CAM-Seg: A Continuous-valued Embedding Approach for Semantic Image Generation

• URL: http://arxiv.org/abs/2503.15617v1
• Date: Wed, 19 Mar 2025 18:06:54 GMT
• Title: CAM-Seg: A Continuous-valued Embedding Approach for Semantic Image Generation
• Authors: Masud Ahmed, Zahid Hasan, Syed Arefinul Haque, Abu Zaher Md Faridee, Sanjay Purushotham, Suya You, Nirmalya Roy,
• Abstract summary: Autoencoder accuracy on segmentation mask using quantized embeddings is 8% lower than continuous-valued embeddings.<n>We propose a continuous-valued embedding framework for semantic segmentation.<n>Our approach eliminates the need for discrete latent representations while preserving fine-grained semantic details.
• Score: 11.170848285659572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional transformer-based semantic segmentation relies on quantized embeddings. However, our analysis reveals that autoencoder accuracy on segmentation mask using quantized embeddings (e.g. VQ-VAE) is 8% lower than continuous-valued embeddings (e.g. KL-VAE). Motivated by this, we propose a continuous-valued embedding framework for semantic segmentation. By reformulating semantic mask generation as a continuous image-to-embedding diffusion process, our approach eliminates the need for discrete latent representations while preserving fine-grained spatial and semantic details. Our key contribution includes a diffusion-guided autoregressive transformer that learns a continuous semantic embedding space by modeling long-range dependencies in image features. Our framework contains a unified architecture combining a VAE encoder for continuous feature extraction, a diffusion-guided transformer for conditioned embedding generation, and a VAE decoder for semantic mask reconstruction. Our setting facilitates zero-shot domain adaptation capabilities enabled by the continuity of the embedding space. Experiments across diverse datasets (e.g., Cityscapes and domain-shifted variants) demonstrate state-of-the-art robustness to distribution shifts, including adverse weather (e.g., fog, snow) and viewpoint variations. Our model also exhibits strong noise resilience, achieving robust performance ($\approx$ 95% AP compared to baseline) under gaussian noise, moderate motion blur, and moderate brightness/contrast variations, while experiencing only a moderate impact ($\approx$ 90% AP compared to baseline) from 50% salt and pepper noise, saturation and hue shifts. Code available: https://github.com/mahmed10/CAMSS.git

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