30 citations · 152 across the 26 of their papers we have counts for
26 papers
Likelihood-Aware Semantic Alignment for Full-Spectrum Out-of-Distribution Detection
Fan Lu, Kai Zhu, Kecheng Zheng +2
Full-spectrum out-of-distribution (F-OOD) detection aims to accurately recognize in-distribution (ID) samples while encountering semantic and covariate shifts simultaneously. Howev…
Regularized Mask Tuning: Uncovering Hidden Knowledge in Pre-trained Vision-Language Models
Kecheng Zheng, Wei Wu, Ruili Feng +6
Prompt tuning and adapter tuning have shown great potential in transferring pre-trained vision-language models (VLMs) to various downstream tasks. In this work, we design a new typ…
CoDeF: Content Deformation Fields for Temporally Consistent Video Processing
Hao Ouyang, Qiuyu Wang, Yuxi Xiao +6
We present the content deformation field CoDeF as a new type of video representation, which consists of a canonical content field aggregating the static contents in the entire vide…
Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase
Qiuyu Wang, Zifan Shi, Kecheng Zheng +3
Despite the rapid advance of 3D-aware image synthesis, existing studies usually adopt a mixture of techniques and tricks, leaving it unclear how each part contributes to the final…
Cones 2: Customizable Image Synthesis with Multiple Subjects
Zhiheng Liu, Yifei Zhang, Yujun Shen +7
Synthesizing images with user-specified subjects has received growing attention due to its practical applications. Despite the recent success in single subject customization, exist…
Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection
Fan Lu, Kai Zhu, Wei Zhai +2
Semantically coherent out-of-distribution (SCOOD) detection aims to discern outliers from the intended data distribution with access to unlabeled extra set. The coexistence of in-d…
Cones: Concept Neurons in Diffusion Models for Customized Generation
Zhiheng Liu, Ruili Feng, Kai Zhu +6
Human brains respond to semantic features of presented stimuli with different neurons. It is then curious whether modern deep neural networks admit a similar behavior pattern. Spec…
Neural Dependencies Emerging from Learning Massive Categories
Ruili Feng, Kecheng Zheng, Kai Zhu +7
This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category c…
Rank Diminishing in Deep Neural Networks
Ruili Feng, Kecheng Zheng, Yukun Huang +3
The rank of neural networks measures information flowing across layers. It is an instance of a key structural condition that applies across broad domains of machine learning. In pa…
Principled Knowledge Extrapolation with GANs
Ruili Feng, Jie Xiao, Kecheng Zheng +4
Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions. To imitate this ability via generative models, previous wor…
FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization
Kecheng Zheng, Yang Cao, Kai Zhu +2
MLP-like models built entirely upon multi-layer perceptrons have recently been revisited, exhibiting the comparable performance with transformers. It is one of most promising archi…
Modality-Adaptive Mixup and Invariant Decomposition for RGB-Infrared Person Re-Identification
Zhipeng Huang, Jiawei Liu, Liang Li +2
RGB-infrared person re-identification is an emerging cross-modality re-identification task, which is very challenging due to significant modality discrepancy between RGB and infrar…
Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification
Jiawei Liu, Zhipeng Huang, Liang Li +2
Generalizable person re-identification aims to learn a model with only several labeled source domains that can perform well on unseen domains. Without access to the unseen domain,…
Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification
Zizheng Yang, Xin Jin, Kecheng Zheng +1
Existing person re-identification (ReID) methods typically directly load the pre-trained ImageNet weights for initialization. However, as a fine-grained classification task, ReID i…
Calibrated Feature Decomposition for Generalizable Person Re-Identification
Kecheng Zheng, Jiawei Liu, Wei Wu +2
Existing disentangled-based methods for generalizable person re-identification aim at directly disentangling person representations into domain-relevant interference and identity-r…
Semi-Supervised Domain Generalizable Person Re-Identification
Lingxiao He, Wu Liu, Jian Liang +4
Existing person re-identification (re-id) methods are stuck when deployed to a new unseen scenario despite the success in cross-camera person matching. Recent efforts have been sub…
Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification
Kecheng Zheng, Cuiling Lan, Wenjun Zeng +3
Occluded person re-identification (ReID) aims to match person images with occlusion. It is fundamentally challenging because of the serious occlusion which aggravates the misalignm…
Adaptive Domain-Specific Normalization for Generalizable Person Re-Identification
Jiawei Liu, Zhipeng Huang, Kecheng Zheng +3
Although existing person re-identification (Re-ID) methods have shown impressive accuracy, most of them usually suffer from poor generalization on unseen target domain. Thus, gener…
Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos
Jiawei Liu, Zheng-Jun Zha, Wei Wu +2
Video-based person re-identification aims to match pedestrians from video sequences across non-overlapping camera views. The key factor for video person re-identification is to eff…
Memory Enhanced Embedding Learning for Cross-Modal Video-Text Retrieval
Rui Zhao, Kecheng Zheng, Zheng-Jun Zha +2
Cross-modal video-text retrieval, a challenging task in the field of vision and language, aims at retrieving corresponding instance giving sample from either modality. Existing app…
Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification
Zhizheng Zhang, Cuiling Lan, Wenjun Zeng +4
Unsupervised domain adaptive (UDA) person re-identification (ReID) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain for person matching.…
Group-aware Label Transfer for Domain Adaptive Person Re-identification
Kecheng Zheng, Wu Liu, Lingxiao He +3
Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at adapting the model trained on a labeled source-domain dataset to a target-domain dataset without any furt…
Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization
Xin Jin, Tianyu He, Kecheng Zheng +7
Cloth-Changing person re-identification (CC-ReID) aims at matching the same person across different locations over a long-duration, e.g., over days, and therefore inevitably meets…
Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification
Kecheng Zheng, Cuiling Lan, Wenjun Zeng +2
Many unsupervised domain adaptive (UDA) person re-identification (ReID) approaches combine clustering-based pseudo-label prediction with feature fine-tuning. However, because of do…