Boosting Open-Vocabulary Object Detection by Handling Background Samples

• URL: http://arxiv.org/abs/2410.08645v1
• Date: Fri, 11 Oct 2024 09:15:50 GMT
• Title: Boosting Open-Vocabulary Object Detection by Handling Background Samples
• Authors: Ruizhe Zeng, Lu Zhang, Xu Yang, Zhiyong Liu,
• Abstract summary: We propose a novel approach to address the limitations of CLIP in handling background samples. We introduce Partial Object Suppression (POS) to address the issue of misclassifying partial regions as foreground. Our proposed model is capable of achieving performance enhancements across various open-vocabulary detectors.
• Score: 9.07525578809556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-vocabulary object detection is the task of accurately detecting objects from a candidate vocabulary list that includes both base and novel categories. Currently, numerous open-vocabulary detectors have achieved success by leveraging the impressive zero-shot capabilities of CLIP. However, we observe that CLIP models struggle to effectively handle background images (i.e. images without corresponding labels) due to their language-image learning methodology. This limitation results in suboptimal performance for open-vocabulary detectors that rely on CLIP when processing background samples. In this paper, we propose Background Information Representation for open-vocabulary Detector (BIRDet), a novel approach to address the limitations of CLIP in handling background samples. Specifically, we design Background Information Modeling (BIM) to replace the single, fixed background embedding in mainstream open-vocabulary detectors with dynamic scene information, and prompt it into image-related background representations. This method effectively enhances the ability to classify oversized regions as background. Besides, we introduce Partial Object Suppression (POS), an algorithm that utilizes the ratio of overlap area to address the issue of misclassifying partial regions as foreground. Experiments on OV-COCO and OV-LVIS benchmarks demonstrate that our proposed model is capable of achieving performance enhancements across various open-vocabulary detectors.

Related papers

• Fine-Grained Open-Vocabulary Object Recognition via User-Guided Segmentation [1.590984668118904]
  FOCUS: Finegrained Open-Vocabulary Object ReCognition via User-Guided. We propose a novel foundation model-based detection method called FOCUS: Finegrained Open-Vocabulary Object ReCognition via User-Guided.
  arXiv  Detail & Related papers  (2024-11-23T18:13:27Z)
• MROVSeg: Breaking the Resolution Curse of Vision-Language Models in Open-Vocabulary Semantic Segmentation [33.67313662538398]
  We propose a multi-resolution training framework for open-vocabulary semantic segmentation with a single pretrained CLIP backbone. MROVSeg uses sliding windows to slice the high-resolution input into uniform patches, each matching the input size of the well-trained image encoder. We demonstrate the superiority of MROVSeg on well-established open-vocabulary semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2024-08-27T04:45:53Z)
• 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)
• LOGO: Video Text Spotting with Language Collaboration and Glyph Perception Model [20.007650672107566]
  Video text spotting (VTS) aims to simultaneously localize, recognize and track text instances in videos. Recent methods track the zero-shot results of state-of-the-art image text spotters directly. Fine-tuning transformer-based text spotters on specific datasets could yield performance enhancements.
  arXiv  Detail & Related papers  (2024-05-29T15:35:09Z)
• DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection [111.68263493302499]
  We introduce DetCLIPv3, a high-performing detector that excels at both open-vocabulary object detection and hierarchical labels. DetCLIPv3 is characterized by three core designs: 1) Versatile model architecture; 2) High information density data; and 3) Efficient training strategy. DetCLIPv3 demonstrates superior open-vocabulary detection performance, outperforming GLIPv2, GroundingDINO, and DetCLIPv2 by 18.0/19.6/6.6 AP, respectively.
  arXiv  Detail & Related papers  (2024-04-14T11:01:44Z)
• Open-Vocabulary Camouflaged Object Segmentation [66.94945066779988]
  We introduce a new task, open-vocabulary camouflaged object segmentation (OVCOS) We construct a large-scale complex scene dataset (textbfOVCamo) containing 11,483 hand-selected images with fine annotations and corresponding object classes. By integrating the guidance of class semantic knowledge and the supplement of visual structure cues from the edge and depth information, the proposed method can efficiently capture camouflaged objects.
  arXiv  Detail & Related papers  (2023-11-19T06:00:39Z)
• DisCLIP: Open-Vocabulary Referring Expression Generation [37.789850573203694]
  We build on CLIP, a large-scale visual-semantic model, to guide an LLM to generate a contextual description of a target concept in an image. We measure the quality of the generated text by evaluating the capability of a receiver model to accurately identify the described object within the scene. Our results highlight the potential of using pre-trained visual-semantic models for generating high-quality contextual descriptions.
  arXiv  Detail & Related papers  (2023-05-30T15:13:17Z)
• Fine-grained Visual-Text Prompt-Driven Self-Training for Open-Vocabulary Object Detection [87.39089806069707]
  We propose a fine-grained Visual-Text Prompt-driven self-training paradigm for Open-Vocabulary Detection (VTP-OVD) During the adapting stage, we enable VLM to obtain fine-grained alignment by using learnable text prompts to resolve an auxiliary dense pixel-wise prediction task. Experiments show that our method achieves the state-of-the-art performance for open-vocabulary object detection, e.g., 31.5% mAP on unseen classes of COCO.
  arXiv  Detail & Related papers  (2022-11-02T03:38:02Z)
• Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection [54.96069171726668]
  Two popular forms of weak-supervision used in open-vocabulary detection (OVD) include pretrained CLIP model and image-level supervision. We propose to address this problem by performing object-centric alignment of the language embeddings from the CLIP model. We establish a bridge between the above two object-alignment strategies via a novel weight transfer function.
  arXiv  Detail & Related papers  (2022-07-07T17:59:56Z)
• Open-Vocabulary DETR with Conditional Matching [86.1530128487077]
  OV-DETR is an open-vocabulary detector based on DETR. It can detect any object given its class name or an exemplar image. It achieves non-trivial improvements over current state of the arts.
  arXiv  Detail & Related papers  (2022-03-22T16:54:52Z)

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.