MOD-UV: Learning Mobile Object Detectors from Unlabeled Videos

• URL: http://arxiv.org/abs/2405.14841v3
• Date: Wed, 31 Jul 2024 04:26:43 GMT
• Title: MOD-UV: Learning Mobile Object Detectors from Unlabeled Videos
• Authors: Yihong Sun, Bharath Hariharan,
• Abstract summary: Embodied agents must detect and localize objects of interest, e.g. traffic participants for self-driving cars. We propose MOD-UV, a Mobile Object Detector learned from Unlabeled Videos. We achieve state-of-the-art performance in unsupervised mobile object detection on Open, nuScenes, and KITTI datasets.
• Score: 34.24947555223083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embodied agents must detect and localize objects of interest, e.g. traffic participants for self-driving cars. Supervision in the form of bounding boxes for this task is extremely expensive. As such, prior work has looked at unsupervised instance detection and segmentation, but in the absence of annotated boxes, it is unclear how pixels must be grouped into objects and which objects are of interest. This results in over-/under-segmentation and irrelevant objects. Inspired by human visual system and practical applications, we posit that the key missing cue for unsupervised detection is motion: objects of interest are typically mobile objects that frequently move and their motions can specify separate instances. In this paper, we propose MOD-UV, a Mobile Object Detector learned from Unlabeled Videos only. We begin with instance pseudo-labels derived from motion segmentation, but introduce a novel training paradigm to progressively discover small objects and static-but-mobile objects that are missed by motion segmentation. As a result, though only learned from unlabeled videos, MOD-UV can detect and segment mobile objects from a single static image. Empirically, we achieve state-of-the-art performance in unsupervised mobile object detection on Waymo Open, nuScenes, and KITTI Datasets without using any external data or supervised models. Code is available at https://github.com/YihongSun/MOD-UV.

Related papers

• VOVTrack: Exploring the Potentiality in Videos for Open-Vocabulary Object Tracking [61.56592503861093]
  This issue amalgamates the complexities of open-vocabulary object detection (OVD) and multi-object tracking (MOT) Existing approaches to OVMOT often merge OVD and MOT methodologies as separate modules, predominantly focusing on the problem through an image-centric lens. We propose VOVTrack, a novel method that integrates object states relevant to MOT and video-centric training to address this challenge from a video object tracking standpoint.
  arXiv  Detail & Related papers  (2024-10-11T05:01:49Z)
• Object-Centric Multiple Object Tracking [124.30650395969126]
  This paper proposes a video object-centric model for multiple-object tracking pipelines. It consists of an index-merge module that adapts the object-centric slots into detection outputs and an object memory module. Benefited from object-centric learning, we only require sparse detection labels for object localization and feature binding.
  arXiv  Detail & Related papers  (2023-09-01T03:34:12Z)
• InstMove: Instance Motion for Object-centric Video Segmentation [70.16915119724757]
  In this work, we study the instance-level motion and present InstMove, which stands for Instance Motion for Object-centric Video. In comparison to pixel-wise motion, InstMove mainly relies on instance-level motion information that is free from image feature embeddings. With only a few lines of code, InstMove can be integrated into current SOTA methods for three different video segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-14T17:58:44Z)
• Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns [92.80981308407098]
  We propose a new approach to learn to segment multiple image objects without manual supervision. The method can extract objects form still images, but uses videos for supervision. We show state-of-the-art unsupervised object segmentation performance on simulated and real-world benchmarks.
  arXiv  Detail & Related papers  (2022-10-21T17:57:05Z)
• MOVE: Unsupervised Movable Object Segmentation and Detection [32.73565093619594]
  MOVE is a method to segment objects without any form of supervision. It exploits the fact that foreground objects can be shifted locally relative to their initial position. It gives an average CorLoc improvement of 7.2% over the SotA.
  arXiv  Detail & Related papers  (2022-10-14T16:05:46Z)
• Discovering Objects that Can Move [55.743225595012966]
  We study the problem of object discovery -- separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. We choose to focus on dynamic objects -- entities that can move independently in the world.
  arXiv  Detail & Related papers  (2022-03-18T21:13:56Z)
• 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.