Dichotomous Image Segmentation
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Currently, existing image segmentation tasks mainly focus on segmenting objects with specific characteristics, e.g., salient, camouflaged, meticulous, or specific categories. Most of them have the same input/output formats, and barely use exclusive mechanisms designed for segmenting targets in their models, which means almost all tasks are dataset-dependent. Thus, it is very promising to formulate a category-agnostic DIS task for accurately segmenting objects with different structure complexities, regardless of their characteristics. Compared with semantic segmentation, the proposed DIS task usually focuses on images with single or a few targets, from which getting richer accurate details of each target is more feasible.
논문
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BEN: Using Confidence-Guided Matting for Dichotomous Image Segmentation
Mask Factory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation
High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity
Evaluation Study on SAM 2 for Class-agnostic Instance-level Segmentation
Multi-view Aggregation Network for Dichotomous Image Segmentation
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
Promoting Segment Anything Model towards Highly Accurate Dichotomous Image Segmentation
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process
Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image Segmentation
Revisiting Image Pyramid Structure for High Resolution Salient Object Detection
Highly Accurate Dichotomous Image Segmentation
Rethinking BiSeNet For Real-time Semantic Segmentation
Camouflaged Object Segmentation with Distraction Mining
Concealed Object Detection
HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation
Suppress and Balance: A Simple Gated Network for Salient Object Detection
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Global Context-Aware Progressive Aggregation Network for Salient Object Detection
F3Net: Fusion, Feedback and Focus for Salient Object Detection
Deep High-Resolution Representation Learning for Visual Recognition
Searching for MobileNetV3
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
Rethinking Atrous Convolution for Semantic Image Segmentation
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
Pyramid Scene Parsing Network
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.