Enhancing Open-Vocabulary Object Detection through Multi-Level Fine-Grained Visual-Language Alignment

• URL: http://arxiv.org/abs/2602.00531v1
• Date: Sat, 31 Jan 2026 05:51:45 GMT
• Title: Enhancing Open-Vocabulary Object Detection through Multi-Level Fine-Grained Visual-Language Alignment
• Authors: Tianyi Zhang, Antoine Simoulin, Kai Li, Sana Lakdawala, Shiqing Yu, Arpit Mittal, Hongyu Fu, Yu Lin,
• Abstract summary: Open-vocabulary object detection (OVD) enables the identification of objects from novel classes not present in the training set.<n>Recent advances in visual-language modeling have led to significant progress of OVD.<n>We propose Visual-Language Detection (VLDet), a novel framework that revamps feature pyramid for fine-grained visual-language alignment.
• Score: 14.066865082355696
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional object detection systems are typically constrained to predefined categories, limiting their applicability in dynamic environments. In contrast, open-vocabulary object detection (OVD) enables the identification of objects from novel classes not present in the training set. Recent advances in visual-language modeling have led to significant progress of OVD. However, prior works face challenges in either adapting the single-scale image backbone from CLIP to the detection framework or ensuring robust visual-language alignment. We propose Visual-Language Detection (VLDet), a novel framework that revamps feature pyramid for fine-grained visual-language alignment, leading to improved OVD performance. With the VL-PUB module, VLDet effectively exploits the visual-language knowledge from CLIP and adapts the backbone for object detection through feature pyramid. In addition, we introduce the SigRPN block, which incorporates a sigmoid-based anchor-text contrastive alignment loss to improve detection of novel categories. Through extensive experiments, our approach achieves 58.7 AP for novel classes on COCO2017 and 24.8 AP on LVIS, surpassing all state-of-the-art methods and achieving significant improvements of 27.6% and 6.9%, respectively. Furthermore, VLDet also demonstrates superior zero-shot performance on closed-set object detection.

Related papers

• Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object Detection [11.497620257835964]
  We propose CCKT-Det trained without any extra supervision.<n>The proposed framework constructs a cyclic and dynamic knowledge transfer from language queries and visual region features extracted from vision-language models (VLMs)<n> CCKT-Det can consistently improve performance as the scale of VLMs increases, all while requiring the detector at a moderate level of overhead.
  arXiv  Detail & Related papers  (2025-03-14T02:04:28Z)
• 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)
• 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)
• Enhancing Novel Object Detection via Cooperative Foundational Models [68.93124785575739]
  We present a novel approach to transform existing closed-set detectors into open-set detectors.<n>We surpass the current state-of-the-art by a margin of 7.2 $ textAP_50 $ for novel classes.
  arXiv  Detail & Related papers  (2023-11-19T17:28:28Z)
• Region-centric Image-Language Pretraining for Open-Vocabulary Detection [39.17829005627821]
  We present a new open-vocabulary detection approach based on region-centric image-language pretraining. At the pretraining phase, we incorporate the detector architecture on top of the classification backbone. Our approach is a simple yet effective extension of the contrastive learning method to learn emergent object-semantic cues.
  arXiv  Detail & Related papers  (2023-09-29T21:56:37Z)
• 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)
• Learning to Decompose Visual Features with Latent Textual Prompts [140.2117637223449]
  We propose Decomposed Feature Prompting (DeFo) to improve vision-language models. Our empirical study shows DeFo's significance in improving the vision-language models.
  arXiv  Detail & Related papers  (2022-10-09T15:40:13Z)
• 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 Object Detection with Proposal Mining and Prediction Equalization [73.14053674836838]
  Open-vocabulary object detection (OVD) aims to scale up vocabulary size to detect objects of novel categories beyond the training vocabulary. Recent work resorts to the rich knowledge in pre-trained vision-language models. We present MEDet, a novel OVD framework with proposal mining and prediction equalization.
  arXiv  Detail & Related papers  (2022-06-22T14:30:41Z)

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.