SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction

• URL: http://arxiv.org/abs/2507.15852v2
• Date: Tue, 22 Jul 2025 10:51:42 GMT
• Title: SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction
• Authors: Zhixiong Zhang, Shuangrui Ding, Xiaoyi Dong, Songxin He, Jianfan Lin, Junsong Tang, Yuhang Zang, Yuhang Cao, Dahua Lin, Jiaqi Wang,
• Abstract summary: Video Object (VOS) is a core task in computer vision, requiring models to track and segment target objects across video frames.<n>We propose Segment Concept (SeC), a concept-driven segmentation framework that shifts from conventional feature matching to the progressive construction and utilization of high-level, object-centric representations.<n>SeC achieves an 11.8-point improvement over SAM SeCVOS, establishing a new state-of-the-art concept-aware video object segmentation.
• Score: 65.15449703659772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Object Segmentation (VOS) is a core task in computer vision, requiring models to track and segment target objects across video frames. Despite notable advances with recent efforts, current techniques still lag behind human capabilities in handling drastic visual variations, occlusions, and complex scene changes. This limitation arises from their reliance on appearance matching, neglecting the human-like conceptual understanding of objects that enables robust identification across temporal dynamics. Motivated by this gap, we propose Segment Concept (SeC), a concept-driven segmentation framework that shifts from conventional feature matching to the progressive construction and utilization of high-level, object-centric representations. SeC employs Large Vision-Language Models (LVLMs) to integrate visual cues across diverse frames, constructing robust conceptual priors. During inference, SeC forms a comprehensive semantic representation of the target based on processed frames, realizing robust segmentation of follow-up frames. Furthermore, SeC adaptively balances LVLM-based semantic reasoning with enhanced feature matching, dynamically adjusting computational efforts based on scene complexity. To rigorously assess VOS methods in scenarios demanding high-level conceptual reasoning and robust semantic understanding, we introduce the Semantic Complex Scenarios Video Object Segmentation benchmark (SeCVOS). SeCVOS comprises 160 manually annotated multi-scenario videos designed to challenge models with substantial appearance variations and dynamic scene transformations. In particular, SeC achieves an 11.8-point improvement over SAM 2.1 on SeCVOS, establishing a new state-of-the-art in concept-aware video object segmentation.

Related papers

• Compositional Video Synthesis by Temporal Object-Centric Learning [3.2228025627337864]
  We present a novel framework for compositional video synthesis that leverages temporally consistent object-centric representations.<n>Our approach explicitly captures temporal dynamics by learning pose invariant object-centric slots and conditioning them on pretrained diffusion models.<n>This design enables high-quality, pixel-level video synthesis with superior temporal coherence.
  arXiv  Detail & Related papers  (2025-07-28T14:11:04Z)
• Advancing Complex Wide-Area Scene Understanding with Hierarchical Coresets Selection [10.810165156142563]
  Scene understanding is one of the core tasks in computer vision, aiming to extract semantic information from images to identify objects.<n>Existing Vision-Language Models (VLMs) face challenges in adaptation to unseen complex wide-area scenes.<n>This paper proposes a Hierarchical Coresets Selection mechanism to advance the adaptation ofVLMs in complex wide-area scene understanding.
  arXiv  Detail & Related papers  (2025-07-17T12:29:06Z)
• A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects [53.15503034595476]
  Video Scene Parsing (VSP) has emerged as a cornerstone in computer vision.<n>VSP has emerged as a cornerstone in computer vision, facilitating the simultaneous segmentation, recognition, and tracking of diverse visual entities in dynamic scenes.
  arXiv  Detail & Related papers  (2025-06-16T14:39:03Z)
• Distilling Spectral Graph for Object-Context Aware Open-Vocabulary Semantic Segmentation [47.047267066525265]
  We introduce a novel approach that incorporates object-level contextual knowledge within images.<n>Our proposed approach achieves state-of-the-art performance with strong generalizability across diverse datasets.
  arXiv  Detail & Related papers  (2024-11-26T06:34:48Z)
• Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases [69.46487306858789]
  Conditional Autoregressive Slot Attention (CA-SA) is a framework that enhances the temporal consistency of extracted object-centric representations in video-centric vision tasks. We present qualitative and quantitative results showing that our proposed method outperforms the considered baselines on downstream tasks.
  arXiv  Detail & Related papers  (2024-10-21T07:44:44Z)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation [35.063881868130075]
  This paper studies referring video object segmentation (RVOS) by boosting video-level visual-linguistic alignment. We propose Semantic-assisted Object Cluster (SOC), which aggregates video content and textual guidance for unified temporal modeling and cross-modal alignment. We conduct extensive experiments on popular RVOS benchmarks, and our method outperforms state-of-the-art competitors on all benchmarks by a remarkable margin.
  arXiv  Detail & Related papers  (2023-05-26T15:13:44Z)
• ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation [17.019848796027485]
  Self-supervised visual pre-training models have shown great promise in representing pixel-level semantic relationships. In this work, we investigate the pixel-level semantic aggregation in self-trained models as image encodes and design concepts. We propose the Adaptive Concept Generator (ACG) which adaptively maps these prototypes to informative concepts for each image.
  arXiv  Detail & Related papers  (2022-10-12T06:16:34Z)
• Scalable Video Object Segmentation with Identification Mechanism [125.4229430216776]
  This paper explores the challenges of achieving scalable and effective multi-object modeling for semi-supervised Video Object (VOS) We present two innovative approaches, Associating Objects with Transformers (AOT) and Associating Objects with Scalable Transformers (AOST) Our approaches surpass the state-of-the-art competitors and display exceptional efficiency and scalability consistently across all six benchmarks.
  arXiv  Detail & Related papers  (2022-03-22T03:33:27Z)
• Contrastive Transformation for Self-supervised Correspondence Learning [120.62547360463923]
  We study the self-supervised learning of visual correspondence using unlabeled videos in the wild. Our method simultaneously considers intra- and inter-video representation associations for reliable correspondence estimation. Our framework outperforms the recent self-supervised correspondence methods on a range of visual tasks.
  arXiv  Detail & Related papers  (2020-12-09T14:05:06Z)
• Visual Concept Reasoning Networks [93.99840807973546]
  A split-transform-merge strategy has been broadly used as an architectural constraint in convolutional neural networks for visual recognition tasks. We propose to exploit this strategy and combine it with our Visual Concept Reasoning Networks (VCRNet) to enable reasoning between high-level visual concepts. Our proposed model, VCRNet, consistently improves the performance by increasing the number of parameters by less than 1%.
  arXiv  Detail & Related papers  (2020-08-26T20:02:40Z)

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.