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Found 109 papers, 34 papers with code
A Flexible Recurrent Residual Pyramid Network for Video Frame Interpolation
Inspired by classical pyramid energy minimization optical flow algorithms, this paper proposes a recurrent residual pyramid network (RRPN) for video frame interpolation.
Rethinking Sentiment Style Transfer
To overcome this issue, we propose a graph-based method to extract attribute content and attribute-independent content from input sentences in the YELP dataset and IMDB dataset.
A Versatile Adaptive Curriculum Learning Framework for Task-oriented Dialogue Policy Learning
It supports evaluating the difficulty of dialogue tasks only using the learning experiences of dialogue policy and skip-level selection according to their learning needs to maximize the learning efficiency.
A Simple Yet Effective Corpus Construction Method for Chinese Sentence Compression
To remedy this shortcoming, we present a dependency-tree-based method to construct a Chinese corpus with 151k pairs of sentences and compression based on Chinese language-specific characteristics.
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack
“Explanations can increase the transparency of neural networks and make them more trustworthy.
Deformation measurement of a soil mixing retaining wall using terrestrial laser scanning
Retaining walls are often built to prevent excessive lateral movements of the ground surrounding an excavation site.
Dilation-Erosion for Single-Frame Supervised Temporal Action Localization
To balance the annotation labor and the granularity of supervision, single-frame annotation has been introduced in temporal action localization.
Life-long Learning for Multilingual Neural Machine Translation with Knowledge Distillation
To alleviate the CF, we investigate knowledge distillation based life-long learning methods.
Stereo Image Rain Removal via Dual-View Mutual Attention
Stereo images, containing left and right view images with disparity, are utilized in solving low-vision tasks recently, e. g., rain removal and super-resolution.
Boosting Semi-Supervised 3D Object Detection with Semi-Sampling
When training a teacher-student semi-supervised framework, we randomly select gt samples and pseudo samples to both labeled frames and unlabeled frames, making a strong data augmentation for them.
NASA: Neural Architecture Search and Acceleration for Hardware Inspired Hybrid Networks
Multiplication is arguably the most cost-dominant operation in modern deep neural networks (DNNs), limiting their achievable efficiency and thus more extensive deployment in resource-constrained applications.
ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design
Specifically, on the algorithm level, ViTCoD prunes and polarizes the attention maps to have either denser or sparser fixed patterns for regularizing two levels of workloads without hurting the accuracy, largely reducing the attention computations while leaving room for alleviating the remaining dominant data movements; on top of that, we further integrate a lightweight and learnable auto-encoder module to enable trading the dominant high-cost data movements for lower-cost computations.
Behavioral graph fraud detection in E-commerce
Our experiments show that embedding features learned from similarity based behavioral graph have achieved significant performance increase to the baseline fraud detection model in various business scenarios.
CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing
Leveraging supervised information can lead to superior retrieval performance in the image hashing domain but the performance degrades significantly without enough labeled data.
Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task
End-to-end text image translation (TIT), which aims at translating the source language embedded in images to the target language, has attracted intensive attention in recent research.
Video-Guided Curriculum Learning for Spoken Video Grounding
To rectify the discriminative phonemes and extract video-related information from noisy audio, we develop a novel video-guided curriculum learning (VGCL) during the audio pre-training process, which can make use of the vital visual perceptions to help understand the spoken language and suppress the external noise.
Learning an Efficient Multimodal Depth Completion Model
With the wide application of sparse ToF sensors in mobile devices, RGB image-guided sparse depth completion has attracted extensive attention recently, but still faces some problems.
Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic Segmentation
Generative methods are common approaches to minimizing the domain gap of aerial images which improves the performance of the downstream tasks, e. g., cross-domain semantic segmentation.
AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments
The task of argument mining aims to detect all possible argumentative components and identify their relationships automatically.
e-G2C: A 0.14-to-8.31 $μ$J/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM
This work presents the first silicon-validated dedicated EGM-to-ECG (G2C) processor, dubbed e-G2C, featuring continuous lightweight anomaly detection, event-driven coarse/precise conversion, and on-chip adaptation.
Turning to a Teacher for Timestamp Supervised Temporal Action Segmentation
To obtain more information to optimize the model, the existing method generated pseudo frame-wise labels iteratively based on the output of a segmentation model and the timestamp annotations.
AntPivot: Livestream Highlight Detection via Hierarchical Attention Mechanism
In recent days, streaming technology has greatly promoted the development in the field of livestream.
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection
Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inference with graph neural networks (GNNs), which is useful to catch multihop risk propagation in a transaction graph.
Age Minimization in Outdoor and Indoor Communications with Relay-aided Dual RIS
In this paper, we investigate an outdoor and indoor wireless communication network with the assistance of a novel relay-aided double-sided reconfigurable intelligent surface (RIS).
Indoor simultaneous localization and mapping based on fringe projection profilometry
Simultaneous Localization and Mapping (SLAM) plays an important role in outdoor and indoor applications ranging from autonomous driving to indoor robotics.
Modelling graph dynamics in fraud detection with "Attention"
At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions.
SS-SAM : Stochastic Scheduled Sharpness-Aware Minimization for Efficiently Training Deep Neural Networks
By driving optimizers to converge to flat minima, sharpness-aware minimization (SAM) has shown the power to improve the model generalization.
Spatio-temporal Gait Feature with Global Distance Alignment
In this paper, we try to enhance the discrimination of spatio-temporal gait features from two aspects: effective extraction of spatio-temporal gait features and reasonable refinement of extracted features.
Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning
In this paper, we propose an effective method to improve the model generalization by additionally penalizing the gradient norm of loss function during optimization.
ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation
The performance of a semantic segmentation model for remote sensing (RS) images pretrained on an annotated dataset would greatly decrease when testing on another unannotated dataset because of the domain gap.
Neighborhood Region Smoothing Regularization for Finding Flat Minima In Deep Neural Networks
NRS leverages the finding that models would benefit from converging to flat minima, and tries to regularize the neighborhood region in weight space to yield approximate outputs.
Equivalence between algorithmic instability and transition to replica symmetry breaking in perceptron learning systems
Binary perceptron is a fundamental model of supervised learning for the non-convex optimization, which is a root of the popular deep learning.
Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture
Transaction checkout fraud detection is an essential risk control components for E-commerce marketplaces.
TransTCN: An Attention-based TCN Framework for Sequential Modeling
The model was evaluated across a wide range of the tasks in time series, which are commonly used to the benchmark of TCN and recurrent networks.
D$^2$-GCN: Data-Dependent GCNs for Boosting Both Efficiency and Scalability
Graph Convolutional Networks (GCNs) have gained an increasing attention thanks to their state-of-the-art (SOTA) performance in graph-based learning tasks.
Rethinking Deep Face Restoration
While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features and cannot authentically reconstruct the faces.
Multi-frame Joint Enhancement for Early Interlaced Videos
Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality.
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency
The recent breakthroughs of deep neural networks (DNNs) and the advent of billions of Internet of Things (IoT) devices have excited an explosive demand for intelligent IoT devices equipped with domain-specific DNN accelerators.
EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation
The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e. g., diabetic, hypertensive and solar retinopathies.
Cascaded Prediction Network via Segment Tree for Temporal Video Grounding
Temporal video grounding aims to localize the target segment which is semantically aligned with the given sentence in an untrimmed video.
Multi-Scale Context Aggregation Network with Attention-Guided for Crowd Counting
In this paper, we propose a multi-scale context aggregation network (MSCANet) based on single-column encoder-decoder architecture for crowd counting, which consists of an encoder based on a dense context-aware module (DCAM) and a hierarchical attention-guided decoder.
Estimating the Generalization in Deep Neural Networks via Sparsity
By training DNNs with a wide range of generalization gap on popular datasets, we show that our key quantities and linear model could be efficient tools for estimating the generalization gap of DNNs.
Super-Resolving Compressed Video in Coding Chain
Scaling and lossy coding are widely used in video transmission and storage.
HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark
To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance of all the networks in the search spaces of both NAS-Bench-201 and FBNet, on six hardware devices that fall into three categories (i. e., commercial edge devices, FPGA, and ASIC).
Hardware Aware Neural Architecture Search
Neural Architecture Search
Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation
We show that by investigating the feature entropy of units on only training data, it could give discrimination between networks with different generalization ability from the view of the effectiveness of feature representations.
Interaction between optical pulse and tumor using finite element analysis
As a non-destructive biological tissue imaging technology, photoacoustic imaging has important application value in the field of biomedicine.
Biological Physics
Waveform and Beamforming Design for Intelligent Reflecting Surface Aided Wireless Power Transfer: Single-User and Multi-User Solutions
This paper highlights the fact that IRS can provide an extra passive beamforming gain on output DC power over conventional WPT designs and significantly influence the waveform design by leveraging the benefit of passive beamforming, frequency diversity and energy harvester nonlinearity.
Information Theory Signal Processing Information Theory
SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training
Results show that: 1) applied to inference, SD achieves up to 2. 44x energy efficiency as evaluated via real hardware implementations; 2) applied to training, SD leads to 10. 56x and 4. 48x reduction in the storage and training energy, with negligible accuracy loss compared to state-of-the-art training baselines.
SDA: Improving Text Generation with Self Data Augmentation
Data augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision.
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark
To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance (e. g., energy cost and latency) of all the networks in the search space of both NAS-Bench-201 and FBNet, considering six hardware devices that fall into three categories (i. e., commercial edge devices, FPGA, and ASIC).
Hardware Aware Neural Architecture Search
Neural Architecture Search
Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling
Energy-based models (EBMs) for generative modeling parametrize a single net and can be directly trained by maximum likelihood estimation.
Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance
The proposed UFG image dataset and evaluation protocols is intended to serve as a benchmark platform that can advance research of visual classification from approaching human performance to beyond human ability, via facilitating benchmark data of artificial intelligence (AI) not to be limited by the labels of human intelligence (HI).
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such systems remains a challenge due to the limited resources available at the edge and the required massive training costs for state-of-the-art (SOTA) DNNs.
A Comprehensive Survey of 6G Wireless Communications
First, we give an overview of 6G from perspectives of technologies, security and privacy, and applications.
Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks
Massive account registration has raised concerns on risk management in e-commerce companies, especially when registration increases rapidly within a short time frame.
IRS-Aided SWIPT: Joint Waveform, Active and Passive Beamforming Design Under Nonlinear Harvester Model
To facilitate practical implementation, we also propose a low-complexity design based on closed-form adaptive waveform schemes.
Information Theory Signal Processing Information Theory
ReMP: Rectified Metric Propagation for Few-Shot Learning
Few-shot learning features the capability of generalizing from a few examples.
Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity
Previous studies combining knowledge graph (KG) with neural machine translation (NMT) have two problems: i) Knowledge under-utilization: they only focus on the entities that appear in both KG and training sentence pairs, making much knowledge in KG unable to be fully utilized.
Unpaired Image-to-Image Translation via Latent Energy Transport
Instead of explicitly extracting the two codes and applying adaptive instance normalization to combine them, our latent EBM can implicitly learn to transport the source style code to the target style code while preserving the content code, an advantage over existing image translation methods.
Rethinking deinterlacing for early interlaced videos
Traditional deinterlacing approaches are mainly focused on early interlacing scanning systems and thus cannot handle the complex and complicated artifacts in real-world early interlaced videos.
xFraud: Explainable Fraud Transaction Detection
At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss.
A topological approach to exploring convolutional neural networks
The first one reveals the topological essence of CNN filters.
Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems
For instance, specificity is mandatory in a food-ordering dialogue task, whereas fluency is preferred in a language-teaching dialogue system.
Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning
Specifically, we introduce a selection module that is independent of the translation module to score each candidate context sentence.
Q-learning with Language Model for Edit-based Unsupervised Summarization
Q-learning is leveraged to train the agent to produce proper edit actions.
FaultNet: A Deep Convolutional Neural Network for bearing fault classification
In this work, we analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods and coupling them with machine learning techniques to classify different types of bearing faults.
Ranked #1 on
Classification
on CWRU Bearing Dataset
Structure-Aware Human-Action Generation
Generating long-range skeleton-based human actions has been a challenging problem since small deviations of one frame can cause a malformed action sequence.
Ranked #2 on
Human action generation
on NTU RGB+D 2D
CASIA's System for IWSLT 2020 Open Domain Translation
This paper describes the CASIA{'}s system for the IWSLT 2020 open domain translation task.
Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients
In a case where the aggregator is untrusted and LDP is not applied to each user gradient, the aggregator can recover sensitive user data from these gradients.
SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation
We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep neural networks (DNNs).
TIMELY: Pushing Data Movements and Interfaces in PIM Accelerators Towards Local and in Time Domain
Resistive-random-access-memory (ReRAM) based processing-in-memory (R$^2$PIM) accelerators show promise in bridging the gap between Internet of Thing devices' constrained resources and Convolutional/Deep Neural Networks' (CNNs/DNNs') prohibitive energy cost.
Bayesian Meta Sampling for Fast Uncertainty Adaptation
Specifically, we propose a Bayesian meta sampling framework consisting of two main components: a meta sampler and a sample adapter.
Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck
Variational autoencoders (VAEs) are essential tools in end-to-end representation learning.
Local Differential Privacy based Federated Learning for Internet of Things
To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model.
Feature Quantization Improves GAN Training
The instability in GAN training has been a long-standing problem despite remarkable research efforts.
Ranked #1 on
Image-to-Image Translation
on anime-to-selfie
Dual-discriminator GAN: A GAN way of profile face recognition
A wealth of angle problems occur when facial recognition is performed: At present, the feature extraction network presents eigenvectors with large differences between the frontal face and profile face recognition of the same person in many cases.
A New MRAM-based Process In-Memory Accelerator for Efficient Neural Network Training with Floating Point Precision
The excellent performance of modern deep neural networks (DNNs) comes at an often prohibitive training cost, limiting the rapid development of DNN innovations and raising various environmental concerns.
DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architectures
The recent breakthroughs in deep neural networks (DNNs) have spurred a tremendously increased demand for DNN accelerators.
Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences
In this paper, we consider a novel task, Spatio-Temporal Video Grounding for Multi-Form Sentences (STVG).
AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs
Specifically, AutoDNNchip consists of two integrated enablers: (1) a Chip Predictor, built on top of a graph-based accelerator representation, which can accurately and efficiently predict a DNN accelerator's energy, throughput, and area based on the DNN model parameters, hardware configuration, technology-based IPs, and platform constraints; and (2) a Chip Builder, which can automatically explore the design space of DNN chips (including IP selection, block configuration, resource balancing, etc.
Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions
In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality.
Ranked #4 on
Human action generation
on NTU RGB+D 2D
Patchy Image Structure Classification Using Multi-Orientation Region Transform
To address above limitations, this paper proposes a novel Multi-Orientation Region Transform (MORT), which can effectively characterize both contour and structure features simultaneously, for patchy image structure classification.
A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation
To build connections among prediction branches, this paper introduces gradient boosting framework to deep classification model and proposes a gradient boosting network called BoostNet.
Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator
Pointer Generators have been the de facto standard for modern summarization systems.
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings
Extensive simulations and ablation studies, with real energy measurements from an FPGA board, confirm the superiority of our proposed strategies and demonstrate remarkable energy savings for training.
DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition
Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.
Deep High-Resolution Representation Learning for Visual Recognition
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.
Ranked #1 on
Object Detection
on COCO test-dev
(Hardware Burden metric)
MobileFAN: Transferring Deep Hidden Representation for Face Alignment
To this end, we propose an effective lightweight model, namely Mobile Face Alignment Network (MobileFAN), using a simple backbone MobileNetV2 as the encoder and three deconvolutional layers as the decoder.
Unsupervised Rewriter for Multi-Sentence Compression
First, the word graph approach that simply concatenates fragments from multiple sentences may yield non-fluent or ungrammatical compression.
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data.
High-Resolution Representations for Labeling Pixels and Regions
The proposed approach achieves superior results to existing single-model networks on COCO object detection.
Ranked #4 on
Semantic Segmentation
on LIP val
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild
3D face reconstruction from a single 2D image is a challenging problem with broad applications.
Ranked #7 on
Face Alignment
on AFLW2000-3D
Synchronous Bidirectional Inference for Neural Sequence Generation
In this work, we propose a synchronous bidirectional inference model to generate outputs using both left-to-right and right-to-left decoding simultaneously and interactively.
Self-Adversarially Learned Bayesian Sampling
Scalable Bayesian sampling is playing an important role in modern machine learning, especially in the fast-developed unsupervised-(deep)-learning models.
Variance Reduction in Stochastic Particle-Optimization Sampling
Stochastic particle-optimization sampling (SPOS) is a recently-developed scalable Bayesian sampling framework that unifies stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) algorithms based on Wasserstein gradient flows.
EnergyNet: Energy-Efficient Dynamic Inference
The prohibitive energy cost of running high-performance Convolutional Neural Networks (CNNs) has been limiting their deployment on resource-constrained platforms including mobile and wearable devices.
Addressing Troublesome Words in Neural Machine Translation
One of the weaknesses of Neural Machine Translation (NMT) is in handling lowfrequency and ambiguous words, which we refer as troublesome words.
Random Occlusion-recovery for Person Re-identification
As a basic task of multi-camera surveillance system, person re-identification aims to re-identify a query pedestrian observed from non-overlapping multiple cameras or across different time with a single camera.
A Language Model based Evaluator for Sentence Compression
We herein present a language-model-based evaluator for deletion-based sentence compression and view this task as a series of deletion-and-evaluation operations using the evaluator.
Ranked #2 on
Sentence Compression
on Google Dataset
Multispectral Image Intrinsic Decomposition via Subspace Constraint
In this paper, a new Multispectral Image Intrinsic Decomposition model (MIID) is presented to decompose the shading and reflectance from a single multispectral image.
Phrase Table as Recommendation Memory for Neural Machine Translation
Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently.
Multispectral Image Intrinsic Decomposition via Low Rank Constraint
In this paper, a Low Rank Multispectral Image Intrinsic Decomposition model (LRIID) is presented to decompose the shading and reflectance from a single multispectral image.
Towards Neural Machine Translation with Partially Aligned Corpora
While neural machine translation (NMT) has become the new paradigm, the parameter optimization requires large-scale parallel data which is scarce in many domains and language pairs.
Automatic Spatially-aware Fashion Concept Discovery
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites.
A Conditional Variational Framework for Dialog Generation
Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems.
GUN: Gradual Upsampling Network for Single Image Super-Resolution
The GUN consists of an input layer, multiple upsampling and convolutional layers, and an output layer.
Local Patch Encoding-Based Method for Single Image Super-Resolution
The proposed method consists of a learning stage and a reconstructing stage.
Parallel Spectral Clustering Algorithm Based on Hadoop
Spectral clustering and cloud computing is emerging branch of computer science or related discipline.
