Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos

• URL: http://arxiv.org/abs/2101.02196v1
• Date: Wed, 6 Jan 2021 18:56:24 GMT
• Title: Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos
• Authors: Bin Zhao, Goutam Bhat, Martin Danelljan, Luc Van Gool, Radu Timofte
• Abstract summary: We introduce a method for generating segmentation masks from per-frame bounding box annotations in videos. We use our resulting accurate masks for weakly supervised training of video object segmentation (VOS) networks. The additional data provides substantially better generalization performance leading to state-of-the-art results in both the VOS and more challenging tracking domain.
• Score: 159.02703673838639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmenting objects in videos is a fundamental computer vision task. The current deep learning based paradigm offers a powerful, but data-hungry solution. However, current datasets are limited by the cost and human effort of annotating object masks in videos. This effectively limits the performance and generalization capabilities of existing video segmentation methods. To address this issue, we explore weaker form of bounding box annotations. We introduce a method for generating segmentation masks from per-frame bounding box annotations in videos. To this end, we propose a spatio-temporal aggregation module that effectively mines consistencies in the object and background appearance across multiple frames. We use our resulting accurate masks for weakly supervised training of video object segmentation (VOS) networks. We generate segmentation masks for large scale tracking datasets, using only their bounding box annotations. The additional data provides substantially better generalization performance leading to state-of-the-art results in both the VOS and more challenging tracking domain.

Related papers

• Beyond Boxes: Mask-Guided Spatio-Temporal Feature Aggregation for Video Object Detection [12.417754433715903]
  We present FAIM, a new VOD method that enhances temporal Feature Aggregation by leveraging Instance Mask features. Using YOLOX as a base detector, FAIM achieves 87.9% mAP on the ImageNet VID dataset at 33 FPS on a single 2080Ti GPU.
  arXiv  Detail & Related papers  (2024-12-06T10:12:10Z)
• Multi-Granularity Video Object Segmentation [36.06127939037613]
  We propose a large-scale, densely annotated multi-granularity video object segmentation (MUG-VOS) dataset. We automatically collected a training set that assists in tracking both salient and non-salient objects, and we also curated a human-annotated test set for reliable evaluation. In addition, we present memory-based mask propagation model (MMPM), trained and evaluated on MUG-VOS dataset.
  arXiv  Detail & Related papers  (2024-12-02T13:17:41Z)
• 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)
• Training-Free Robust Interactive Video Object Segmentation [82.05906654403684]
  We propose a training-free prompt tracking framework for interactive video object segmentation (I-PT) We jointly adopt sparse points and boxes tracking, filtering out unstable points and capturing object-wise information. Our framework has demonstrated robust zero-shot video segmentation results on popular VOS datasets.
  arXiv  Detail & Related papers  (2024-06-08T14:25:57Z)
• Learning Referring Video Object Segmentation from Weak Annotation [78.45828085350936]
  Referring video object segmentation (RVOS) is a task that aims to segment the target object in all video frames based on a sentence describing the object. We propose a new annotation scheme that reduces the annotation effort by 8 times, while providing sufficient supervision for RVOS. Our scheme only requires a mask for the frame where the object first appears and bounding boxes for the rest of the frames.
  arXiv  Detail & Related papers  (2023-08-04T06:50:52Z)
• Robust Visual Tracking by Segmentation [103.87369380021441]
  Estimating the target extent poses a fundamental challenge in visual object tracking. We propose a segmentation-centric tracking pipeline that produces a highly accurate segmentation mask. Our tracker is able to better learn a target representation that clearly differentiates the target in the scene from background content.
  arXiv  Detail & Related papers  (2022-03-21T17:59:19Z)
• Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation [70.97625552643493]
  This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. We propose a novel graph neuralS network (TG-Net) which captures the local contexts by utilizing all proposals.
  arXiv  Detail & Related papers  (2020-12-10T07:57:44Z)
• Reducing the Annotation Effort for Video Object Segmentation Datasets [50.893073670389164]
  densely labeling every frame with pixel masks does not scale to large datasets. We use a deep convolutional network to automatically create pseudo-labels on a pixel level from much cheaper bounding box annotations. We obtain the new TAO-VOS benchmark, which we make publicly available at www.vision.rwth-aachen.de/page/taovos.
  arXiv  Detail & Related papers  (2020-11-02T17:34:45Z)

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.