100 citations · 275 across the 14 of their papers we have counts for
14 papers
D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation
Hanjun Li, Xiujun Shu, Sunan He +5
Temporal sentence grounding (TSG) aims to locate a specific moment from an untrimmed video with a given natural language query. Recently, weakly supervised methods still have a lar…
Unified and Dynamic Graph for Temporal Character Grouping in Long Videos
Xiujun Shu, Wei Wen, Liangsheng Xu +6
Video temporal character grouping locates appearing moments of major characters within a video according to their identities. To this end, recent works have evolved from unsupervis…
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies
Bei Gan, Xiujun Shu, Ruizhi Qiao +4
Movie highlights stand out of the screenplay for efficient browsing and play a crucial role on social media platforms. Based on existing efforts, this work has two observations: (1…
See Finer, See More: Implicit Modality Alignment for Text-based Person Retrieval
Xiujun Shu, Wei Wen, Haoqian Wu +5
Text-based person retrieval aims to find the query person based on a textual description. The key is to learn a common latent space mapping between visual-textual modalities. To ac…
VLMAE: Vision-Language Masked Autoencoder
Sunan He, Taian Guo, Tao Dai +4
Image and language modeling is of crucial importance for vision-language pre-training (VLP), which aims to learn multi-modal representations from large-scale paired image-text data…
Exploiting Feature Diversity for Make-up Temporal Video Grounding
Xiujun Shu, Wei Wen, Taian Guo +3
This technical report presents the 3rd winning solution for MTVG, a new task introduced in the 4-th Person in Context (PIC) Challenge at ACM MM 2022. MTVG aims at localizing the te…
Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer
Sunan He, Taian Guo, Tao Dai +3
Real-world recognition system often encounters the challenge of unseen labels. To identify such unseen labels, multi-label zero-shot learning (ML-ZSL) focuses on transferring knowl…
Head and Body: Unified Detector and Graph Network for Person Search in Media
Xiujun Shu, Yusheng Tao, Ruizhi Qiao +3
Person search in media has seen increasing potential in Internet applications, such as video clipping and character collection. This task is common but overlooked by previous perso…
Learning to Disentangle Scenes for Person Re-identification
Xianghao Zang, Ge Li, Wei Gao +1
There are many challenging problems in the person re-identification (ReID) task, such as the occlusion and scale variation. Existing works usually tried to solve them by employing…
Exploiting Robust Unsupervised Video Person Re-identification
Xianghao Zang, Ge Li, Wei Gao +1
Unsupervised video person re-identification (reID) methods usually depend on global-level features. And many supervised reID methods employed local-level features and achieved sign…
Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks
Xin Zhang, Xiujun Shu, Bingwen Zhang +3
Radio propagation modeling and prediction is fundamental for modern cellular network planning and optimization. Conventional radio propagation models fall into two categories. Empi…
Semantic-guided Pixel Sampling for Cloth-Changing Person Re-identification
Xiujun Shu, Ge Li, Xiao Wang +2
Cloth-changing person re-identification (re-ID) is a new rising research topic that aims at retrieving pedestrians whose clothes are changed. This task is quite challenging and has…
Large-Scale Spatio-Temporal Person Re-identification: Algorithms and Benchmark
Xiujun Shu, Xiao Wang, Xianghao Zang +4
Person re-identification (re-ID) in the scenario with large spatial and temporal spans has not been fully explored. This is partially because that, existing benchmark datasets were…
Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark
Xiao Wang, Xiujun Shu, Zhipeng Zhang +4
Tracking by natural language specification is a new rising research topic that aims at locating the target object in the video sequence based on its language description. Compared…