UNO: Unifying One-stage Video Scene Graph Generation via Object-Centric Visual Representation Learning

• URL: http://arxiv.org/abs/2509.06165v2
• Date: Sun, 09 Nov 2025 17:05:22 GMT
• Title: UNO: Unifying One-stage Video Scene Graph Generation via Object-Centric Visual Representation Learning
• Authors: Huy Le, Nhat Chung, Tung Kieu, Jingkang Yang, Ngan Le,
• Abstract summary: Video Scene Graph Generation (VidSGG) aims to represent dynamic visual content by detecting objects and modeling their temporal interactions as structured graphs.<n>We present UNO (UNified Object-centric VidSGG), a single-stage, unified framework that jointly addresses both tasks within an end-to-end architecture.
• Score: 22.190757229228996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Scene Graph Generation (VidSGG) aims to represent dynamic visual content by detecting objects and modeling their temporal interactions as structured graphs. Prior studies typically target either coarse-grained box-level or fine-grained panoptic pixel-level VidSGG, often requiring task-specific architectures and multi-stage training pipelines. In this paper, we present UNO (UNified Object-centric VidSGG), a single-stage, unified framework that jointly addresses both tasks within an end-to-end architecture. UNO is designed to minimize task-specific modifications and maximize parameter sharing, enabling generalization across different levels of visual granularity. The core of UNO is an extended slot attention mechanism that decomposes visual features into object and relation slots. To ensure robust temporal modeling, we introduce object temporal consistency learning, which enforces consistent object representations across frames without relying on explicit tracking modules. Additionally, a dynamic triplet prediction module links relation slots to corresponding object pairs, capturing evolving interactions over time. We evaluate UNO on standard box-level and pixel-level VidSGG benchmarks. Results demonstrate that UNO not only achieves competitive performance across both tasks but also offers improved efficiency through a unified, object-centric design.

Related papers

• Referring Video Object Segmentation with Cross-Modality Proxy Queries [23.504655272754587]
  Referring video object segmentation (RVOS) is an emerging cross-modality task that aims to generate pixel-level maps of the target objects referred by given textual expressions.<n>Recent approaches address cross-modality alignment through conditional queries, tracking the target object using a query-response based mechanism.<n>We propose a novel RVOS architecture called ProxyFormer, which introduces a set of proxy queries to integrate visual and text semantics.
  arXiv  Detail & Related papers  (2025-11-26T07:45:41Z)
• FOCUS: Unified Vision-Language Modeling for Interactive Editing Driven by Referential Segmentation [55.01077993490845]
  Recent Large Vision Language Models (LVLMs) demonstrate promising capabilities in unifying visual understanding and generative modeling.<n>We introduce FOCUS, a unified LVLM that integrates segmentation-aware perception and controllable object-centric generation within an end-to-end framework.
  arXiv  Detail & Related papers  (2025-06-20T07:46:40Z)
• Spatio-temporal Graph Learning on Adaptive Mined Key Frames for High-performance Multi-Object Tracking [5.746443489229576]
  Key Frame Extraction (KFE) module leverages reinforcement learning to adaptively segment videos.<n> Intra-Frame Feature Fusion (IFF) module uses a Graph Convolutional Network (GCN) to facilitate information exchange between the target and surrounding objects.<n>Our proposed tracker achieves impressive results on the MOT17 dataset.
  arXiv  Detail & Related papers  (2025-01-17T11:36:38Z)
• Top-Down Guidance for Learning Object-Centric Representations [30.06924788022504]
  Top-Down Guided Network (TDGNet) is a top-down pathway to improve object-centric representations.<n>We show that TDGNet outperforms current object-centric models on multiple datasets of varying complexity.
  arXiv  Detail & Related papers  (2024-05-17T07:48:27Z)
• Multi-Scene Generalized Trajectory Global Graph Solver with Composite Nodes for Multiple Object Tracking [61.69892497726235]
  Composite Node Message Passing Network (CoNo-Link) is a framework for modeling ultra-long frames information for association. In addition to the previous method of treating objects as nodes, the network innovatively treats object trajectories as nodes for information interaction. Our model can learn better predictions on longer-time scales by adding composite nodes.
  arXiv  Detail & Related papers  (2023-12-14T14:00:30Z)
• Identity-Consistent Aggregation for Video Object Detection [21.295859014601334]
  In Video Object Detection (VID), a common practice is to leverage the rich temporal contexts from the video to enhance the object representations in each frame. We propose ClipVID, a VID model equipped with Identity-Consistent Aggregation layers specifically designed for mining fine-grained and identity-consistent temporal contexts. Experiments demonstrate the superiority of our method: a state-of-the-art (SOTA) performance (84.7% mAP) on the ImageNet VID dataset while running at a speed about 7x faster (39.3 fps) than previous SOTAs.
  arXiv  Detail & Related papers  (2023-08-15T12:30:22Z)
• Target Adaptive Context Aggregation for Video Scene Graph Generation [36.669700084337045]
  This paper deals with a challenging task of video scene graph generation (VidSGG) We present a new em detect-to-track paradigm for this task by decoupling the context modeling for relation prediction from the complicated low-level entity tracking.
  arXiv  Detail & Related papers  (2021-08-18T12:46:28Z)
• Learning to Associate Every Segment for Video Panoptic Segmentation [123.03617367709303]
  We learn coarse segment-level matching and fine pixel-level matching together. We show that our per-frame computation model can achieve new state-of-the-art results on Cityscapes-VPS and VIPER datasets.
  arXiv  Detail & Related papers  (2021-06-17T13:06:24Z)
• Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation [79.6596425920849]
  This paper addresses the task of unsupervised video multi-object segmentation. We introduce a novel approach for more accurate and efficient unseen-temporal segmentation. We evaluate the proposed approach on DAVIS$_17$ and YouTube-VIS, and the results demonstrate that it outperforms state-of-the-art methods both in segmentation accuracy and inference speed.
  arXiv  Detail & Related papers  (2021-04-10T14:39:44Z)
• CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection [91.91911418421086]
  Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue is how to effectively capture co-saliency cues by modeling and exploiting inter-image relationships. We present an end-to-end collaborative aggregation-and-distribution network (CoADNet) to capture both salient and repetitive visual patterns from multiple images.
  arXiv  Detail & Related papers  (2020-11-10T04:28:11Z)
• MOPT: Multi-Object Panoptic Tracking [33.77171216778909]
  We introduce a novel perception task denoted as multi-object panoptic tracking (MOPT) MOPT allows for exploiting pixel-level semantic information of 'thing' and'stuff' classes, temporal coherence, and pixel-level associations over time. We present extensive quantitative and qualitative evaluations of both vision-based and LiDAR-based MOPT that demonstrate encouraging results.
  arXiv  Detail & Related papers  (2020-04-17T11:45:28Z)
• Look-into-Object: Self-supervised Structure Modeling for Object Recognition [71.68524003173219]
  We propose to "look into object" (explicitly yet intrinsically model the object structure) through incorporating self-supervisions. We show the recognition backbone can be substantially enhanced for more robust representation learning. Our approach achieves large performance gain on a number of benchmarks, including generic object recognition (ImageNet) and fine-grained object recognition tasks (CUB, Cars, Aircraft)
  arXiv  Detail & Related papers  (2020-03-31T12:22:51Z)
• Object-Centric Image Generation from Layouts [93.10217725729468]
  We develop a layout-to-image-generation method to generate complex scenes with multiple objects. Our method learns representations of the spatial relationships between objects in the scene, which lead to our model's improved layout-fidelity. We introduce SceneFID, an object-centric adaptation of the popular Fr'echet Inception Distance metric, that is better suited for multi-object images.
  arXiv  Detail & Related papers  (2020-03-16T21:40:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.