GenMask：通过直接掩码生成适应DiT进行分割
GenMask: Adapting DiT for Segmentation via Direct Mask Generation
AI总结：
本文提出GenMask，通过直接生成二进制掩码实现分割任务，避免传统间接特征提取流程，提升分割性能。
摘要：
本文提出GenMask，通过直接生成二进制掩码实现分割任务，避免传统间接特征提取流程，提升分割性能。
英文摘要：
Recent approaches for segmentation have leveraged pretrained generative models as feature extractors, treating segmentation as a downstream adaptation task via indirect feature retrieval. This implicit use suffers from a fundamental misalignment in representation. It also depends heavily on indirect feature extraction pipelines, which complicate the workflow and limit adaptation. In this paper, we argue that instead of indirect adaptation, segmentation tasks should be trained directly in a generative manner. We identify a key obstacle to this unified formulation: VAE latents of binary masks are sharply distributed, noise robust, and linearly separable, distinct from natural image latents. To bridge this gap, we introduce timesteps sampling strategy for binary masks that emphasizes extreme noise levels for segmentation and moderate noise for image generation, enabling harmonious joint training. We present GenMask, a DiT trains to generate black-and-white segmentation masks as well as colorful images in RGB space under the original generative objective. GenMask preserves the original DiT architecture while removing the need of feature extraction pipelines tailored for segmentation tasks. Empirically, GenMask attains state-of-the-art performance on referring and reasoning segmentation benchmarks and ablations quantify the contribution of each component.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
