Diff-Feat：基于扩散模型的跨模态特征提取框架用于多标签分类

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Abstract:Multi-label classification has broad applications and depends on powerful representations capable of capturing multiple interactions. We introduce Diff-Feat, a simple yet effective framework that extracts intermediate features from pre-trained diffusion-Transformer models for both images and text, and fuses them for downstream tasks. We observe distinct patterns in optimal feature selection: for vision tasks, the most discriminative intermediate feature along the diffusion process occurs at the middle step and is located at the middle block in Transformer. In contrast, for language tasks, the best feature occurs at the noise-free step and is located at the deepest block. Notably, a consistent phenomenon across diverse datasets: a fixed "Middle Layer" yields the best performance on various downstream classification tasks for images (under DiT-XL/2-256$\times$256 and various U-ViT architectures). We further analyze the underlying mechanism and its scaling behavior across model scales. To efficiently select representations, we devise a heuristic local-search algorithm that pinpoints the locally optimal "image-text"$\times$"block-timestep" pair among a few candidates, avoiding an exhaustive grid search. A simple fusion-linear projection followed by addition-of the selected representations achieves 46.1% mAP on Visual Genome 500 (with 500 categories) and 80.2% mAP on Pix2Cap-COCO (with 80 categories), consistently outperforming strong baseline methods. Code is available at this https URL.

Submission history

From: Tian Lan [view email]
[v1] Fri, 19 Sep 2025 03:13:58 UTC (3,521 KB)
[v2] Sat, 24 Oct 2026 07:02:30 UTC (6,790 KB)