Selected work

Representative Papers

Training-Free Fine-Grained Semantic Segmentations in Low Data Regimes: A FungiTastic Baseline

May 21, 2026

This work addresses the challenges of precise localization and discrimination among visually similar classes in fine-grained semantic segmentation under low-data regimes, particularly for fungal images exhibiting long-tailed distributions and varying acquisition conditions. To tackle these issues, the authors propose a training-free, two-stage decoupled framework: first, category-agnostic masks are generated using SAM3 guided by coarse-class prompts; then, fine-grained labels are assigned via prototype matching in the DINOv2 embedding space, augmented with simple feature-space transformations to enhance classification performance. This approach establishes the first effective baseline for low-data fine-grained segmentation, demonstrating superior performance across settings ranging from one-shot to hundreds of samples, while offering strong scalability and low computational cost.

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Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Map

May 06, 2025

Addressing the high annotation cost and stringent accuracy requirements in industrial defect segmentation, this paper proposes a bounding-box-guided diffusion model synthesis framework. We innovatively introduce an enhanced bounding-box representation as conditional input to DDPM, enabling geometry-aware encoding, layout-appearance disentangled control, and multi-scale feature alignment—significantly improving defect localization accuracy and cross-sample consistency. Two novel quantitative metrics are proposed to evaluate synthetic image quality. Experiments demonstrate that combining only 10% real annotations with synthetic data achieves 96.3% of the full-supervision segmentation performance baseline; synthetic images yield a 27% reduction in FID score, while segmentation masks achieve a 14.8% improvement in IoU. This work establishes a new paradigm for low-supervision industrial vision: high-fidelity, pixel-precise data generation.

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Recent publications

Latest Papers

Training-Free Fine-Grained Semantic Segmentations in Low Data Regimes: A FungiTastic Baseline

May 21, 2026

This work addresses the challenges of precise localization and discrimination among visually similar classes in fine-grained semantic segmentation under low-data regimes, particularly for fungal images exhibiting long-tailed distributions and varying acquisition conditions. To tackle these issues, the authors propose a training-free, two-stage decoupled framework: first, category-agnostic masks are generated using SAM3 guided by coarse-class prompts; then, fine-grained labels are assigned via prototype matching in the DINOv2 embedding space, augmented with simple feature-space transformations to enhance classification performance. This approach establishes the first effective baseline for low-data fine-grained segmentation, demonstrating superior performance across settings ranging from one-shot to hundreds of samples, while offering strong scalability and low computational cost.

0 citationsRead paper

Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Map

May 06, 2025

Addressing the high annotation cost and stringent accuracy requirements in industrial defect segmentation, this paper proposes a bounding-box-guided diffusion model synthesis framework. We innovatively introduce an enhanced bounding-box representation as conditional input to DDPM, enabling geometry-aware encoding, layout-appearance disentangled control, and multi-scale feature alignment—significantly improving defect localization accuracy and cross-sample consistency. Two novel quantitative metrics are proposed to evaluate synthetic image quality. Experiments demonstrate that combining only 10% real annotations with synthetic data achieves 96.3% of the full-supervision segmentation performance baseline; synthetic images yield a 27% reduction in FID score, while segmentation masks achieve a 14.8% improvement in IoU. This work establishes a new paradigm for low-supervision industrial vision: high-fidelity, pixel-precise data generation.

0 citationsRead paper