3rd Place Solution for MeViS Track in CVPR 2024 PVUW workshop: Motion Expression guided Video Segmentation

• URL: http://arxiv.org/abs/2406.04842v1
• Date: Fri, 7 Jun 2024 11:15:03 GMT
• Title: 3rd Place Solution for MeViS Track in CVPR 2024 PVUW workshop: Motion Expression guided Video Segmentation
• Authors: Feiyu Pan, Hao Fang, Xiankai Lu,
• Abstract summary: We propose using frozen pre-trained vision-language models (VLM) as backbones, with a specific emphasis on enhancing cross-modal feature interaction. Firstly, we use frozen convolutional CLIP backbone to generate feature-aligned vision and text features, alleviating the issue of domain gap. Secondly, we add more cross-modal feature fusion in the pipeline to enhance the utilization of multi-modal information.
• Score: 13.622700558266658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring video object segmentation (RVOS) relies on natural language expressions to segment target objects in video, emphasizing modeling dense text-video relations. The current RVOS methods typically use independently pre-trained vision and language models as backbones, resulting in a significant domain gap between video and text. In cross-modal feature interaction, text features are only used as query initialization and do not fully utilize important information in the text. In this work, we propose using frozen pre-trained vision-language models (VLM) as backbones, with a specific emphasis on enhancing cross-modal feature interaction. Firstly, we use frozen convolutional CLIP backbone to generate feature-aligned vision and text features, alleviating the issue of domain gap and reducing training costs. Secondly, we add more cross-modal feature fusion in the pipeline to enhance the utilization of multi-modal information. Furthermore, we propose a novel video query initialization method to generate higher quality video queries. Without bells and whistles, our method achieved 51.5 J&F on the MeViS test set and ranked 3rd place for MeViS Track in CVPR 2024 PVUW workshop: Motion Expression guided Video Segmentation.

Related papers

• VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding [63.075626670943116]
  We introduce a cutting-edge framework, VaQuitA, designed to refine the synergy between video and textual information. At the data level, instead of sampling frames uniformly, we implement a sampling method guided by CLIP-score rankings. At the feature level, we integrate a trainable Video Perceiver alongside a Visual-Query Transformer.
  arXiv  Detail & Related papers  (2023-12-04T19:48:02Z)
• Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training [70.83385449872495]
  The correlation between the vision and text is essential for video moment retrieval (VMR) Existing methods rely on separate pre-training feature extractors for visual and textual understanding. We propose a generic method, referred to as Visual-Dynamic Injection (VDI), to empower the model's understanding of video moments.
  arXiv  Detail & Related papers  (2023-02-28T19:29:05Z)
• STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training [30.16501510589718]
  We propose a pre-training framework that jointly models object and action information across spatial and temporal dimensions. We design two auxiliary tasks to better incorporate both kinds of information into the pre-training process of the video-language model.
  arXiv  Detail & Related papers  (2023-02-20T03:13:45Z)
• Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation [56.41614987789537]
  Text-based video segmentation aims to segment the target object in a video based on a describing sentence. We propose a method to fuse and align appearance, motion, and linguistic features to achieve accurate segmentation.
  arXiv  Detail & Related papers  (2022-04-06T02:42:33Z)
• Align and Prompt: Video-and-Language Pre-training with Entity Prompts [111.23364631136339]
  Video-and-language pre-training has shown promising improvements on various downstream tasks. We propose Align and Prompt: an efficient and effective video-and-language pre-training framework with better cross-modal alignment. Our code and pre-trained models will be released.
  arXiv  Detail & Related papers  (2021-12-17T15:55:53Z)
• Video-Text Pre-training with Learned Regions [59.30893505895156]
  Video-Text pre-training aims at learning transferable representations from large-scale video-text pairs. We propose a module for videotext-learning, RegionLearner, which can take into account the structure of objects during pre-training on large-scale video-text pairs.
  arXiv  Detail & Related papers  (2021-12-02T13:06:53Z)
• Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation [140.4291169276062]
  Referring video object segmentation (RVOS) aims to segment video objects with the guidance of natural language reference. Previous methods typically tackle RVOS through directly grounding linguistic reference over the image lattice. In this work, we put forward a two-stage, top-down RVOS solution. First, an exhaustive set of object tracklets is constructed by propagating object masks detected from several sampled frames to the entire video. Second, a Transformer-based tracklet-language grounding module is proposed, which models instance-level visual relations and cross-modal interactions simultaneously and efficiently.
  arXiv  Detail & Related papers  (2021-06-02T10:26:13Z)
• Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network [27.792054915363106]
  Cross-modal self-attention (CMSA) module to utilize fine details of individual words and the input image or video. gated multi-level fusion (GMLF) module to selectively integrate self-attentive cross-modal features. Cross-frame self-attention (CFSA) module to effectively integrate temporal information in consecutive frames.
  arXiv  Detail & Related papers  (2021-02-09T11:27:59Z)

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.