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Learning to Adapt Structured Output Space for Semantic Segmentation
TLDR
A multi-level adversarial network is constructed to effectively perform output space domain adaptation at different feature levels and it is shown that the proposed method performs favorably against the state-of-the-art methods in terms of accuracy and visual quality.
Adversarial Learning for Semi-supervised Semantic Segmentation
TLDR
It is shown that the proposed discriminator can be used to improve semantic segmentation accuracy by coupling the adversarial loss with the standard cross entropy loss of the proposed model.
SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
TLDR
This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos, and demonstrates that introducing optical flow improves the performance of segmentation, against the state-of-the-art algorithms.
Video Segmentation via Object Flow
TLDR
This work forms a principled, multiscale, spatio-temporal objective function that uses optical flow to propagate information between frames for video segmentation and demonstrates the effectiveness of jointly optimizing optical flow and video segmentations using an iterative scheme.
Deep Image Harmonization
TLDR
This work proposes an end-to-end deep convolutional neural network for image harmonization, which can capture both the context and semantic information of the composite images during harmonization and introduces an efficient way to collect large-scale and high-quality training data that can facilitate the training process.
Fast and Accurate Online Video Object Segmentation via Tracking Parts
TLDR
This paper proposes a fast and accurate video object segmentation algorithm that can immediately start the segmentation process once receiving the images, and performs favorably against state-of-the-art algorithms in accuracy on the DAVIS benchmark dataset, while achieving much faster runtime performance.
Weakly-Supervised Semantic Segmentation via Sub-Category Exploration
TLDR
This work proposes a simple yet effective approach that introduces a self-supervised task by exploiting the sub- category information and performs clustering on image features to generate pseudo sub-categories labels within each annotated parent class.
Domain Adaptation for Structured Output via Discriminative Patch Representations
TLDR
A domain adaptation method to adapt the source data to the unlabeled target domain by discovering multiple modes of patch-wise output distribution through the construction of a clustered space and using an adversarial learning scheme to push the feature representations of target patches in the clustered space closer to the distributions of source patches.
BiFuse: Monocular 360 Depth Estimation via Bi-Projection Fusion
TLDR
A bi-projection fusion scheme along with learnable masks to balance the feature map from the two projections is proposed to predict the depth map of a monocular 360 image by mimicking both peripheral and foveal vision of the human eye.
Active Adversarial Domain Adaptation
TLDR
This work shows that the two views of adversarial domain alignment and importance sampling can be unified in one framework for domain adaptation and transfer learning when the source domain has many labeled examples while the target domain does not.
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