Generalizable Semantic Vision Query Generation for Zero-shot Panoptic and Semantic Segmentation

TL;DR

This paper introduces CONCAT, a novel method for zero-shot panoptic segmentation that improves generalization to unseen categories by aligning visual and semantic features and synthesizing detailed pseudo-queries, achieving state-of-the-art results.

Contribution

The paper proposes CONCAT, a new approach combining feature alignment and semantic-vision training to enhance zero-shot segmentation performance and speed.

Findings

Achieves 5.2% higher hPQ than previous SOTA.

Effective in inductive ZPS and open-vocabulary segmentation.

Runs twice as fast during testing.

Abstract

Zero-shot Panoptic Segmentation (ZPS) aims to recognize foreground instances and background stuff without images containing unseen categories in training. Due to the visual data sparsity and the difficulty of generalizing from seen to unseen categories, this task remains challenging. To better generalize to unseen classes, we propose Conditional tOken aligNment and Cycle trAnsiTion (CONCAT), to produce generalizable semantic vision queries. First, a feature extractor is trained by CON to link the vision and semantics for providing target queries. Formally, CON is proposed to align the semantic queries with the CLIP visual CLS token extracted from complete and masked images. To address the lack of unseen categories, a generator is required. However, one of the gaps in synthesizing pseudo vision queries, ie, vision queries for unseen categories, is describing fine-grained visual details through semantic embeddings. Therefore, we approach CAT to train the generator in semantic-vision and vision-semantic manners. In semantic-vision, visual query contrast is proposed to model the high granularity of vision by pulling the pseudo vision queries with the corresponding targets containing segments while pushing those without segments away. To ensure the generated queries retain semantic information, in vision-semantic, the pseudo vision queries are mapped back to semantic and supervised by real semantic embeddings. Experiments on ZPS achieve a 5.2% hPQ increase surpassing SOTA. We also examine inductive ZPS and open-vocabulary semantic segmentation and obtain comparative results while being 2 times faster in testing.