Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation

• URL: http://arxiv.org/abs/2106.01061v2
• Date: Fri, 19 Jan 2024 13:44:46 GMT
• Title: Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation
• Authors: Chen Liang, Yu Wu, Tianfei Zhou, Wenguan Wang, Zongxin Yang, Yunchao Wei and Yi Yang
• Abstract summary: 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.
• Score: 140.4291169276062
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Such bottom-up strategy fails to explore object-level cues, easily leading to inferior results. In this work, we instead 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. Our model ranks first place on CVPR2021 Referring Youtube-VOS challenge.

Related papers

• 3rd Place Solution for MeViS Track in CVPR 2024 PVUW workshop: Motion Expression guided Video Segmentation [13.622700558266658]
  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.
  arXiv  Detail & Related papers  (2024-06-07T11:15:03Z)
• 1st Place Solution for 5th LSVOS Challenge: Referring Video Object Segmentation [65.45702890457046]
  We integrate strengths of leading RVOS models to build up an effective paradigm. To improve the consistency and quality of masks, we propose Two-Stage Multi-Model Fusion strategy. Our method achieves 75.7% J&F on Ref-Youtube-VOS validation set and 70% J&F on test set, which ranks 1st place on 5th Large-scale Video Object Challenge (ICCV 2023) track 3.
  arXiv  Detail & Related papers  (2024-01-01T04:24:48Z)
• Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Models [28.304047711166056]
  Large-scale pre-trained models have shown promising advances in detecting and segmenting objects in 2D static images in the wild. This begs the question: can we re-purpose these large-scale pre-trained static image models for open-vocabulary video tracking? In this paper, we re-purpose an open-vocabulary detector, segmenter, and dense optical flow estimator, into a model that tracks and segments objects of any category in 2D videos.
  arXiv  Detail & Related papers  (2023-10-10T20:25:30Z)
• MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions [93.35942025232943]
  We propose a large-scale dataset called MeViS, which contains numerous motion expressions to indicate target objects in complex environments. The goal of our benchmark is to provide a platform that enables the development of effective language-guided video segmentation algorithms.
  arXiv  Detail & Related papers  (2023-08-16T17:58:34Z)
• Segmenting Moving Objects via an Object-Centric Layered Representation [100.26138772664811]
  We introduce an object-centric segmentation model with a depth-ordered layer representation. We introduce a scalable pipeline for generating synthetic training data with multiple objects. We evaluate the model on standard video segmentation benchmarks.
  arXiv  Detail & Related papers  (2022-07-05T17:59:43Z)
• Towards Robust Video Object Segmentation with Adaptive Object Calibration [18.094698623128146]
  Video object segmentation (VOS) aims at segmenting objects in all target frames of a video, given annotated object masks of reference frames. We propose a new deep network, which can adaptively construct object representations and calibrate object masks to achieve stronger robustness. Our model achieves the state-of-the-art performance among existing published works, and also exhibits superior robustness against perturbations.
  arXiv  Detail & Related papers  (2022-07-02T17:51:29Z)
• The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation [18.630453674396534]
  ReferFormer aims to segment object instances in a given video referred by a language expression in all video frames. This work proposes several tricks to boost further, including cyclical learning rates, semi-supervised approach, and test-time augmentation inference. The improved ReferFormer ranks 2nd place on CVPR2022 Referring Youtube-VOS Challenge.
  arXiv  Detail & Related papers  (2022-06-24T02:15:06Z)
• Locate then Segment: A Strong Pipeline for Referring Image Segmentation [73.19139431806853]
  Referring image segmentation aims to segment the objects referred by a natural language expression. Previous methods usually focus on designing an implicit and recurrent interaction mechanism to fuse the visual-linguistic features to directly generate the final segmentation mask. We present a "Then-Then-Segment" scheme to tackle these problems. Our framework is simple but surprisingly effective.
  arXiv  Detail & Related papers  (2021-03-30T12:25:27Z)
• DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping [72.84991726271024]
  We describe an unsupervised method to detect and segment portions of images of live scenes that are seen moving as a coherent whole. Our method first partitions the motion field by minimizing the mutual information between segments. It uses the segments to learn object models that can be used for detection in a static image.
  arXiv  Detail & Related papers  (2020-08-16T22:05:13Z)

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.