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Summary of Improving Multi-label Recognition Using Class Co-occurrence Probabilities, by Samyak Rawlekar et al.


Improving Multi-label Recognition using Class Co-Occurrence Probabilities

by Samyak Rawlekar, Shubhang Bhatnagar, Vishnuvardhan Pogunulu Srinivasulu, Narendra Ahuja

First submitted to arxiv on: 24 Apr 2024

Categories

  • Main: Computer Vision and Pattern Recognition (cs.CV)
  • Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM); Image and Video Processing (eess.IV)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
A novel framework for Multi-label Recognition (MLR) leverages information from vision-language models (VLMs) to improve the identification of multiple objects within an image. By incorporating conditional probabilities between object pairs, this method surpasses state-of-the-art approaches on four MLR datasets. The proposed framework utilizes a Graph Convolutional Network (GCN) to refine initial estimates derived from VLMs and image-text sources.
Low GrooveSquid.com (original content) Low Difficulty Summary
For multi-object detection in images, researchers have been using vision-language models trained on large text-image datasets. These models learn separate classifiers for each object, without considering when objects appear together. Our new approach looks at these co-occurrences to make the recognition more accurate. We use a special type of neural network called Graph Convolutional Network (GCN) to combine this information and improve the results.

Keywords

» Artificial intelligence  » Convolutional network  » Gcn  » Neural network  » Object detection