A Hierarchical Semantic Distillation Framework for Open-Vocabulary Object Detection

• URL: http://arxiv.org/abs/2503.10152v1
• Date: Thu, 13 Mar 2025 08:27:18 GMT
• Title: A Hierarchical Semantic Distillation Framework for Open-Vocabulary Object Detection
• Authors: Shenghao Fu, Junkai Yan, Qize Yang, Xihan Wei, Xiaohua Xie, Wei-Shi Zheng,
• Abstract summary: We propose a hierarchical semantic distillation framework named HD-OVD to construct a comprehensive distillation process.<n>Our HD-OVD inherits generalizable recognition ability from CLIP in instance, class, and image levels.<n>We boost the novel AP on the OV-COCO dataset to 46.4% with a ResNet50 backbone, which outperforms others by a clear margin.
• Score: 47.18069715855738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-vocabulary object detection (OVD) aims to detect objects beyond the training annotations, where detectors are usually aligned to a pre-trained vision-language model, eg, CLIP, to inherit its generalizable recognition ability so that detectors can recognize new or novel objects. However, previous works directly align the feature space with CLIP and fail to learn the semantic knowledge effectively. In this work, we propose a hierarchical semantic distillation framework named HD-OVD to construct a comprehensive distillation process, which exploits generalizable knowledge from the CLIP model in three aspects. In the first hierarchy of HD-OVD, the detector learns fine-grained instance-wise semantics from the CLIP image encoder by modeling relations among single objects in the visual space. Besides, we introduce text space novel-class-aware classification to help the detector assimilate the highly generalizable class-wise semantics from the CLIP text encoder, representing the second hierarchy. Lastly, abundant image-wise semantics containing multi-object and their contexts are also distilled by an image-wise contrastive distillation. Benefiting from the elaborated semantic distillation in triple hierarchies, our HD-OVD inherits generalizable recognition ability from CLIP in instance, class, and image levels. Thus, we boost the novel AP on the OV-COCO dataset to 46.4% with a ResNet50 backbone, which outperforms others by a clear margin. We also conduct extensive ablation studies to analyze how each component works.

Related papers

• Hierarchical Cross-Modal Alignment for Open-Vocabulary 3D Object Detection [45.68105299990119]
  Open-vocabulary 3D object detection (OV-3DOD) aims at localizing and classifying novel objects beyond closed sets. We propose a hierarchical framework, named HCMA, to simultaneously learn local object and global scene information for OV-3DOD.
  arXiv  Detail & Related papers  (2025-03-10T17:55:22Z)
• 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)
• Domain Adaptation for Large-Vocabulary Object Detectors [103.16365373806829]
  This paper presents KGD, a Knowledge Graph Distillation technique that exploits the implicit knowledge graphs (KG) in CLIP for effectively adapting LVDs to various downstream domains. Experiments over multiple widely adopted detection benchmarks show that KGD outperforms the state-of-the-art consistently by large margins.
  arXiv  Detail & Related papers  (2024-01-13T03:51:18Z)
• Simple Image-level Classification Improves Open-vocabulary Object Detection [27.131298903486474]
  Open-Vocabulary Object Detection (OVOD) aims to detect novel objects beyond a given set of base categories on which the detection model is trained. Recent OVOD methods focus on adapting the image-level pre-trained vision-language models (VLMs), such as CLIP, to a region-level object detection task via, eg., region-level knowledge distillation, regional prompt learning, or region-text pre-training. These methods have demonstrated remarkable performance in recognizing regional visual concepts, but they are weak in exploiting the VLMs' powerful global scene understanding ability learned from the billion-scale
  arXiv  Detail & Related papers  (2023-12-16T13:06:15Z)
• Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning [13.667326007851674]
  We propose CastDet, a CLIP-activated student-teacher open-vocabulary object detection framework. Our approach boosts not only novel object proposals but also classification. Experimental results demonstrate our CastDet achieving superior open-vocabulary detection performance.
  arXiv  Detail & Related papers  (2023-11-20T10:26:04Z)
• 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)
• Multi-Modal Classifiers for Open-Vocabulary Object Detection [104.77331131447541]
  The goal of this paper is open-vocabulary object detection (OVOD) We adopt a standard two-stage object detector architecture. We explore three ways via: language descriptions, image exemplars, or a combination of the two.
  arXiv  Detail & Related papers  (2023-06-08T18:31:56Z)
• HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models [30.279621764192843]
  Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors. We propose a novel HOI detection framework that efficiently extracts prior knowledge from CLIP and achieves better generalization.
  arXiv  Detail & Related papers  (2023-03-28T07:54:54Z)
• 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)

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.