Leveraging Unknown Objects to Construct Labeled-Unlabeled Meta-Relationships for Zero-Shot Object Navigation

• URL: http://arxiv.org/abs/2405.15222v2
• Date: Mon, 27 May 2024 02:39:39 GMT
• Title: Leveraging Unknown Objects to Construct Labeled-Unlabeled Meta-Relationships for Zero-Shot Object Navigation
• Authors: Yanwei Zheng, Changrui Li, Chuanlin Lan, Yaling Li, Xiao Zhang, Yifei Zou, Dongxiao Yu, Zhipeng Cai,
• Abstract summary: Zero-shot object navigation (ZSON) addresses situation where an agent navigates to an unseen object that does not present in the training set. We introduce seen objects without labels into training procedure to enrich the agent's knowledge base with distinguishable but previously overlooked information.
• Score: 14.336117107170153
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot object navigation (ZSON) addresses situation where an agent navigates to an unseen object that does not present in the training set. Previous works mainly train agent using seen objects with known labels, and ignore the seen objects without labels. In this paper, we introduce seen objects without labels, herein termed as ``unknown objects'', into training procedure to enrich the agent's knowledge base with distinguishable but previously overlooked information. Furthermore, we propose the label-wise meta-correlation module (LWMCM) to harness relationships among objects with and without labels, and obtain enhanced objects information. Specially, we propose target feature generator (TFG) to generate the features representation of the unlabeled target objects. Subsequently, the unlabeled object identifier (UOI) module assesses whether the unlabeled target object appears in the current observation frame captured by the camera and produces an adapted target features representation specific to the observed context. In meta contrastive feature modifier (MCFM), the target features is modified via approaching the features of objects within the observation frame while distancing itself from features of unobserved objects. Finally, the meta object-graph learner (MOGL) module is utilized to calculate the relationships among objects based on the features. Experiments conducted on AI2THOR and RoboTHOR platforms demonstrate the effectiveness of our proposed method.

Related papers

• 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)
• Referring Camouflaged Object Detection [97.90911862979355]
  Ref-COD aims to segment specified camouflaged objects based on a small set of referring images with salient target objects. We first assemble a large-scale dataset, called R2C7K, which consists of 7K images covering 64 object categories in real-world scenarios.
  arXiv  Detail & Related papers  (2023-06-13T04:15:37Z)
• Position-Aware Contrastive Alignment for Referring Image Segmentation [65.16214741785633]
  We present a position-aware contrastive alignment network (PCAN) to enhance the alignment of multi-modal features. Our PCAN consists of two modules: 1) Position Aware Module (PAM), which provides position information of all objects related to natural language descriptions, and 2) Contrastive Language Understanding Module (CLUM), which enhances multi-modal alignment.
  arXiv  Detail & Related papers  (2022-12-27T09:13:19Z)
• 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)
• Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation [66.87777732230884]
  We propose a saliency guided Inter- and Intra-Class Relation Constrained (I$2$CRC) framework to assist the expansion of the activated object regions. We also introduce an object guided label refinement module to take a full use of both the segmentation prediction and the initial labels for obtaining superior pseudo-labels.
  arXiv  Detail & Related papers  (2022-06-20T03:40:56Z)
• Synthesizing the Unseen for Zero-shot Object Detection [72.38031440014463]
  We propose to synthesize visual features for unseen classes, so that the model learns both seen and unseen objects in the visual domain. We use a novel generative model that uses class-semantics to not only generate the features but also to discriminatively separate them.
  arXiv  Detail & Related papers  (2020-10-19T12:36:11Z)
• ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects [119.46959413000594]
  This document summarizes the consensus recommendations of a working group on ObjectNav. We make recommendations on subtle but important details of evaluation criteria. We provide a detailed description of the instantiation of these recommendations in challenges organized at the Embodied AI workshop at CVPR 2020.
  arXiv  Detail & Related papers  (2020-06-23T17:18:54Z)
• A Deep Learning Approach to Object Affordance Segmentation [31.221897360610114]
  We design an autoencoder that infers pixel-wise affordance labels in both videos and static images. Our model surpasses the need for object labels and bounding boxes by using a soft-attention mechanism. We show that our model achieves competitive results compared to strongly supervised methods on SOR3D-AFF.
  arXiv  Detail & Related papers  (2020-04-18T15:34:41Z)

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.