Language-Driven Visual Consensus for Zero-Shot Semantic Segmentation
This work addresses the challenge of generalizing segmentation models to unseen classes in computer vision, representing an incremental improvement over existing methods.
The paper tackles the problem of overfitting and mask fragmentation in zero-shot semantic segmentation by proposing a Language-Driven Visual Consensus (LDVC) approach, which improves alignment between visual features and class embeddings, resulting in mIoU gains of 4.5 on PASCAL VOC 2012 and 3.6 on COCO-Stuff 164k for unseen classes.
The pre-trained vision-language model, exemplified by CLIP, advances zero-shot semantic segmentation by aligning visual features with class embeddings through a transformer decoder to generate semantic masks. Despite its effectiveness, prevailing methods within this paradigm encounter challenges, including overfitting on seen classes and small fragmentation in masks. To mitigate these issues, we propose a Language-Driven Visual Consensus (LDVC) approach, fostering improved alignment of semantic and visual information.Specifically, we leverage class embeddings as anchors due to their discrete and abstract nature, steering vision features toward class embeddings. Moreover, to circumvent noisy alignments from the vision part due to its redundant nature, we introduce route attention into self-attention for finding visual consensus, thereby enhancing semantic consistency within the same object. Equipped with a vision-language prompting strategy, our approach significantly boosts the generalization capacity of segmentation models for unseen classes. Experimental results underscore the effectiveness of our approach, showcasing mIoU gains of 4.5 on the PASCAL VOC 2012 and 3.6 on the COCO-Stuff 164k for unseen classes compared with the state-of-the-art methods.