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Recent advances in convolutional neural networks
TLDR
This paper details the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation, and introduces various applications of convolutional neural networks in computer vision, speech and natural language processing. Expand
Joint source channel rate-distortion analysis for adaptive mode selection and rate control in wireless video coding
TLDR
An analytic solution for adaptive intra mode selection and joint source-channel rate control under time-varying wireless channel conditions is derived and significantly improves the end-to-end video quality in wireless video coding and transmission. Expand
Pluralistic Image Completion
TLDR
This paper proposes a novel and probabilistically principled framework with two parallel paths for pluralistic image completion that utilizes the only one given ground truth to get prior distribution of missing parts and rebuild the original image from this distribution. Expand
Auto-Encoding Scene Graphs for Image Captioning
TLDR
This work proposes Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions and validates the effectiveness of SGAE on the challenging MS-COCO image captioned benchmark. Expand
3D Hand Shape and Pose Estimation From a Single RGB Image
TLDR
This work proposes a Graph Convolutional Neural Network (Graph CNN) based method to reconstruct a full 3D mesh of hand surface that contains richer information of both 3D hand shape and pose and proposes a weakly-supervised approach by leveraging the depth map as a weak supervision in training. Expand
Weakly-Supervised 3D Hand Pose Estimation from Monocular RGB Images
TLDR
A weakly-supervised method, adaptating from fully-annotated synthetic dataset toWeakly-labeled real-world dataset with the aid of a depth regularizer, which generates depth maps from predicted 3D pose and serves as weak supervision for3D pose regression. Expand
Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment
TLDR
A novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before, in which multi-scale shared features are learned firstly, and high-level features of face alignment are fed into AU detection. Expand
Exploiting Spatial-Temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks
TLDR
A novel graph-based method to tackle the problem of 3D human body and 3D hand pose estimation from a short sequence of 2D joint detections, where domain knowledge about the human hand (body) configurations is explicitly incorporated into the graph convolutional operations to meet the specific demand of the 3D pose estimation. Expand
T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks
TLDR
A framework that comprises an image translation network for enhancing realism of input images, followed by a depth prediction network that can be trained end-to-end, leading to good results, even surpassing early deep-learning methods that use real paired data. Expand
User-Friendly Interactive Image Segmentation Through Unified Combinatorial User Inputs
TLDR
This paper proposes a constrained random walks algorithm that facilitates the use of three types of user inputs: 1) foreground and background seed input, 2) soft constraint input, and 3) hard constraintinput, as well as their combinations. Expand
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