Towards Open-Vocabulary Video Semantic Segmentation

• URL: http://arxiv.org/abs/2412.09329v1
• Date: Thu, 12 Dec 2024 14:53:16 GMT
• Title: Towards Open-Vocabulary Video Semantic Segmentation
• Authors: Xinhao Li, Yun Liu, Guolei Sun, Min Wu, Le Zhang, Ce Zhu,
• Abstract summary: We introduce the Open Vocabulary Video Semantic (OV-VSS) task, designed to accurately segment every pixel across a wide range of open-vocabulary categories. To enhance OV-VSS performance, we propose a robust baseline, OV2VSS, which integrates a spatial-temporal fusion module. Our approach also includes video text encoding, which strengthens the model's capability to interpret textual information within the video context.
• Score: 40.58291642595943
• License:
• Abstract: Semantic segmentation in videos has been a focal point of recent research. However, existing models encounter challenges when faced with unfamiliar categories. To address this, we introduce the Open Vocabulary Video Semantic Segmentation (OV-VSS) task, designed to accurately segment every pixel across a wide range of open-vocabulary categories, including those that are novel or previously unexplored. To enhance OV-VSS performance, we propose a robust baseline, OV2VSS, which integrates a spatial-temporal fusion module, allowing the model to utilize temporal relationships across consecutive frames. Additionally, we incorporate a random frame enhancement module, broadening the model's understanding of semantic context throughout the entire video sequence. Our approach also includes video text encoding, which strengthens the model's capability to interpret textual information within the video context. Comprehensive evaluations on benchmark datasets such as VSPW and Cityscapes highlight OV-VSS's zero-shot generalization capabilities, especially in handling novel categories. The results validate OV2VSS's effectiveness, demonstrating improved performance in semantic segmentation tasks across diverse video datasets.

Related papers

• Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation [15.941958367737408]
  We present Seg-TTO, a framework for zero-shot, open-vocabulary semantic segmentation (OVSS) We focus on segmentation-specific test-time optimization to address this gap. We integrate Seg-TTO with three state-of-the-art OVSS approaches and evaluate across 22 challenging OVSS tasks covering a range of specialized domains.
  arXiv  Detail & Related papers  (2025-01-08T18:58:24Z)
• Rethinking Video Segmentation with Masked Video Consistency: Did the Model Learn as Intended? [22.191260650245443]
  Video segmentation aims at partitioning video sequences into meaningful segments based on objects or regions of interest within frames. Current video segmentation models are often derived from image segmentation techniques, which struggle to cope with small-scale or class-imbalanced video datasets. We propose a training strategy Masked Video Consistency, which enhances spatial and temporal feature aggregation.
  arXiv  Detail & Related papers  (2024-08-20T08:08:32Z)
• Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models [96.97910688908956]
  We introduce the first zero-shot approach for Video Semantic (VSS) based on pre-trained diffusion models. We propose a framework tailored for VSS based on pre-trained image and video diffusion models. Experiments show that our proposed approach outperforms existing zero-shot image semantic segmentation approaches.
  arXiv  Detail & Related papers  (2024-05-27T08:39:38Z)
• DVIS++: Improved Decoupled Framework for Universal Video Segmentation [30.703276476607545]
  We present OV-DVIS++, the first open-vocabulary universal video segmentation framework. By integrating CLIP with DVIS++, we present OV-DVIS++, the first open-vocabulary universal video segmentation framework.
  arXiv  Detail & Related papers  (2023-12-20T03:01:33Z)
• RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation [53.4319652364256]
  This paper presents the RefSAM model, which explores the potential of SAM for referring video object segmentation. Our proposed approach adapts the original SAM model to enhance cross-modality learning by employing a lightweight Cross-RValModal. We employ a parameter-efficient tuning strategy to align and fuse the language and vision features effectively.
  arXiv  Detail & Related papers  (2023-07-03T13:21:58Z)
• 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)
• Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation [58.74791043631219]
  We propose a novel framework STswinCL that explores the complementary intra- and inter-video relations to boost segmentation performance. We extensively validate our approach on two public surgical video benchmarks, including EndoVis18 Challenge and CaDIS dataset. Experimental results demonstrate the promising performance of our method, which consistently exceeds previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-03-29T05:52:23Z)
• BiST: Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues [95.8297116307127]
  We propose Bi-directional Spatio-Temporal Learning (BiST), a vision-language neural framework for high-resolution queries in videos. Specifically, our approach exploits both spatial and temporal-level information, and learns dynamic information diffusion between the two feature spaces. BiST achieves competitive performance and generates reasonable responses on a large-scale AVSD benchmark.
  arXiv  Detail & Related papers  (2020-10-20T07:43:00Z)
• Video Panoptic Segmentation [117.08520543864054]
  We propose and explore a new video extension of this task, called video panoptic segmentation. To invigorate research on this new task, we present two types of video panoptic datasets. We propose a novel video panoptic segmentation network (VPSNet) which jointly predicts object classes, bounding boxes, masks, instance id tracking, and semantic segmentation in video frames.
  arXiv  Detail & Related papers  (2020-06-19T19:35:47Z)

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.