Search Results for author:
Found 15 papers, 6 papers with code
Mci-net: multi-scale context integrated network for liver ct image segmentation
Owing to the various object scales and high similarity with the surrounding organs (e. g., kidney, stomach, and spleen), it is difficult to accurately segment the liver region from the abdominal computed tomography images.
Mastering Asymmetrical Multiplayer Game with Multi-Agent Asymmetric-Evolution Reinforcement Learning
We propose asymmetric-evolution training (AET), a novel multi-agent reinforcement learning framework that can train multiple kinds of agents simultaneously in AMP game.
Learning Analytical Posterior Probability for Human Mesh Recovery
Despite various probabilistic methods for modeling the uncertainty and ambiguity in human mesh recovery, their overall precision is limited because existing formulations for joint rotations are either not constrained to SO(3) or difficult to learn for neural networks.
CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector Graphics
Considerable progress has recently been made in leveraging CLIP (Contrastive Language-Image Pre-Training) models for text-guided image manipulation.
Underwater Ranker: Learn Which Is Better and How to Be Better
To achieve that, we also contribute a dataset, URankerSet, containing sufficient results enhanced by different UIE algorithms and the corresponding perceptual rankings, to train our URanker.
LookinGood^π: Real-time Person-independent Neural Re-rendering for High-quality Human Performance Capture
The guidance of the rendered image is realized by blending features from two branches effectively in the training of the detail branch, which improves both the warping accuracy and the details' fidelity.
SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation
For unsupervised image-to-image translation, we propose a discriminator architecture which focuses on the statistical features instead of individual patches.
A Deep Graph Neural Networks Architecture Design: From Global Pyramid-like Shrinkage Skeleton to Local Link Rewiring
Expressivity plays a fundamental role in evaluating deep neural networks, and it is closely related to understanding the limit of performance improvement.
Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge
WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda University, etc.
Soft policy optimization using dual-track advantage estimator
In reinforcement learning (RL), we always expect the agent to explore as many states as possible in the initial stage of training and exploit the explored information in the subsequent stage to discover the most returnable trajectory.
GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes
The task of room layout estimation is to locate the wall-floor, wall-ceiling, and wall-wall boundaries.
SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration
Dialogue systems in open domain have achieved great success due to the easily obtained single-turn corpus and the development of deep learning, but the multi-turn scenario is still a challenge because of the frequent coreference and information omission.
Ranked #1 on
Dialogue Rewriting
on Multi-Rewrite
(BLEU-1 metric)
PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation
This linear model is then utilized to reduce the redundant information in the left and right road images.
The Expressivity and Training of Deep Neural Networks: toward the Edge of Chaos?
In this paper, we provide a quantitative analysis of the expressivity for the deep neural network (DNN) from its dynamic model, where the Hilbert space is employed to analyze the convergence and criticality.
Edge-Semantic Learning Strategy for Layout Estimation in Indoor Environment
More specifically, we present an encoder-decoder network with shared encoder and two separate decoders, which are composed of multiple deconvolution (transposed convolution) layers, to jointly learn the edge maps and semantic labels of a room image.
