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In A Latest Computer Vision Paper, Researchers Propose A Novel Framework To Leverage The Representation And Generalization Capability Of Pre-Trained Multi-Modal Models Towards Improved Open-Vocabulary Detection (OVD)


Open Vocabulary Detection attempts to generalize beyond the restricted number of base classes taught in the training phase. In inference, the goal is to identify novel types defined by infinite or open vocabulary. Due to the difficulty of the OVD problem, several types of weak handling for novel categories are commonly used, such as additional image caption pairs to increase vocabulary, image level labels in datasets. classification, and pretrained open vocabulary classification models such as CLIP.

Intrinsic mismatch between region and image-level signals is one of the critical issues in expanding vocabulary using image-level supervision (ILS) or pretrained models learned using ILS. The use of weak management to expand vocabulary is reasonable because the cost of annotating large categories of identification data is too expensive. In contrast, image-text/label matches are easily accessible through large categorization datasets or online sources. Because the CLIP model is made with full photographs, the pretrained CLIP embeddings used in previous OVD models are not good at locating object areas.

The most recent work investigates expensive pretraining with additional goals or heuristics such as max-score or max-size boxes for label grounding in images. Similarly, photographs with poor handling, such as caption descriptions or image level labeling, do not transmit exact object-centered information. The researchers designed this study to bridge the gap between object-centric and image-centric representation within the OVD pipeline. They recommend the use of a pretrained multi-modal vision transformer (ViT) to generate high-quality object-agnostic and class-specific recommendations.

The proposed approach links image, region, and language representations to improve the overall open vocabulary items. Class-agnostic object proposals are then used to extract region-specific information from CLIP visual embeddings, allowing them to be used for local objects. In addition, the class-specific proposal set allows us to see a broader vocabulary, which helps to generalize to new categories. The third and most critical topic is to make visual-language (VL) mapping compatible with local object-centric data. They present a region-conditioned weight transfer method that tightly integrates image and regional VL mapping.

Source: https://arxiv.org/pdf/2207.03482v1.pdf

This study makes significant contributions as follows:

  • Propose regional-based knowledge distillation to modify CLIP image-centered embeddings for local regions, improving alignment between regional and language embeddings
  • To detect weak image labels, the technique uses high-quality object suggestions from pretrained multi-modal ViTs to perform pseudo-labeling.
  • The contributions listed above are primarily aimed at the visual realm. To maintain the benefits of object-centric alignment in the language domain, a unique weight transfer function is suggested to condition (pseudo-labeled) image-level VL mapping to region-level VL mapping.

On the COCO and LVIS benchmarks, our method achieved absolute wins of 11.9 and 5.0 AP over the novel and rare classes of current SOTA methods. Further generalizability was demonstrated by our cross-dataset evaluation performed on COCO, OpenImages, and Objects365, which led to steady improvement compared to existing methods. The code implementation of this paper is free to use on GitHub.

This Article is written as a summary article by Marktechpost Staff based on the research paper 'Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection . All Credit For This Research Goes To Researchers on This Project. Checkout the paper and github link.

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