UN-DETR: Promoting Objectness Learning via Joint Supervision for Unknown Object Detection

• URL: http://arxiv.org/abs/2412.10176v1
• Date: Fri, 13 Dec 2024 14:45:11 GMT
• Title: UN-DETR: Promoting Objectness Learning via Joint Supervision for Unknown Object Detection
• Authors: Haomiao Liu, Hao Xu, Chuhuai Yue, Bo Ma,
• Abstract summary: Unknown Object Detection (UOD) aims to identify objects of unseen categories, differing from the traditional detection paradigm limited by the closed-world assumption. Previous methods obtain supervision signals for learning objectness in isolation from either localization or classification information, leading to poor performance for UOD. We propose a transformer-based UOD framework, UN-DETR. Based on this, we craft Instance Presence Score (IPS) to represent the probability of an object's presence.
• Score: 6.837147490662261
• License:
• Abstract: Unknown Object Detection (UOD) aims to identify objects of unseen categories, differing from the traditional detection paradigm limited by the closed-world assumption. A key component of UOD is learning a generalized representation, i.e. objectness for both known and unknown categories to distinguish and localize objects from the background in a class-agnostic manner. However, previous methods obtain supervision signals for learning objectness in isolation from either localization or classification information, leading to poor performance for UOD. To address this issue, we propose a transformer-based UOD framework, UN-DETR. Based on this, we craft Instance Presence Score (IPS) to represent the probability of an object's presence. For the purpose of information complementarity, IPS employs a strategy of joint supervised learning, integrating attributes representing general objectness from the positional and the categorical latent space as supervision signals. To enhance IPS learning, we introduce a one-to-many assignment strategy to incorporate more supervision. Then, we propose Unbiased Query Selection to provide premium initial query vectors for the decoder. Additionally, we propose an IPS-guided post-process strategy to filter redundant boxes and correct classification predictions for known and unknown objects. Finally, we pretrain the entire UN-DETR in an unsupervised manner, in order to obtain objectness prior. Our UN-DETR is comprehensively evaluated on multiple UOD and known detection benchmarks, demonstrating its effectiveness and achieving state-of-the-art performance.

Related papers

• Open-Set Object Detection By Aligning Known Class Representations [24.708230848232432]
  Open-Set Object Detection (OSOD) has emerged as a contemporary research direction to address the detection of unknown objects. We propose a new semantic clustering-based approach to facilitate a meaningful alignment of clusters in semantic space. Our approach further incorporates an object focus module to predict objectness scores, which enhances the detection of unknown objects.
  arXiv  Detail & Related papers  (2024-12-30T04:26:56Z)
• Semi-supervised Open-World Object Detection [74.95267079505145]
  We introduce a more realistic formulation, named semi-supervised open-world detection (SS-OWOD) We demonstrate that the performance of the state-of-the-art OWOD detector dramatically deteriorates in the proposed SS-OWOD setting. Our experiments on 4 datasets including MS COCO, PASCAL, Objects365 and DOTA demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-02-25T07:12:51Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Open-World Object Detection via Discriminative Class Prototype Learning [4.055884768256164]
  Open-world object detection (OWOD) is a challenging problem that combines object detection with incremental learning and open-set learning. We propose a novel and efficient OWOD solution from a prototype perspective, which we call OCPL: Open-world object detection via discnative OCPL: Open-world object detection via discriminative OCPL: Open-world object detection via discriminative OCPL: Open-world object detection via discriminative OCPL: Open-world object detection via discriminative OCPL: Open-world object detection via discriminative OCPL: Open-world object detection via
  arXiv  Detail & Related papers  (2023-02-23T03:05:04Z)
• CLIP the Gap: A Single Domain Generalization Approach for Object Detection [60.20931827772482]
  Single Domain Generalization tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. We propose to leverage a pre-trained vision-language model to introduce semantic domain concepts via textual prompts. We achieve this via a semantic augmentation strategy acting on the features extracted by the detector backbone, as well as a text-based classification loss.
  arXiv  Detail & Related papers  (2023-01-13T12:01:18Z)
• Open World DETR: Transformer based Open World Object Detection [60.64535309016623]
  We propose a two-stage training approach named Open World DETR for open world object detection based on Deformable DETR. We fine-tune the class-specific components of the model with a multi-view self-labeling strategy and a consistency constraint. Our proposed method outperforms other state-of-the-art open world object detection methods by a large margin.
  arXiv  Detail & Related papers  (2022-12-06T13:39:30Z)
• PROB: Probabilistic Objectness for Open World Object Detection [15.574535196804042]
  Open World Object Detection (OWOD) is a new computer vision task that bridges the gap between classic object detection (OD) benchmarks and object detection in the real world. We introduce a novel probabilistic framework for objectness estimation, where we alternate between probability distribution estimation and objectness likelihood of known objects. The resulting Probabilistic Objectness transformer-based open-world detector, PROB, integrates our framework into traditional object detection models.
  arXiv  Detail & Related papers  (2022-12-02T20:04:24Z)
• Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning [25.935629339091697]
  Open-set object detection (OSOD) is a promising direction to handle the problem consisting of two subtasks: objects and background separation, and open-set object classification. We present Openset RCNN to address the challenging OSOD. We show that our Openset RCNN can endow the robot with an open-set perception ability to support robotic rearrangement tasks in cluttered environments.
  arXiv  Detail & Related papers  (2022-11-21T15:00:04Z)
• OW-DETR: Open-world Detection Transformer [90.56239673123804]
  We introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. OW-DETR comprises three dedicated components namely, attention-driven pseudo-labeling, novelty classification and objectness scoring. Our model outperforms the recently introduced OWOD approach, ORE, with absolute gains ranging from 1.8% to 3.3% in terms of unknown recall.
  arXiv  Detail & Related papers  (2021-12-02T18:58:30Z)
• Learning Open-World Object Proposals without Learning to Classify [110.30191531975804]
  We propose a classification-free Object Localization Network (OLN) which estimates the objectness of each region purely by how well the location and shape of a region overlaps with any ground-truth object. This simple strategy learns generalizable objectness and outperforms existing proposals on cross-category generalization.
  arXiv  Detail & Related papers  (2021-08-15T14:36:02Z)

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.