20 citations · 59 across the 13 of their papers we have counts for
13 papers
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…
Knowledge-Enhanced Hierarchical Information Correlation Learning for Multi-Modal Rumor Detection
Jiawei Liu, Jingyi Xie, Fanrui Zhang +2
The explosive growth of rumors with text and images on social media platforms has drawn great attention. Existing studies have made significant contributions to cross-modal informa…
Sounding Video Generator: A Unified Framework for Text-guided Sounding Video Generation
Jiawei Liu, Weining Wang, Sihan Chen +2
As a combination of visual and audio signals, video is inherently multi-modal. However, existing video generation methods are primarily intended for the synthesis of visual frames,…
Label Noise-Resistant Mean Teaching for Weakly Supervised Fake News Detection
Jingyi Xie, Jiawei Liu, Zheng-Jun Zha
Fake news spreads at an unprecedented speed, reaches global audiences and poses huge risks to users and communities. Most existing fake news detection algorithms focus on building…
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,…
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…
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…
Temporal Attribute-Appearance Learning Network for Video-based Person Re-Identification
Jiawei Liu, Xierong Zhu, Zheng-Jun Zha
Video-based person re-identification aims to match a specific pedestrian in surveillance videos across different time and locations. Human attributes and appearance are complementa…
Co-Saliency Spatio-Temporal Interaction Network for Person Re-Identification in Videos
Jiawei Liu, Zheng-Jun Zha, Xierong Zhu +1
Person re-identification aims at identifying a certain pedestrian across non-overlapping camera networks. Video-based re-identification approaches have gained significant attention…
CA3Net: Contextual-Attentional Attribute-Appearance Network for Person Re-Identification
Jiawei Liu, Zheng-Jun Zha, Hongtao Xie +2
Person re-identification aims to identify the same pedestrian across non-overlapping camera views. Deep learning techniques have been applied for person re-identification recently,…