Weakly-Supervised Semantic Segmentation
The semantic segmentation task is to assign a label from a label set to each pixel in an image. In the case of fully supervised setting, the dataset consists of images and their corresponding pixel-level class-specific annotations (expensive pixel-level annotations). However, in the weakly-supervised setting, the dataset consists of images and corresponding annotations that are relatively easy to obtain, such as tags/labels of objects present in the image.
( Image credit: Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing )
Leaderboards
Greatest papers with code
Spatio-temporal video autoencoder with differentiable memory
At each time step, the system receives as input a video frame, predicts the optical flow based on the current observation and the LSTM memory state as a dense transformation map, and applies it to the current frame to generate the next frame.
MOTION ESTIMATION OPTICAL FLOW ESTIMATION WEAKLY-SUPERVISED SEMANTIC SEGMENTATION
Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation
Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.
SEMI-SUPERVISED SEMANTIC SEGMENTATION WEAKLY-SUPERVISED SEMANTIC SEGMENTATION
Weakly Supervised Learning of Instance Segmentation With Inter-Pixel Relations
For generating the pseudo labels, we first identify confident seed areas of object classes from attention maps of an image classification model, and propagate them to discover the entire instance areas with accurate boundaries.
IMAGE CLASSIFICATION INSTANCE SEGMENTATION WEAKLY-SUPERVISED INSTANCE SEGMENTATION WEAKLY-SUPERVISED SEMANTIC SEGMENTATION
Weakly-Supervised Semantic Segmentation Network With Deep Seeded Region Growing
CVPR 2018
• speedinghzl/DSRG
• 
Inspired by the traditional image segmentation methods of seeded region growing, we propose to train a semantic segmentation network starting from the discriminative regions and progressively increase the pixel-level supervision using by seeded region growing.
Weakly- and Semi-Supervised Panoptic Segmentation
We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks.
#12 best model for
Panoptic Segmentation
on Cityscapes val
INSTANCE SEGMENTATION PANOPTIC SEGMENTATION WEAKLY-SUPERVISED INSTANCE SEGMENTATION WEAKLY-SUPERVISED PANOPTIC SEGMENTATION WEAKLY-SUPERVISED SEMANTIC SEGMENTATION
Constrained-CNN losses for weakly supervised segmentation
• meng-tang/rloss
• 
To the best of our knowledge, the method of [Pathak et al., 2015] is the only prior work that addresses deep CNNs with linear constraints in weakly supervised segmentation.
MEDICAL IMAGE SEGMENTATION WEAKLY-SUPERVISED SEMANTIC SEGMENTATION
Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation
This regularized CAM can be embedded in most recent advanced weakly supervised semantic segmentation framework.
Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation
• arslan-chaudhry/dcsp_segmentation
• 
We propose an approach to discover class-specific pixels for the weakly-supervised semantic segmentation task.
Integral Object Mining via Online Attention Accumulation
ICCV 2019
• PengtaoJiang/OAA
•
In order to accumulate the discovered different object parts, we propose an online attention accumulation (OAA) strategy which maintains a cumulative attention map for each target category in each training image so that the integral object regions can be gradually promoted as the training goes.
Mask Based Unsupervised Content Transfer
• rmokady/mbu-content-tansfer
•
We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other.