SGC-VQGAN: Towards Complex Scene Representation via Semantic Guided Clustering Codebook

• URL: http://arxiv.org/abs/2409.06105v1
• Date: Mon, 9 Sep 2024 23:12:43 GMT
• Title: SGC-VQGAN: Towards Complex Scene Representation via Semantic Guided Clustering Codebook
• Authors: Chenjing Ding, Chiyu Wang, Boshi Liu, Xi Guo, Weixuan Tang, Wei Wu,
• Abstract summary: We introduce SGC-VQGAN through Semantic Online Clustering method to enhance token semantics through Consistent Semantic Learning. Our approach constructs a temporospatially consistent semantic codebook, addressing issues of codebook collapse and imbalanced token semantics.
• Score: 9.993066868670283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vector quantization (VQ) is a method for deterministically learning features through discrete codebook representations. Recent works have utilized visual tokenizers to discretize visual regions for self-supervised representation learning. However, a notable limitation of these tokenizers is lack of semantics, as they are derived solely from the pretext task of reconstructing raw image pixels in an auto-encoder paradigm. Additionally, issues like imbalanced codebook distribution and codebook collapse can adversely impact performance due to inefficient codebook utilization. To address these challenges, We introduce SGC-VQGAN through Semantic Online Clustering method to enhance token semantics through Consistent Semantic Learning. Utilizing inference results from segmentation model , our approach constructs a temporospatially consistent semantic codebook, addressing issues of codebook collapse and imbalanced token semantics. Our proposed Pyramid Feature Learning pipeline integrates multi-level features to capture both image details and semantics simultaneously. As a result, SGC-VQGAN achieves SOTA performance in both reconstruction quality and various downstream tasks. Its simplicity, requiring no additional parameter learning, enables its direct application in downstream tasks, presenting significant potential.

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