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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.
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.
SCOPS: Self-Supervised Co-Part Segmentation
TLDR
This work proposes a self-supervised deep learning approach for part segmentation, where several loss functions are devised that aids in predicting part segments that are geometrically concentrated, robust to object variations and are also semantically consistent across different object instances.
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.
Progressive Domain Adaptation for Object Detection
TLDR
This paper proposes to bridge the domain gap with an intermediate domain and progressively solve easier adaptation subtasks by adopting adversarial learning to align distributions at the feature level.
Unsupervised Visual Representation Learning by Graph-Based Consistent Constraints
TLDR
This paper proposes to use a cycle consistency criterion for mining positive pairs and geodesic distance in the graph for hard negative mining, and shows that the mined positive and negative image pairs can provide accurate supervisory signals for learning effective representations using Convolutional Neural Networks (CNNs).
Activity Recognition with sensors on mobile devices
TLDR
A mobile phone-based system that employs the accelerometer and the gyroscope signals for AR and shows that the features extracted from the Gyroscope enhance the classification accuracy in term of dynamic activities recognition such as walking and upstairs.
From Image Collections to Point Clouds With Self-Supervised Shape and Pose Networks
TLDR
A key novelty of the proposed technique is to impose 3D geometric reasoning into predicted 3D point clouds by rotating them with randomly sampled poses and then enforcing cycle consistency on both 3D reconstructions and poses.
Scene Parsing with Global Context Embedding
TLDR
This work trains a context network based on scene similarities to generate feature representations for global contexts and designs modules to embed the feature representations and the priors into the segmentation network as additional global context cues.
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