Video SGG

SGG: Scene Graph Generation

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11 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

RTAformer: Relation-Transition-Aware Transformer for Video Scene Graph Generation
Oct 14, 2026Jaeyeon Heo, Geuntaek Lim, Yukyung ChoiVideo SGG Temporal Video Understanding
Sejong University, Seoul, Republic of Korea
OvDSGG: End-to-End Open-Vocabulary Dynamic Scene Graph Generation
Aug 14, 2026John Helsby, Yi Yang, Bodo Rosenhahn +1Scene Graph Generation Video SGG
University of Bath, UK · Leibniz Universität Hannover, Germany
Graph it first! Enabling Reasoning on Long-form Egocentric Videos through Scene Graphs
Jun 24, 2026Agnese Taluzzi, Riccardo Santambrogio, Simone Mentasti +2Egocentric Video QA Video QA
Politecnico di Milano, Milan, Italy · Bocconi University, Milan, Italy
SceneGraphVLM: Dynamic Scene Graph Generation from Video with Vision-Language Models
May 13, 2026Vladislav Makarov, Mark Gizetdinov, Dmitry YudinScene Graph Generation Vision-Language Models
MIRAI · MIREA · MIRAI, AXXX
Frequency-guided Multi-level Reasoning for Scene Graph Generation in Video
Apr 19, 2026Chenxing Li, Yiping Duan, Xiaoming TaoLong-Tail Learning Relational Reasoning
Department of Electronic Engineering, Tsinghua University, Beijing, China · State Key Laboratory of Space Network and Communications · Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China +1
Revisiting Weakly-Supervised Video Scene Graph Generation via Pair Affinity Learning
Mar 23, 2026Minseok Kang, Minhyeok Lee, Minjung Kim +5Video SGG Scene Graph Generation
Yonsei University · LG Electronics
Towards Spatio-Temporal World Scene Graph Generation from Monocular Videos
Mar 13, 2026Rohith Peddi, Saurabh, Shravan Shanmugam +4Scene Graphs Video SGG
The University of Texas at Dallas, Richardson, TX 75080, USA · Indian Institute of Technology Delhi, New Delhi, India
Synthetic Visual Genome 2: Extracting Large-scale Spatio-Temporal Scene Graphs from Videos
Feb 26, 2026Ziqi Gao, Jieyu Zhang, Wisdom Oluchi Ikezogwo +10Video SGG Scene Graph Generation
Allen Institute for AI · University of Washington · Microsoft +1
GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking
Feb 19, 2026Zixu Cheng, Da Li, Jian Hu +4Video SGG Video Reasoning
Queen Mary University of London · Samsung AI Centre Cambridge · Shanghai Artificial Intelligence Laboratory +1
THYME: Temporal Hierarchical-Cyclic Interactivity Modeling for Video Scene Graphs in Aerial Footage
Jul 12, 2025Trong-Thuan Nguyen, Pha Nguyen, Jackson Cothren +3Video SGG Temporal Video Understanding
University of Arkansas · University of Science, VNU-HCM; Vietnam National University, Ho Chi Minh, Viet Nam · Ohio State University
A Closer Look at Dynamic Scene Graph Generation In the Era of Multimodal Large Language Models
Mar 20, 2025Xuanming Cui, Jaiminkumar Ashokbhai Bhoi, Chionh Wei Peng +2Scene Graph Generation Video SGG
University of Central Florida · DSO National Laboratories

Oct 14, 2026J/K move · Enter open · S save

RTAformer: Relation-Transition-Aware Transformer for Video Scene Graph Generation

Jaeyeon Heo, Geuntaek Lim, Yukyung Choi

Sejong University, Seoul, Republic of Korea

Video SGG Temporal Video Understanding Transformer

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Video scene graph generation (VidSGG) aims to capture objects and their evolving relationships in videos. This requires accounting for both the persistence of object identities and abrupt changes in relationships. Existing methods aggregate information across frames to exploit temporal context, but an emphasis on consistency can obscure these changes. As a result, a model may retain its previous prediction even after the underlying relation has changed. We refer to this failure mode as relational semantic inertia. To address this issue, we propose Relation-Transition-Aware Transformer (RTAformer), a one-stage framework that separately models entity persistence and relation changes. It injects calibrated differences between adjacent-frame relation queries to guide predicate updates, while cross-frame instance alignment during training helps reduce cross-frame representational drift. Experiments on Action Genome show improvements in recall and mean recall over existing methods, with ablation studies supporting the contributions of the proposed components and comparisons using matched backbones showing larger gains on relation-transition frames than on non-transition frames.