Search Results for author:
Found 34 papers, 8 papers with code
ALL-E: Aesthetics-guided Low-light Image Enhancement
In this paper, we propose a new paradigm, i. e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward.
Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy
We propose a binary stochastic learning algorithm that modifies all elementary neural network operations, by introducing (i) stochastic binarization of both the forwarding signals and the activation function derivatives, (ii) signed binarization of the backpropagating errors, and (iii) step-wised weight updates.
Conceptual Reinforcement Learning for Language-Conditioned Tasks
Recently, the language-conditioned policy is proposed to facilitate policy transfer through learning the joint representation of observation and text that catches the compact and invariant information across environments.
Ultra-low Precision Multiplication-free Training for Deep Neural Networks
The energy-efficient works try to decrease the precision of multiplication or replace the multiplication with energy-efficient operations such as addition or bitwise shift, to reduce the energy consumption of FP32 multiplications.
Evaluating the Efficacy of Skincare Product: A Realistic Short-Term Facial Pore Simulation
In this paper, we propose the first simulation model to reveal facial pore changes after using skincare products.
Towards High Performance One-Stage Human Pose Estimation
Making top-down human pose estimation method present both good performance and high efficiency is appealing.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning
To address this issue, we propose CDHRL, a causality-driven hierarchical reinforcement learning framework, leveraging a causality-driven discovery instead of a randomness-driven exploration to effectively build high-quality hierarchical structures in complicated environments.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
SaiT: Sparse Vision Transformers through Adaptive Token Pruning
Sparse adaptive image Transformer (SaiT) offers varying levels of model acceleration by merely changing the token sparsity on the fly.
MaiT: Leverage Attention Masks for More Efficient Image Transformers
Though image transformers have shown competitive results with convolutional neural networks in computer vision tasks, lacking inductive biases such as locality still poses problems in terms of model efficiency especially for embedded applications.
Automatic Facial Skin Feature Detection for Everyone
We present an automatic facial skin feature detection method that works across a variety of skin tones and age groups for selfies in the wild.
Semantically Contrastive Learning for Low-light Image Enhancement
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.
MaiT: integrating spatial locality into image transformers with attention masks
Moreover, attention masks regulate the training of attention maps, which facilitates the convergence and improves the accuracy of deeper transformers.
Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms
In this framework, the environment can be easily configured to realize all kinds of RL tasks in the mainstream research.
Deep learning based low-dose synchrotron radiation CT reconstruction
Synchrotron radiation sources are widely used in various fields, among which computed tomography (CT) is one of the most important.
Active Terahertz Imaging Dataset for Concealed Object Detection
Concealed object detection in Terahertz imaging is an urgent need for public security and counter-terrorism.
Product semantics translation from brain activity via adversarial learning
To modify a design semantic of a given product from personalised brain activity via adversarial learning, in this work, we propose a deep generative transformation model to modify product semantics from the brain signal.
Verifying Design through Generative Visualization of Neural Activities
A recurrent neural network is used as the encoder to learn latent representation from electroencephalogram (EEG) signals, recorded while subjects looked at 50 categories of images.
A General Framework for Revealing Human Mind with auto-encoding GANs
For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment.
Causal Factors, Benefits and Challenges of Test-Driven Development: Practitioner Perceptions
This report describes the experiences of one organization's adoption of Test Driven Development (TDD) practices as part of a medium-term software project employing Extreme Programming as a methodology.
Software Engineering
Conditional Local Convolution for Spatio-temporal Meteorological Forecasting
We further propose the distance and orientation scaling terms to reduce the impacts of irregular spatial distribution.
Structured Context Enhancement Network for Mouse Pose Estimation
However, quantifying mouse behaviours from videos or images remains a challenging problem, where pose estimation plays an important role in describing mouse behaviours.
Muti-view Mouse Social Behaviour Recognition with Deep Graphical Model
To address this problem, we here propose a novel multiview latent-attention and dynamic discriminative model that jointly learns view-specific and view-shared sub-structures, where the former captures unique dynamics of each view whilst the latter encodes the interaction between the views.
CANet: Context Aware Network for 3D Brain Glioma Segmentation
Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning.
Using the Split Bregman Algorithm to Solve the Self-repelling Snake Model
Preserving contour topology during image segmentation is useful in many practical scenarios.
Regularizing Semi-supervised Graph Convolutional Networks with a Manifold Smoothness Loss
Existing graph convolutional networks focus on the neighborhood aggregation scheme.
Cost-sensitive Boosting Pruning Trees for depression detection on Twitter
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year.
A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images
But a large number of useless raw images, limited data storage resource and poor transmission capability on satellites hinder our use of valuable images.
A Research and Strategy of Remote Sensing Image Denoising Algorithms
Most raw data download from satellites are useless, resulting in transmission waste, one solution is to process data directly on satellites, then only transmit the processed results to the ground.
A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images
Methods: In this paper, we proposed an automatic, end-to-end, multi-modality diagnosis framework, including segmentation, registration, feature generation and machine learning, to process the information of the striatum for the diagnosis of PD.
A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm
Euler's Elastica based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images.
Semi-supervised Learning on Graph with an Alternating Diffusion Process
Graph-based semi-supervised learning usually involves two separate stages, constructing an affinity graph and then propagating labels for transductive inference on the graph.
Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks
In this paper, we report a robust hierarchical synthesis reconstruction approach, where training data is pre-processed to separate the information on the domains where sampling biases are suspected.
Sparse Subspace Clustering via Diffusion Process
Subspace clustering refers to the problem of clustering high-dimensional data that lie in a union of low-dimensional subspaces.
