Unified and Dynamic Graph for Temporal Character Grouping in Long Videos

no code implementations • 27 Aug 2023 • Xiujun Shu, Wei Wen, Liangsheng Xu, Ruizhi Qiao, Taian Guo, Hanjun Li, Bei Gan, Xiao Wang, Xing Sun

In this paper, we present a unified and dynamic graph (UniDG) framework for temporal character grouping.

Clustering Graph Clustering

D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation

1 code implementation • ICCV 2023 • Hanjun Li, Xiujun Shu, Sunan He, Ruizhi Qiao, Wei Wen, Taian Guo, Bei Gan, Xing Sun

Under this setup, we propose a Dynamic Gaussian prior based Grounding framework with Glance annotation (D3G), which consists of a Semantic Alignment Group Contrastive Learning module (SA-GCL) and a Dynamic Gaussian prior Adjustment module (DGA).

Contrastive Learning Sentence +1

Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies

1 code implementation • CVPR 2023 • Bei Gan, Xiujun Shu, Ruizhi Qiao, Haoqian Wu, Keyu Chen, Hanjun Li, Bo Ren

Based on existing efforts, this work has two observations: (1) For different annotators, labeling highlight has uncertainty, which leads to inaccurate and time-consuming annotations.

Highlight Detection Learning with noisy labels +1

NewsNet: A Novel Dataset for Hierarchical Temporal Segmentation

no code implementations • CVPR 2023 • Haoqian Wu, Keyu Chen, Haozhe Liu, Mingchen Zhuge, Bing Li, Ruizhi Qiao, Xiujun Shu, Bei Gan, Liangsheng Xu, Bo Ren, Mengmeng Xu, Wentian Zhang, Raghavendra Ramachandra, Chia-Wen Lin, Bernard Ghanem

Temporal video segmentation is the get-to-go automatic video analysis, which decomposes a long-form video into smaller components for the following-up understanding tasks.

Video Segmentation Video Semantic Segmentation

Few-shot Forgery Detection via Guided Adversarial Interpolation

no code implementations • 12 Apr 2022 • Haonan Qiu, Siyu Chen, Bei Gan, Kun Wang, Huafeng Shi, Jing Shao, Ziwei Liu

Notably, our method is also validated to be robust to choices of majority and minority forgery approaches.

ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis

2 code implementations • CVPR 2021 • Yinan He, Bei Gan, Siyu Chen, Yichun Zhou, Guojun Yin, Luchuan Song, Lu Sheng, Jing Shao, Ziwei Liu

To counter this emerging threat, we construct the ForgeryNet dataset, an extremely large face forgery dataset with unified annotations in image- and video-level data across four tasks: 1) Image Forgery Classification, including two-way (real / fake), three-way (real / fake with identity-replaced forgery approaches / fake with identity-remained forgery approaches), and n-way (real and 15 respective forgery approaches) classification.

Benchmarking Classification +2