CRIS: CLIP-Driven Referring Image Segmentation (2111.15174v2)

Abstract: Referring image segmentation aims to segment a referent via a natural linguistic expression.Due to the distinct data properties between text and image, it is challenging for a network to well align text and pixel-level features. Existing approaches use pretrained models to facilitate learning, yet separately transfer the language/vision knowledge from pretrained models, ignoring the multi-modal corresponding information. Inspired by the recent advance in Contrastive Language-Image Pretraining (CLIP), in this paper, we propose an end-to-end CLIP-Driven Referring Image Segmentation framework (CRIS). To transfer the multi-modal knowledge effectively, CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. More specifically, we design a vision-language decoder to propagate fine-grained semantic information from textual representations to each pixel-level activation, which promotes consistency between the two modalities. In addition, we present text-to-pixel contrastive learning to explicitly enforce the text feature similar to the related pixel-level features and dissimilar to the irrelevances. The experimental results on three benchmark datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art performance without any post-processing. The code will be released.

An Analysis of CRIS: CLIP-Driven Referring Image Segmentation

The paper entitled "CRIS: CLIP-Driven Referring Image Segmentation" introduces an advanced framework for the challenging task of referring image segmentation, which involves identifying specific image regions based on natural language descriptions. This task is non-trivial due to the inherent differences between text and image data modalities. Existing methods often struggle to effectively align textual descriptions with pixel-level features, frequently necessitating pre-trained models for isolated language or vision tasks, thus neglecting the potential for true multi-modal synchronization. This paper proposes an innovative framework, denoted as CRIS, which builds upon the recent developments in Contrastive Language-Image Pretraining (CLIP) to better align text features with image data at the pixel level.

Methodology

CRIS stands out by its novel architecture that employs vision-language decoding alongside contrastive learning to achieve fine-grained alignment between text-based descriptions and image features. Specifically, the architecture leverages:

1. Vision-Language Decoder: This component is critical for transmitting rich semantic information from textual representations directly to pixel-level activations, ensuring consistency across both modalities. The decoder employs cross-attention mechanisms that allow for detailed propagation of text-based semantics into the visual domain, thus refining the synergy between image and linguistic data.
2. Text-to-Pixel Contrastive Learning: By implementing a novel contrastive learning technique, CRIS directly enhances the model's ability to distinguish relevant pixel-level features from irrelevancies, thereby improving segmentation accuracy. This approach explicitly ties the text representations to their associated visual characteristics, consequently optimizing the segmentation masks.

Experimental Results and Comparative Analysis

The paper reports compelling experimental results across three benchmarks: RefCOCO, RefCOCO+, and G-Ref datasets. The CRIS framework yields substantial improvements over state-of-the-art methods, with significant IoU gains reported on all datasets. For instance, when harnessing the ResNet-101 backbone, CRIS achieves a notable increase in IoU by approximately 4-8% across various datasets compared to prior leading methodologies.

The improvement trajectory is justified by the paper's extensive ablations, highlighting the individual contributions from both the contrastive learning and the vision-language decoding components. These components collectively contribute to the framework's ability to accurately interpret complex linguistic inputs and delineate the corresponding image regions effectively. The results accentuate the framework's capacity to maintain high segmentation performance even with lengthy and complex referring expressions, which represent real-world challenges in understanding nuanced language-visual cues.

Implications and Future Directions

The implications of this work are significant for advancing referring image segmentation. By tightly integrating the CLIP pretraining paradigm with new methods of multi-modal data alignment, the CRIS framework establishes a pathway for improved performance in tasks that require sophisticated interpretation of combined language and image data. This model opens up potential applications in areas such as interactive image editing and AI-driven human-computer interaction systems.

The paper's methodology and results suggest exciting prospects for future developments in this area. An exploration of further integrating transformer-based architectures with CRIS may offer more sophisticated solutions to cross-domain interaction problems. Additionally, extending this approach to other tasks within multi-modal AI, such as complex video scene understanding or 3D object segmentation, represents a promising avenue for continued research. Further refinements in learning strategies, including real-time adaptation and domain-specific customization, could also broaden the practical applicability of the CRIS framework.

In conclusion, the CRIS framework embodies a significant step towards more effective referring image segmentation. By capitalizing on the strengths of recent advancements in multi-modal learning, it paves the way for continued innovation in the alignment of language and visual data within AI systems.