Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ZSD-YOLO: Zero-Shot YOLO Detection using Vision-Language
  KnowledgeDistillation   Real-world object sampling produces long-tailed distributions requiring
exponentially more images for rare types. Zero-shot detection, which aims to
detect unseen objects, is one direction to address this problem. A dataset such
as COCO is extensively annotated across many images but with a sparse number of
categories and annotating all object classes across a diverse domain is
expensive and challenging. To advance zero-shot detection, we develop a
Vision-Language distillation method that aligns both image and text embeddings
from a zero-shot pre-trained model such as CLIP to a modified semantic
prediction head from a one-stage detector like YOLOv5. With this method, we are
able to train an object detector that achieves state-of-the-art accuracy on the
COCO zero-shot detection splits with fewer model parameters. During inference,
our model can be adapted to detect any number of object classes without
additional training. We also find that the improvements provided by the scaling
of our method are consistent across various YOLOv5 scales. Furthermore, we
develop a self-training method that provides a significant score improvement
without needing extra images nor labels.

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The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: math
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The categories of this paper are: physics
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: eess