GroupViT: Semantic Segmentation Emerges from Text Supervision (2202.11094v5)

Abstract: Grouping and recognition are important components of visual scene understanding, e.g., for object detection and semantic segmentation. With end-to-end deep learning systems, grouping of image regions usually happens implicitly via top-down supervision from pixel-level recognition labels. Instead, in this paper, we propose to bring back the grouping mechanism into deep networks, which allows semantic segments to emerge automatically with only text supervision. We propose a hierarchical Grouping Vision Transformer (GroupViT), which goes beyond the regular grid structure representation and learns to group image regions into progressively larger arbitrary-shaped segments. We train GroupViT jointly with a text encoder on a large-scale image-text dataset via contrastive losses. With only text supervision and without any pixel-level annotations, GroupViT learns to group together semantic regions and successfully transfers to the task of semantic segmentation in a zero-shot manner, i.e., without any further fine-tuning. It achieves a zero-shot accuracy of 52.3% mIoU on the PASCAL VOC 2012 and 22.4% mIoU on PASCAL Context datasets, and performs competitively to state-of-the-art transfer-learning methods requiring greater levels of supervision. We open-source our code at https://github.com/NVlabs/GroupViT .

GroupViT: Semantic Segmentation Emerges from Text Supervision

The paper introduces a novel architecture, GroupViT, which aims to perform semantic segmentation purely through text supervision. Unlike traditional methods that rely heavily on pixel-level annotations, GroupViT leverages image-text pairs to learn meaningful segmentation in a zero-shot manner. This paper presents both the theoretical framework and empirical results demonstrating its capability to achieve competitive segmentation accuracy without pixel-wise supervision.

Methodology

The core concept of GroupViT is the incorporation of a grouping mechanism into the Vision Transformer architecture (ViT). This method segments images into semantically relevant regions guided by text supervision. The architecture employs a hierarchical grouping process using group tokens that coalesce image tokens into progressively larger and arbitrarily-shaped segments.

The model's training relies on a contrastive learning framework. GroupViT uses image-text data pairs, aligning visual embeddings with corresponding textual descriptions. This is accomplished through contrastive losses, enabling the model to associate visual region groupings cohesively with textual concepts. Additionally, a multi-label contrastive loss is introduced, using textual prompts of noun words to enhance the training signal.

Numerical Results

GroupViT delivers robust performance across standard benchmarks. It achieves a zero-shot mIoU of 52.3% on the PASCAL VOC 2012 dataset and 22.4% on the PASCAL Context dataset, highlighting its potential to rival transfer-learning methods that require extensive supervision. The experimental results affirm that GroupViT can generalize to various domains without the need for fine-tuning, showcasing its versatility and efficiency in zero-shot scenarios.

Implications and Future Directions

The results presented in the paper illuminate a pathway for reduction in human annotation efforts, potentially transforming how models can be trained directly from unstructured web data. GroupViT's ability to learn and infer semantic groupings without pixel-level annotations opens a new dimension in efficient semantic segmentation using text supervision, which has primarily focused on classification tasks.

Future developments could explore optimizing GroupViT's architecture for improved segmentation boundary recognition and extending its application to broader datasets, considering background and contextual classes. Additionally, incorporating segmentation-specific techniques like dilated convolutions or pyramid pooling could further enhance performance.

In summary, GroupViT sets a strong precedent in the field of zero-shot semantic segmentation using text data alone. It demonstrates that visual and textual integration via Transformers can yield meaningful semantic understanding, which may inspire enhancements in AI applications across tasks requiring less explicit supervision. The open-sourcing of their code invites further exploration and innovation from the research community.