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
