Summary of Leveraging Mllm Embeddings and Attribute Smoothing For Compositional Zero-shot Learning, by Xudong Yan et al.
Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot Learning
by Xudong Yan, Songhe Feng, Yang Zhang, Jian Yang, Yueguan Lin, Haojun Fei
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Multimodal Large Language Model (MLLM) embeddings and attribute smoothing guided disentanglement (TRIDENT) framework tackles compositional zero-shot learning (CZSL) by addressing three limitations of existing methods: background influence, multimodal semantic information capture, and overconfidence in seen compositions. TRIDENT leverages feature adaptive aggregation modules to mitigate the impact of background and learnable condition masks for capturing multigranularity features during disentanglement. Additionally, it employs the last hidden states of MLLM as word embeddings for superior representation capabilities. Furthermore, attribute smoothing with auxiliary attributes generated by Large Language Model (LLM) is proposed to address overconfidence issues. Extensive experiments demonstrate that TRIDENT achieves state-of-the-art performance on three benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Compositional zero-shot learning (CZSL) is a way for machines to recognize new combinations of things they’ve learned from before. Current methods do this by breaking down what’s the same and different between images, and then using special words to help them understand. However, these methods have three problems: they get confused by the background in images, can’t capture all the complex meanings, and are too confident when they’re right. To fix these issues, scientists created a new way called TRIDENT that uses clever modules to ignore the background and learn more about what’s going on in the image. They also use special words generated by another language model to help them understand the meaning of things. This new method is really good at recognizing new combinations of things! |
Keywords
» Artificial intelligence » Language model » Large language model » Zero shot
