Search Results for author:
Found 63 papers, 29 papers with code
Single Image Super-resolution using Deformable Patches
We proposed a deformable patches based method for single image super-resolution.
Fusing multiple features for depth-based action recognition
The experiments are conducted on four challenging depth action databases, in order to evaluate and find the best fusion methods generally.
Region-Based Temporally Consistent Video Post-Processing
We study the problem of temporally consistent video post-processing.
Modeling Deformable Gradient Compositions for Single-Image Super-Resolution
Based on the fact that singular primitive patches are more invariant to the scale change (i. e. have less ambiguity across different scales), we represent the non-singular primitives as compositions of singular ones, each of which is allowed some deformation.
Fidelity-Naturalness Evaluation of Single Image Super Resolution
In this paper, we proposed to use both fidelity (the difference with original images) and naturalness (human visual perception of super resolved images) for evaluation.
ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition
In this paper, we introduce a new and challenging large-scale food image dataset called "ChineseFoodNet", which aims to automatically recognizing pictured Chinese dishes.
A Novel Co-design Peta-scale Heterogeneous Cluster for Deep Learning Training
Therefore, MiMatrix intrinsically solves the bandwidth bottleneck of central node in parameter server framework that is widely used in distributed DL tasks.
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
Slot filling is a critical task in natural language understanding (NLU) for dialog systems.
A Brand-level Ranking System with the Customized Attention-GRU Model
In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is an indicator of quality.
Addressing the Item Cold-start Problem by Attribute-driven Active Learning
Both content information (e. g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new item.
Hybrid Beamforming for Millimeter Wave Systems Using the MMSE Criterion
A particular innovation in our proposed alternating minimization algorithms is a carefully designed initialization method, which leads to faster convergence.
Information Theory Information Theory
Hybrid beamforming for single carrier mmWave MIMO systems
Hybrid analog and digital beamforming (HBF) has been recognized as an attractive technique offering a tradeoff between hardware implementation limitation and system performance for future broadband millimeter wave (mmWave) communications.
Signal Processing
Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning
Beamforming (BF) design for large-scale antenna arrays with limited radio frequency chains and the phase-shifter-based analog BF architecture, has been recognized as a key issue in millimeter wave communication systems.
Information Theory Information Theory
Deep Session Interest Network for Click-Through Rate Prediction
Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt, Deep Session Interest Network(DSIN)
Exact-K Recommendation via Maximal Clique Optimization
This paper targets to a novel but practical recommendation problem named exact-K recommendation.
Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU
A novel query-based interactive recommender system is proposed in this paper, where \textbf{personalized questions are accurately generated from millions of automatically constructed questions} in Step 1, and \textbf{the recommendation is ensured to be closely-related to users' feedback} in Step 2.
SESF-Fuse: An Unsupervised Deep Model for Multi-Focus Image Fusion
In this work, we propose a novel unsupervised deep learning model to address multi-focus image fusion problem.
Attention-based network for low-light image enhancement
Extensive experiments demonstrate the superiority of the proposed network in terms of suppressing the chromatic aberration and noise artifacts in enhancement, especially when the low-light image has severe noise.
GINet: Graph Interaction Network for Scene Parsing
GI unit is further improved by the SC-loss to enhance the semantic representations over the exemplar-based semantic graph.
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
AIM 2020: Scene Relighting and Illumination Estimation Challenge
The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i. e., light source position).
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation
Based on this framework, we enhance the local feature representation by sampling and feeding the point-based features that locate on the semantic edges to an individual Semantic-guided Edge Enhancement module (SEEM), which is specifically designed for promoting depth estimation on the challenging semantic borders.
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction
Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge.
Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond
In the context of simplicial complexes, we specifically focus on signal processing using the Hodge Laplacian matrix, a multi-relational operator that leverages the special structure of simplicial complexes and generalizes desirable properties of the Laplacian matrix in graph signal processing.
Non-uniform Motion Deblurring with Blurry Component Divided Guidance
To tackle the above problems, we present a deep two-branch network to deal with blurry images via a component divided module, which divides an image into two components based on the representation of blurry degree.
Joint Active and Passive Beamforming for Intelligent Reflecting Surface Aided Multiuser MIMO Communications
This letter investigates the joint active and passive beamforming optimization for intelligent reflecting surface (IRS) aided multiuser multiple-input multiple-output systems with the objective of maximizing the weighted sum-rate.
Information Theory Information Theory
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance
While the mappings between image and pixel-wise depth are well-studied in current methods, the correlation between image, depth and scene semantics, however, is less considered.
Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning
We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs).
Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling
On the other hand, we propose a matrix factorization method based on a loss function that generalizes that of the skip-gram model with negative sampling to arbitrary similarity matrices.
An Impossibility Theorem for Node Embedding
With the increasing popularity of graph-based methods for dimensionality reduction and representation learning, node embedding functions have become important objects of study in the literature.
Signal processing on simplicial complexes
Higher-order networks have so far been considered primarily in the context of studying the structure of complex systems, i. e., the higher-order or multi-way relations connecting the constituent entities.
LAE : Long-tailed Age Estimation
To improve the performance of facial age estimation, we first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on.
Exploring and Evaluating Image Restoration Potential in Dynamic Scenes
In this paper, to better study an image's potential value that can be explored for restoration, we propose a novel concept, referring to image restoration potential (IRP).
Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition
To be specific, the universal features denote the general characteristic of facial emotions within a period and the unique features denote the specific characteristic at this moment.
Facial Expression Recognition
Facial Expression Recognition (FER)
SKFlow: Learning Optical Flow with Super Kernels
In this paper, we propose the Super Kernel Flow Network (SKFlow), a CNN architecture to ameliorate the impacts of occlusions on optical flow estimation.
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model
An accurate computational model for image quality assessment (IQA) benefits many vision applications, such as image filtering, image processing, and image generation.
SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision
Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications.
Hypergraphs with Edge-Dependent Vertex Weights: p-Laplacians and Spectral Clustering
We study p-Laplacians and spectral clustering for a recently proposed hypergraph model that incorporates edge-dependent vertex weights (EDVW).
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
AIM 2022 Challenge on Instagram Filter Removal: Methods and Results
This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal.
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
AI of Brain and Cognitive Sciences: From the Perspective of First Principles
Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation.
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images.
Take a Prior from Other Tasks for Severe Blur Removal
For general scene deblurring, the feature space of the blurry image and corresponding sharp image under the high-level vision task is closer, which inspires us to rely on other tasks (e. g. classification) to learn a comprehensive prior in severe blur removal cases.
GRAN: Ghost Residual Attention Network for Single Image Super Resolution
GRAB consists of the Ghost Module and Channel and Spatial Attention Module (CSAM) to alleviate the generation of redundant features.
SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders
In this work, we propose a novel semi-supervised approach to realize few-shot HDR imaging via two stages of training, called SSHDR.
A Unified HDR Imaging Method with Pixel and Patch Level
The proposed HyHDRNet consists of a content alignment subnetwork and a Transformer-based fusion subnetwork.
Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes
To let the geometric perception learned from multi-view cues in static areas propagate to the monocular representation in dynamic areas and let monocular cues enhance the representation of multi-view cost volume, we propose a cross-cue fusion (CCF) module, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other.
Hypergraphs with Edge-Dependent Vertex Weights: Spectral Clustering based on the 1-Laplacian
We propose a flexible framework for defining the 1-Laplacian of a hypergraph that incorporates edge-dependent vertex weights.
Co-design Hardware and Algorithm for Vector Search
Vector search has emerged as the foundation for large-scale information retrieval and machine learning systems, with search engines like Google and Bing processing tens of thousands of queries per second on petabyte-scale document datasets by evaluating vector similarities between encoded query texts and web documents.
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution
To speed up inference and further enhance the performance, our research revisits diffusion models in image super-resolution and proposes a straightforward yet significant diffusion model-based super-resolution method called ACDMSR (accelerated conditional diffusion model for image super-resolution).
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation
To address this issue, we propose a novel All-in-one Multi-degradation Image Restoration Network (AMIRNet) that can effectively capture and utilize accurate degradation representation for image restoration.
Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration
Semi-implicit variational inference (SIVI) has been introduced to expand the analytical variational families by defining expressive semi-implicit distributions in a hierarchical manner.
Towards High-quality HDR Deghosting with Conditional Diffusion Models
To address this challenge, we formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition, consisting of the feature condition generator and the noise predictor.
Multiple Object Tracking based on Occlusion-Aware Embedding Consistency Learning
The OPM predicts occlusion information for each true detection, facilitating the selection of valid samples for consistency learning of the track's visual embedding.
Time-Optimal Control for High-Order Chain-of-Integrators Systems with Full State Constraints and Arbitrary Terminal States (Extended Version)
Time-optimal control for high-order chain-of-integrators systems with full state constraints and arbitrarily given terminal states remains a challenging problem in the optimal control theory domain, yet to be resolved.
ACCL+: an FPGA-Based Collective Engine for Distributed Applications
To facilitate the development of distributed applications with FPGAs, in this paper we propose ACCL+, an open-source versatile FPGA-based collective communication library.
Memory-Efficient Prompt Tuning for Incremental Histopathology Classification
Recent studies have made remarkable progress in histopathology classification.
Boosting Multi-view Stereo with Late Cost Aggregation
To address this challenge, we present a late aggregation approach that allows for aggregating pairwise costs throughout the network feed-forward process, achieving accurate estimations with only minor changes of the plain CasMVSNet.
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy
Reinforcement Learning from Human Feedback (RLHF) is the prevailing approach to ensure Large Language Models (LLMs) align with human values.
GoMVS: Geometrically Consistent Cost Aggregation for Multi-View Stereo
More specifically, we correspond and propagate adjacent costs to the reference pixel by leveraging the local geometric smoothness in conjunction with surface normals.
人文社科学术论文语言变异的多维度分析(A multi-dimensional analysis of register variations in Chinese academic papers of Humanities and Social Sciences)
“通过自建人文和社科领域中文学术期刊论文语料库(逾920万字), 运用多维度分析法对111项语言特征的频次数据进行因子分析和维度识别, 发现人文社科领域学术论文具有7个维度的语言特征共现模式:描述性vs. 阐释性、概念判断vs. 行为再现、铺陈与发展、已然性表述、计数与测量、模糊性表达、顺序与连接。进而, 对语料在上述各维度的量化表现施以统计检验和聚类分析, 发现学术语体在人文与社科两大领域的语言变异显著体现于除“计数与测量”、“顺序与连接”以外的其他5个维度;人文领域和社科领域内部学科的语言变异, 各在6个维度上存在显著差异。本研究为学术汉语写作、汉语语体语法等提供一定启示。”
