Learning Hierarchical Image Segmentation For Recognition and By Recognition
This addresses the lack of detailed visual substantiation in large vision and language models by enabling unsupervised part-whole discovery, which is significant for computer vision tasks like segmentation and classification.
The paper tackles the problem of integrating hierarchical segmentation with recognition in vision models, proposing a method that trains a hierarchical segmenter solely on image-level recognition objectives, resulting in improved performance over Vision Transformer and surpassing SAM by 8% mIoU on PartImageNet segmentation.
Large vision and language models learned directly through image-text associations often lack detailed visual substantiation, whereas image segmentation tasks are treated separately from recognition, supervisedly learned without interconnections. Our key observation is that, while an image can be recognized in multiple ways, each has a consistent part-and-whole visual organization. Segmentation thus should be treated not as an end task to be mastered through supervised learning, but as an internal process that evolves with and supports the ultimate goal of recognition. We propose to integrate a hierarchical segmenter into the recognition process, train and adapt the entire model solely on image-level recognition objectives. We learn hierarchical segmentation for free alongside recognition, automatically uncovering part-to-whole relationships that not only underpin but also enhance recognition. Enhancing the Vision Transformer (ViT) with adaptive segment tokens and graph pooling, our model surpasses ViT in unsupervised part-whole discovery, semantic segmentation, image classification, and efficiency. Notably, our model (trained on unlabeled 1M ImageNet images) outperforms SAM (trained on 11M images and 1 billion masks) by absolute 8% in mIoU on PartImageNet object segmentation.