Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects

• URL: http://arxiv.org/abs/2303.13769v3
• Date: Thu, 20 Apr 2023 01:19:34 GMT
• Title: Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects
• Authors: Wenteng Liang, Feng Xue, Yihao Liu, Guofeng Zhong, Anlong Ming
• Abstract summary: We propose the unknown sniffer (UnSniffer) to find both unknown and known objects. GOC score is introduced, which only uses known samples for supervision and avoids improper suppression of unknowns in the background. We present the Unknown Object Detection Benchmark, the first publicly benchmark that encompasses precision evaluation for unknown detection to our knowledge.
• Score: 21.426594215463105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recently proposed open-world object and open-set detection have achieved a breakthrough in finding never-seen-before objects and distinguishing them from known ones. However, their studies on knowledge transfer from known classes to unknown ones are not deep enough, resulting in the scanty capability for detecting unknowns hidden in the background. In this paper, we propose the unknown sniffer (UnSniffer) to find both unknown and known objects. Firstly, the generalized object confidence (GOC) score is introduced, which only uses known samples for supervision and avoids improper suppression of unknowns in the background. Significantly, such confidence score learned from known objects can be generalized to unknown ones. Additionally, we propose a negative energy suppression loss to further suppress the non-object samples in the background. Next, the best box of each unknown is hard to obtain during inference due to lacking their semantic information in training. To solve this issue, we introduce a graph-based determination scheme to replace hand-designed non-maximum suppression (NMS) post-processing. Finally, we present the Unknown Object Detection Benchmark, the first publicly benchmark that encompasses precision evaluation for unknown detection to our knowledge. Experiments show that our method is far better than the existing state-of-the-art methods.

Related papers

• Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection [101.15777242546649]
  Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection. We present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge.
  arXiv  Detail & Related papers  (2024-06-01T17:32:26Z)
• Road Obstacle Detection based on Unknown Objectness Scores [0.0]
  Anomaly-detection techniques make it possible to identify pixels of unknown objects as out-of-distribution (OoD) samples. The purpose of this study is to achieve stable performance for detecting unknown objects by incorporating the object-detection fashions into the pixel-wise anomaly detection methods.
  arXiv  Detail & Related papers  (2024-03-27T02:35:36Z)
• 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)
• Unsupervised Recognition of Unknown Objects for Open-World Object Detection [28.787586991713535]
  Open-World Object Detection (OWOD) extends object detection problem to a realistic and dynamic scenario. Current OWOD models, such as ORE and OW-DETR, focus on pseudo-labeling regions with high objectness scores as unknowns. This paper proposes a novel approach that learns an unsupervised discriminative model to recognize true unknown objects.
  arXiv  Detail & Related papers  (2023-08-31T08:17:29Z)
• 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)
• Detecting the unknown in Object Detection [20.84221126313118]
  We propose a novel training strategy, called UNKAD, able to predict unknown objects without requiring any annotation. UNKAD first identifies and pseudo-labels unknown objects and then uses the pseudo-annotations to train an additional unknown class. While UNKAD can directly detect unknown objects, we further combine it with previous unknown detection techniques, showing that it improves their performance at no costs.
  arXiv  Detail & Related papers  (2022-08-24T16:27:38Z)
• More Practical Scenario of Open-set Object Detection: Open at Category Level and Closed at Super-category Level [23.98839374194848]
  Open-set object detection (OSOD) has recently attracted considerable attention. We first point out that the scenario of OSOD considered in recent studies, which considers an unlimited variety of unknown objects, has a fundamental issue. This issue leads to difficulty with the evaluation of methods' performance on unknown object detection.
  arXiv  Detail & Related papers  (2022-07-20T09:28:51Z)
• Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild [29.155394550694197]
  We propose a new unknown-aware object detection framework through Spatial-Temporal Unknown Distillation (STUD) STUD distills unknown objects from videos in the wild and meaningfully regularizes the model's decision boundary. We establish the state-of-the-art performance on OOD detection tasks for object detection, reducing the FPR95 score by over 10% compared to the previous best method.
  arXiv  Detail & Related papers  (2022-03-08T01:44:03Z)
• The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods [86.39044549664189]
  Anomaly detection algorithms for feature-vector data identify anomalies as outliers, but outlier detection has not worked well in deep learning. This paper proposes the Familiarity Hypothesis that these methods succeed because they are detecting the absence of familiar learned features rather than the presence of novelty. The paper concludes with a discussion of whether familiarity detection is an inevitable consequence of representation learning.
  arXiv  Detail & Related papers  (2022-03-04T18:32:58Z)
• 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)
• Towards Open World Object Detection [68.79678648726416]
  ORE: Open World Object Detector is based on contrastive clustering and energy based unknown identification. We find that identifying and characterizing unknown instances helps to reduce confusion in an incremental object detection setting.
  arXiv  Detail & Related papers  (2021-03-03T18:58:18Z)

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.