Dichotomous Image Segmentation
22 papers with code • 6 benchmarks • 2 datasets
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.
Libraries
Use these libraries to find Dichotomous Image Segmentation models and implementationsMost implemented papers
BASNet: Boundary-Aware Salient Object Detection
In this paper, we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection.
Suppress and Balance: A Simple Gated Network for Salient Object Detection
With the help of multilevel gate units, the valuable context information from the encoder can be optimally transmitted to the decoder.
Global Context-Aware Progressive Aggregation Network for Salient Object Detection
Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role.
Revisiting Image Pyramid Structure for High Resolution Salient Object Detection
Salient object detection (SOD) has been in the spotlight recently, yet has been studied less for high-resolution (HR) images.
Multi-view Aggregation Network for Dichotomous Image Segmentation
Dichotomous Image Segmentation (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images.
HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation
We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder.
Concealed Object Detection
We present the first systematic study on concealed object detection (COD), which aims to identify objects that are "perfectly" embedded in their background.
Camouflaged Object Segmentation with Distraction Mining
In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature.
Highly Accurate Dichotomous Image Segmentation
We present a systematic study on a new task called dichotomous image segmentation (DIS) , which aims to segment highly accurate objects from natural images.
Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image Segmentation
First, a dual-size input feeds into the shared backbone to produce more holistic and detailed features while keeping the model lightweight.