Summary of Exploiting Lmm-based Knowledge For Image Classification Tasks, by Maria Tzelepi et al.
Exploiting LMM-based knowledge for image classification tasks
by Maria Tzelepi, Vasileios Mezaris
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 The proposed paper leverages Large Multimodal Models (LMMs) to improve image classification tasks by extracting semantic descriptions using MiniGPT-4. Unlike previous approaches like CLIP, which utilize only the image encoder, this paper combines both image and text embeddings generated from the LMM-based knowledge to solve image classification tasks. The experimental results on three datasets demonstrate improved classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers use a special kind of model called Large Multimodal Models (LMMs) to help with picture classification. They take these models, like MiniGPT-4, and get them to describe the pictures in words. Usually, people just use one part of the model for this job, but the new method uses both parts to get even better results. The team tested their idea on three sets of images and found that it really works! |
Keywords
» Artificial intelligence » Classification » Encoder » Image classification
