Summary of Attribute-aware Implicit Modality Alignment For Text Attribute Person Search, by Xin Wang et al.
Attribute-Aware Implicit Modality Alignment for Text Attribute Person Search
by Xin Wang, Fangfang Liu, Zheng Li, Caili Guo
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper proposes a novel framework called Attribute-Aware Implicit Modality Alignment (AIMA) to address the significant modality gap between textual attributes and images in pedestrian search. The AIMA framework learns the correspondence of local representations between textual attributes and images, combining global representation matching to narrow the modality gap. The approach uses CLIP as the backbone, designing prompt templates to transform attribute combinations into structured sentences. This enables the model to better understand image details. The paper also introduces a Masked Attribute Prediction (MAP) module that predicts masked attributes after multi-modal interaction, achieving implicit local relationship alignment. Finally, an Attribute-IoU Guided Intra-Modal Contrastive (A-IoU IMC) loss is proposed, aligning the distribution of different textual attributes in the embedding space with their IoU distribution, achieving better semantic arrangement. The AIMA framework outperforms current state-of-the-art methods on Market-1501 Attribute, PETA, and PA100K datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers find specific people based on descriptions given by witnesses. It’s like searching for someone in a crowd using a written description. The challenge is that words and images are very different, making it hard to match them up correctly. The new approach uses a special technique called AIMA (Attribute-Aware Implicit Modality Alignment) to bridge this gap. It works by first understanding the meaning of the written description and then matching it with an image of the person. This helps the computer find the right person in a crowded scene. |
Keywords
» Artificial intelligence » Alignment » Embedding space » Multi modal » Prompt
