Aligning Forest and Trees in Images & Long Captions for Visually Grounded Understanding

TL;DR

This paper introduces CAFT, a hierarchical vision-language model that improves understanding of long, detailed captions by aligning local scene parts with text, achieving state-of-the-art results in long-text retrieval.

Contribution

CAFT is the first model to jointly learn local part-text and global image-text alignment for detailed scene understanding without explicit region supervision.

Findings

CAFT outperforms previous models on six long-text retrieval benchmarks.

It learns fine-grained, localized semantic representations without explicit supervision.

The model demonstrates strong scaling behavior with large-scale training.

Abstract

Vision-language models such as CLIP often struggle to faithfully understand long, detail-rich captions, relying on dominant scene cues while overlooking fine-grained visual evidence. We propose a hierarchical vision-language learning principle for understanding scenes as part-to-whole compositions: before forming a whole-scene representation, a model should uncover what semantic parts appear where in the image. To this end, we propose CAFT (Cross-domain Alignment of Forests and Trees), a vision-language model that jointly learns local text-region alignment at intermediate representations and global image-text alignment at the final representation. Exploiting the organization of long captions, where local descriptions often correspond to scene parts, CAFT employs a fine-to-coarse image encoder and a part-whole text encoder to discover localized part semantics and progressively compose them into a global image-text representation. Trained on 30M image-text pairs, CAFT achieves state-of-the-art performance on six long-text retrieval benchmarks and exhibits strong scaling behavior. Experiments show that CAFT learns fine-grained representations that localize textual semantics in image regions without explicit region-level supervision.