhipeng Xu +5
Foundation models trained on biased datasets often rely on spurious correlations between target labels and non-causal attributes, resulting in poor generalization on minority group…
Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization
Zhipeng Xu, De Cheng, Xinyang Jiang +3
Single domain generalization (SDG) aims to learn a robust model, which could perform well on many unseen domains while there is only one single domain available for training. One o…
Reasoning-Driven Multimodal LLM for Domain Generalization
Zhipeng Xu, Zilong Wang, Xinyang Jiang +3
This paper addresses the domain generalization (DG) problem in deep learning. While most DG methods focus on enforcing visual feature invariance, we leverage the reasoning capabili…
Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification
Haonan Shi, Yubin Wang, De Cheng +3
Unsupervised visible-infrared person re-identification (USVI-ReID) aims to learn modality-invariant image features from unlabeled cross-modal person datasets by reducing the modali…
Harnessing Textual Semantic Priors for Knowledge Transfer and Refinement in CLIP-Driven Continual Learning
Lingfeng He, De Cheng, Di Xu +2
Continual learning (CL) aims to equip models with the ability to learn from a stream of tasks without forgetting previous knowledge. With the progress of vision-language models lik…
CKAA: Cross-subspace Knowledge Alignment and Aggregation for Robust Continual Learning
Lingfeng He, De Cheng, Zhiheng Ma +4
Continual Learning (CL) empowers AI models to continuously learn from sequential task streams. Recently, parameter-efficient fine-tuning (PEFT)-based CL methods have garnered incre…
Prompt Disentanglement via Language Guidance and Representation Alignment for Domain Generalization
De Cheng, Zhipeng Xu, Xinyang Jiang +3
Domain Generalization (DG) seeks to develop a versatile model capable of performing effectively on unseen target domains. Notably, recent advances in pre-trained Visual Foundation…
EKPC: Elastic Knowledge Preservation and Compensation for Class-Incremental Learning
Huaijie Wang, De Cheng, Lingfeng He +4
Class-Incremental Learning (CIL) aims to enable AI models to continuously learn from sequentially arriving data of different classes over time while retaining previously acquired k…
Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID
De Cheng, Lingfeng He, Nannan Wang +2
Unsupervised visible-infrared person re-identification (USL-VI-ReID) seeks to match pedestrian images of the same individual across different modalities without human annotations f…
StPR: Spatiotemporal Preservation and Routing for Exemplar-Free Video Class-Incremental Learning
Huaijie Wang, De Cheng, Guozhang Li +5
Video Class-Incremental Learning (VCIL) seeks to develop models that continuously learn new action categories over time without forgetting previously acquired knowledge. Unlike tra…
Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning
De Cheng, Yue Lu, Lingfeng He +4
Continual Learning (CL) aims to equip AI models with the ability to learn a sequence of tasks over time, without forgetting previously learned knowledge. Recently, State Space Mode…
Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detection
Ying Yang, De Cheng, Chaowei Fang +4
Unsupervised out-of-distribution (OOD) detection aims to identify out-of-domain data by learning only from unlabeled In-Distribution (ID) training samples, which is crucial for dev…
Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID
Lingfeng He, De Cheng, Nannan Wang +1
Unsupervised visible-infrared person re-identification (USL-VI-ReID) endeavors to retrieve pedestrian images of the same identity from different modalities without annotations. Whi…
EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model
Guozhang Li, Xinpeng Ding, De Cheng +3
Early weakly supervised video grounding (WSVG) methods often struggle with incomplete boundary detection due to the absence of temporal boundary annotations. To bridge the gap betw…
CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization
Ruoyu Zhao, Mingrui Zhu, Shiyin Dong +2
We propose CatVersion, an inversion-based method that learns the personalized concept through a handful of examples. Subsequently, users can utilize text prompts to generate images…
Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement
De Cheng, Xiaojian Huang, Nannan Wang +3
Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims at learning modality-invariant features from unlabeled cross-modality dataset, which is crucial f…
Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID
De Cheng, Lingfeng He, Nannan Wang +3
Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to match pedestrian images of the same identity from different modalities without annotations. Existing wo…
Boosting Weakly-Supervised Temporal Action Localization with Text Information
Guozhang Li, De Cheng, Xinpeng Ding +3
Due to the lack of temporal annotation, current Weakly-supervised Temporal Action Localization (WTAL) methods are generally stuck into over-complete or incomplete localization. In…
Weakly-Supervised Temporal Action Localization with Bidirectional Semantic Consistency Constraint
Guozhang Li, De Cheng, Xinpeng Ding +3
Weakly Supervised Temporal Action Localization (WTAL) aims to classify and localize temporal boundaries of actions for the video, given only video-level category labels in the trai…
Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification
De Cheng, Haichun Tai, Nannan Wang +2
Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by…
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation
De Cheng, Tongliang Liu, Yixiong Ning +5
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 classifie…