Search Results for author:
Found 236 papers, 56 papers with code
Recognizing Reduplicated Forms: Finite-State Buffered Machines
Total reduplication is common in natural language phonology and morphology.
Geometric Estimation via Robust Subspace Recovery
Geometric estimation from image point correspondences is the core procedure of many 3D vision problems, which is prevalently accomplished by random sampling techniques.
FG-NeRF: Flow-GAN based Probabilistic Neural Radiance Field for Independence-Assumption-Free Uncertainty Estimation
Existing works rely on the independence assumption of points in the radiance field or the pixels in input views to obtain tractable forms of the probability density function.
Audio Contrastive based Fine-tuning
Audio classification plays a crucial role in speech and sound processing tasks with a wide range of applications.
Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning
This could be sub-optimal since it is difficult for the same model to handle all the variations during testing.
Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learning
During meta-testing, the trained model is fine-tuned with a few gradient updates to produce a unique set of network parameters for each test instance.
Building explainable graph neural network by sparse learning for the drug-protein binding prediction
Due to the use of the chemical-substructure-based graph, it is guaranteed that any subgraphs in a drug identified by our SLGNN are chemically valid structures.
Efficient Learned Lossless JPEG Recompression
Second, we propose pipeline parallel context model (PPCM) and compressed checkerboard context model (CCCM) for the effective conditional modeling and efficient decoding within luma and chroma components.
Attention-Based Acoustic Feature Fusion Network for Depression Detection
To rectify this, we present the novel Attention-Based Acoustic Feature Fusion Network (ABAFnet) for depression detection.
MetaGCD: Learning to Continually Learn in Generalized Category Discovery
The goal is to continually discover novel classes while maintaining the performance in known classes.
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field
Despite Graph Neural Networks demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with over-fitting and over-smoothing as they go deeper as models of computer vision realm.
ARAI-MVSNet: A multi-view stereo depth estimation network with adaptive depth range and depth interval
Moreover, our method also achieves the lowest $e_{1}$ and $e_{3}$ on the BlendedMVS dataset and the highest Acc and $F_{1}$-score on the ETH 3D dataset, surpassing all listed methods. Project website: https://github. com/zs670980918/ARAI-MVSNet
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank
Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases.
Towards Robust Probabilistic Modeling on SO(3) via Rotation Laplace Distribution
With this benefit, we demonstrate its advantages in semi-supervised rotation regression, where the pseudo labels are noisy.
Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition
In this paper, we treat each image as a bag of instances, and formulate the task of multi-label image recognition as an instance-label matching selection problem.
Inferring High-level Geographical Concepts via Knowledge Graph and Multi-scale Data Integration: A Case Study of C-shaped Building Pattern Recognition
To improve efficiency and effectiveness, we integrate multi-scale data using a knowledge graph, focusing on the recognition of C-shaped building patterns.
Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling
Normalizing flows (NFs) provide a powerful tool to construct an expressive distribution by a sequence of trackable transformations of a base distribution and form a probabilistic model of underlying data.
Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection
The ABRSI achieves fine-grained intrusion knowledge transfer via adaptive bi-recommendation matching.
E-MLB: Multilevel Benchmark for Event-Based Camera Denoising
Event cameras, such as dynamic vision sensors (DVS), are biologically inspired vision sensors that have advanced over conventional cameras in high dynamic range, low latency and low power consumption, showing great application potential in many fields.
Adaptive Data-Free Quantization
Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i. e., informative or not to the learning process of Q, resulting into the overflow of generalization error.
A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation
Rotation Laplace distribution is robust to the disturbance of outliers and enforces much gradient to the low-error region, resulting in a better convergence.
Rethinking Data-Free Quantization as a Zero-Sum Game
how to generate the samples with desirable adaptability to benefit the quantized network?
Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis
The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details.
LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup
On-device Deep Neural Network (DNN) inference consumes significant computing resources and development efforts.
Convergence Analysis of the Deep Galerkin Method for Weak Solutions
This paper analyzes the convergence rate of a deep Galerkin method for the weak solution (DGMW) of second-order elliptic partial differential equations on $\mathbb{R}^d$ with Dirichlet, Neumann, and Robin boundary conditions, respectively.
Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective
In this paper, with extensive and deep-going experiments, we comprehensively analyze existing spatiotemporal graph learning models and reveal that extracting adjacency matrices with carefully design strategies, which are viewed as the key of enhancing performance on graph learning, are largely ineffective.
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach
In this paper, a Geometric Graph Alignment (GGA) approach is leveraged to mask the geometric heterogeneities between domains for better intrusion knowledge transfer.
Decoupling-and-Aggregating for Image Exposure Correction
To this end, we inject the addition/difference operation into the convolution process and devise a Contrast Aware (CA) unit and a Detail Aware (DA) unit to facilitate the statistical and structural regularities modeling.
Domain Generalization by Learning and Removing Domain-specific Features
With the images output by the encoder-decoder network, another classifier is designed to learn the domain-invariant features to conduct image classification.
Ranked #14 on
Domain Generalization
on PACS
An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases
This paper presents the first study of the interpretability and imbalanced semi-supervised learning of the multiclass intelligent skin diagnosis framework (ISDL) using 58, 457 skin images with 10, 857 unlabeled samples.
Multi-Scenario Bimetric-Balanced IoT Resource Allocation: An Evolutionary Approach
In this paper, we allocate IoT devices as resources for smart services with time-constrained resource requirements.
A Joint Framework Towards Class-aware and Class-agnostic Alignment for Few-shot Segmentation
Few-shot segmentation (FSS) aims to segment objects of unseen classes given only a few annotated support images.
Joint Semantic Transfer Network for IoT Intrusion Detection
The scenario semantic endows source NI and II domain with characteristics from each other to ease the knowledge transfer process via a confused domain discriminator and categorical distribution knowledge preservation.
Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oracles
We study the stochastic contextual bandit with knapsacks (CBwK) problem, where each action, taken upon a context, not only leads to a random reward but also costs a random resource consumption in a vector form.
Video Summarization Overview
With the broad growth of video capturing devices and applications on the web, it is more demanding to provide desired video content for users efficiently.
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation
In this paper, we propose task-specific meta distillation that simultaneously learns two models in meta-learning: a large teacher model and a small student model.
FAQS: Communication-efficient Federate DNN Architecture and Quantization Co-Search for personalized Hardware-aware Preferences
Due to user privacy and regulatory restrictions, federate learning (FL) is proposed as a distributed learning framework for training deep neural networks (DNN) on decentralized data clients.
HQNAS: Auto CNN deployment framework for joint quantization and architecture search
Deep learning applications are being transferred from the cloud to edge with the rapid development of embedded computing systems.
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
Most existing methods perform training on multiple source domains using a single model, and the same trained model is used on all unseen target domains.
Ranked #20 on
Domain Generalization
on DomainNet
Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method
In this paper, we propose and solve a new problem called hierarchical few-shot object detection (Hi-FSOD), which aims to detect objects with hierarchical categories in the FSOD paradigm.
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022
In this work, we present the winning solution for ORBIT Few-Shot Video Object Recognition Challenge 2022.
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism.
Delving Globally into Texture and Structure for Image Inpainting
Our model is orthogonal to the fashionable arts, such as Convolutional Neural Networks (CNNs), Attention and Transformer model, from the perspective of texture and structure information for image inpainting.
Switchable Online Knowledge Distillation
Instead of focusing on the accuracy gap at test phase by the existing arts, the core idea of SwitOKD is to adaptively calibrate the gap at training phase, namely distillation gap, via a switching strategy between two modes -- expert mode (pause the teacher while keep the student learning) and learning mode (restart the teacher).
Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics
To infer region-wise proximity under flexible factor-wise combinations and enable dynamic neighborhood aggregations, we further disentangle compounded influences of exogenous factors on region-wise proximity and learn to aggregate them.
Grasping Core Rules of Time Series through Pure Models
Time series underwent the transition from statistics to deep learning, as did many other machine learning fields.
Instance Image Retrieval by Learning Purely From Within the Dataset
Although achieving promising results, this approach is restricted by two issues: 1) the domain gap between benchmark datasets and the dataset of a given retrieval task; 2) the required auxiliary dataset cannot be readily obtained.
Trajectory Tracking Control of the Bionic Joint Actuated by Pneumatic Artificial Muscle Based on Robust Modeling
Then, a hybrid model is established based on the two models (the nonlinear model and the LTI model) and corresponding to it, a cascaded controller is developed, the outer loop of which is an H-infinite controller for the angular position tracking designed by loop-shaping design procedure (LSDP) and the inner loop is a nonlinear controller based on the feedback linearization theory for the PAM driving pressure control.
PECCO: A Profit and Cost-oriented Computation Offloading Scheme in Edge-Cloud Environment with Improved Moth-flame Optimisation
With the fast growing quantity of data generated by smart devices and the exponential surge of processing demand in the Internet of Things (IoT) era, the resource-rich cloud centres have been utilised to tackle these challenges.
De-biased Representation Learning for Fairness with Unreliable Labels
In other words, the fair pre-processing methods ignore the discrimination encoded in the labels either during the learning procedure or the evaluation stage.
Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay
In this paper, we show through empirical results that adopting the data replay is surprisingly favorable.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild
We define two SBSR tasks and construct two benchmarks consisting of more than 46, 000 CAD models, 1, 700 realistic models, and 145, 000 sketches in total.
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification
It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras.
DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection
Since GAD emphasizes the application and the rarity of anomalous samples, enriching the varieties of its datasets is fundamental work.
GLD-Net: Improving Monaural Speech Enhancement by Learning Global and Local Dependency Features with GLD Block
For monaural speech enhancement, contextual information is important for accurate speech estimation.
Improving Visual Speech Enhancement Network by Learning Audio-visual Affinity with Multi-head Attention
The proposed model alleviates these drawbacks by a) applying a model that fuses audio and visual features layer by layer in encoding phase, and that feeds fused audio-visual features to each corresponding decoder layer, and more importantly, b) introducing a 2-stage multi-head cross attention (MHCA) mechanism to infer audio-visual speech enhancement for balancing the fused audio-visual features and eliminating irrelevant features.
On Private Online Convex Optimization: Optimal Algorithms in $\ell_p$-Geometry and High Dimensional Contextual Bandits
To address such a challenge as the online convex optimization with privacy protection, we propose a private variant of online Frank-Wolfe algorithm with recursive gradients for variance reduction to update and reveal the parameters upon each data.
Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication
It is anticipated that 6G wireless networks will accelerate the convergence of the physical and cyber worlds and enable a paradigm-shift in the way we deploy and exploit communication networks.
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
Such user and group preferences are commonly represented as points in the vector space (i. e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs.
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster
To address this challenge, we have open sourced BigDL 2. 0 at https://github. com/intel-analytics/BigDL/ under Apache 2. 0 license (combining the original BigDL and Analytics Zoo projects); using BigDL 2. 0, users can simply build conventional Python notebooks on their laptops (with possible AutoML support), which can then be transparently accelerated on a single node (with up-to 9. 6x speedup in our experiments), and seamlessly scaled out to a large cluster (across several hundreds servers in real-world use cases).
Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison
To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes.
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation
Dynamic Vision Sensor (DVS) can asynchronously output the events reflecting apparent motion of objects with microsecond resolution, and shows great application potential in monitoring and other fields.
S-Rocket: Selective Random Convolution Kernels for Time Series Classification
A population-based optimization algorithm evolves the population in order to find a best state vector which minimizes the number of active kernels while maximizing the accuracy of the classifier.
When Does A Spectral Graph Neural Network Fail in Node Classification?
Although graph filters provide theoretical foundations for model explanations, it is unclear when a spectral GNN will fail.
Approximation bounds for norm constrained neural networks with applications to regression and GANs
This paper studies the approximation capacity of ReLU neural networks with norm constraint on the weights.
MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning
The learning objective of these methods is often hand-engineered and is not directly tied to the objective (i. e. incrementally learning new classes) during testing.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
Dreaming To Prune Image Deraining Networks
It is based on our observation that deep degradation representations can be clustered by degradation characteristics (types of rain) while independent of image content.
Multi-Grained Spatio-Temporal Features Perceived Network for Event-Based Lip-Reading
To recognize words from the event data, we propose a novel Multi-grained Spatio-Temporal Features Perceived Network (MSTP) to perceive fine-grained spatio-temporal features from microsecond time-resolved event data.
Exposure Normalization and Compensation for Multiple-Exposure Correction
However, the procedures of correcting underexposure and overexposure to normal exposures are much different from each other, leading to large discrepancies for the network in correcting multiple exposures, thus resulting in poor performance.
Contrastive Learning for Unsupervised Video Highlight Detection
Our framework encodes a video into a vector representation by learning to pick video clips that help to distinguish it from other videos via a contrastive objective using dropout noise.
Experimental quantum advantage with quantum coupon collector
The information transmitted by the proposed game also broke the classical limit.
Self-supervised Spatiotemporal Representation Learning by Exploiting Video Continuity
Recent self-supervised video representation learning methods have found significant success by exploring essential properties of videos, e. g. speed, temporal order, etc.
Non-Asymptotic Error Bounds for Bidirectional GANs
Our results are also applicable to the Wasserstein bidirectional GAN if the target distribution is assumed to have a bounded support.
Unrestricted Adversarial Attacks on ImageNet Competition
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
Graph Information Matters: Understanding Graph Filters from Interaction Probability
We show that the homophily degree of graphs significantly affects the prediction error of graph filters.
FedDrop: Trajectory-weighted Dropout for Efficient Federated Learning
Federated learning (FL) enables edge clients to train collaboratively while preserving individual's data privacy.
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset
While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall short in two aspects.
Spatio-Temporal Self-Attention Network for Video Saliency Prediction
3D convolutional neural networks have achieved promising results for video tasks in computer vision, including video saliency prediction that is explored in this paper.
Data-Driven Constitutive Relation Reveals Scaling Law for Hydrodynamic Transport Coefficients
We further proposed a constitutive relation model based on scaling law and tested it on the calculation of Rayleigh scattering spectra.
Bias-Tolerant Fair Classification
Therefore, we propose a Bias-TolerantFAirRegularizedLoss (B-FARL), which tries to regain the benefits using data affected by label bias and selection bias.
Visualizing Graph Neural Networks with CorGIE: Corresponding a Graph to Its Embedding
Graph neural networks (GNNs) are a class of powerful machine learning tools that model node relations for making predictions of nodes or links.
PVGNet: A Bottom-Up One-Stage 3D Object Detector With Integrated Multi-Level Features
Quantization-based methods are widely used in LiDAR points 3D object detection for its efficiency in extracting context information.
Image Change Captioning by Learning From an Auxiliary Task
Inspired by the success of multi-task learning, we formulate a training scheme that uses an auxiliary task to improve the training of the change captioning network.
Test-Time Fast Adaptation for Dynamic Scene Deblurring via Meta-Auxiliary Learning
Therefore, we are able to exploit the internal information at test time via the auxiliary task to enhance the performance of deblurring.
Deep Generative Learning via Schrödinger Bridge
At the sample level, we derive our Schr\"{o}dinger Bridge algorithm by plugging the drift term estimated by a deep score estimator and a deep density ratio estimator into the Euler-Maruyama method.
Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification
However, data augmentation for graph-based models remains a challenging problem, as graph data is more complex than traditional data, which consists of two features with different properties: graph topology and node attributes.
DECORE: Deep Compression with Reinforcement Learning
For a larger dataset like ImageNet with just 30 epochs of training, it can compress the ResNet-50 architecture by 44. 7% and reduce FLOPs by 42. 3%, with just a 0. 69% drop on Top-5 accuracy of the uncompressed model.
Generalized Linear Bandits with Local Differential Privacy
In this paper, we design LDP algorithms for stochastic generalized linear bandits to achieve the same regret bound as in non-privacy settings.
Learning Elastic Embeddings for Customizing On-Device Recommenders
The core idea is to compose elastic embeddings for each item, where an elastic embedding is the concatenation of a set of embedding blocks that are carefully chosen by an automated search function.
An error analysis of generative adversarial networks for learning distributions
This paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples.
Dual-side Sparse Tensor Core
We demonstrate the feasibility of our design with minimal changes to the existing production-scale inner-product-based Tensor Core.
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling
As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering.
DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks
Classical network embeddings create a low dimensional representation of the learned relationships between features across nodes.
Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network
In this paper, we propose a cross-modal self-attention (CMSA) module to utilize fine details of individual words and the input image or video, which effectively captures the long-range dependencies between linguistic and visual features.
Ranked #5 on
Referring Expression Segmentation
on J-HMDB
(Precision@0.9 metric)
STUaNet: Understanding uncertainty in spatiotemporal collective human mobility
The high dynamics and heterogeneous interactions in the complicated urban systems have raised the issue of uncertainty quantification in spatiotemporal human mobility, to support critical decision-makings in risk-aware web applications such as urban event prediction where fluctuations are of significant interests.
Wasserstein Graph Neural Networks for Graphs with Missing Attributes
In this paper, we propose an innovative node representation learning framework, Wasserstein Graph Neural Network (WGNN), to mitigate the problem.
VSEGAN: Visual Speech Enhancement Generative Adversarial Network
This paper proposes a novel frameworkthat involves visual information for speech enhancement, by in-corporating a Generative Adversarial Network (GAN).
On the capacity of deep generative networks for approximating distributions
Furthermore, it is shown that the approximation error in Wasserstein distance grows at most linearly on the ambient dimension and that the approximation order only depends on the intrinsic dimension of the target distribution.
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
Graph convolutional networks (GCNs) have achieved great success on graph-structured data.
Joint Visual and Audio Learning for Video Highlight Detection
In video highlight detection, the goal is to identify the interesting moments within an unedited video.
Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction
The main idea is to use CNNs to learn local appearances of vessels in image crops while using another point-cloud network to learn the global geometry of vessels in the entire image.
A Sequence-to-Sequence Approach to Dialogue State Tracking
This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system.
Ranked #1 on
Classification
on SGD
Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis
We applied Machine Learning techniques to find a cost-effective solution to the pipe failure problem in these Australian cities, where on average 1500 of water main failures occur each year.
AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting
In particular, we propose a new problem called unlabeled scene-adaptive crowd counting.
SmartTriage: A system for personalized patient data capture, documentation generation, and decision support
Symptom checkers have emerged as an important tool for collecting symptoms and diagnosing patients, minimizing the involvement of clinical personnel.
Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment Analysis
In this research, we first propose a common network to discover both intra-modal and inter-modal dynamics by utilizing basic LSTMs and tensor based convolution networks.
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network
Single image super-resolution is an effective way to enhance the spatial resolution of remote sensing image, which is crucial for many applications such as target detection and image classification.
Where is the Model Looking At?--Concentrate and Explain the Network Attention
We generate attribute-based textual explanations for the network and ground the attributes on the image to show visual explanations.
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior
Nonnegative matrix factorization is usually powerful for learning the "shallow" parts-based representation, but it clearly fails to discover deep hierarchical information within both the basis and representation spaces.
A Mathematical Introduction to Generative Adversarial Nets (GAN)
This paper attempts to provide an overview of GANs from a mathematical point of view.
Sentence Guided Temporal Modulation for Dynamic Video Thumbnail Generation
We consider the problem of sentence specified dynamic video thumbnail generation.
Ontology-based annotation and analysis of COVID-19 phenotypes
The commonly occurring 17 phenotypes were classified into different groups based on the Human Phenotype Ontology (HPO).
Adaptive Video Highlight Detection by Learning from User History
In this paper, we propose a simple yet effective framework that learns to adapt highlight detection to a user by exploiting the user's history in the form of highlights that the user has previously created.
Few-shot Scene-adaptive Anomaly Detection
In this paper, we propose a novel few-shot scene-adaptive anomaly detection problem to address the limitations of previous approaches.
Cross-Modal Weighting Network for RGB-D Salient Object Detection
In this paper, we propose a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channels for RGB-D SOD.
Ranked #9 on
RGB-D Salient Object Detection
on NJU2K
Recurrent Relational Memory Network for Unsupervised Image Captioning
Unsupervised image captioning with no annotations is an emerging challenge in computer vision, where the existing arts usually adopt GAN (Generative Adversarial Networks) models.
Unsupervised Vehicle Re-identification with Progressive Adaptation
For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as ``pseudo target samples''.
Unsupervised Vehicle Re-Identification
Vehicle Re-Identification
Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning
Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid.
Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning
The model provides automated recognition of given scans and generation of reports.
Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion
In this paper, we provide a substantial overview of the existing state-of-the-arts on the filed of multi-modal data analytics from shallow to deep spaces.
Utilizing machine learning to prevent water main breaks by understanding pipeline failure drivers
Data61 and Western Water worked collaboratively to apply engineering expertise and Machine Learning tools to find a cost-effective solution to the pipe failure problem in the region west of Melbourne, where on average 400 water main failures occur per year.
Deep Degradation Prior for Low-Quality Image Classification
Since the statistical properties are independent to image content, deep degradation prior can be learned on a training set of limited images without supervision of semantic labels and served in a form of "plugging-in" module of the existing classification networks to improve their performance on degraded images.
Ontology-based systematic classification and analysis of coronaviruses, hosts, and host-coronavirus interactions towards deep understanding of COVID-19
We hypothesized that ontology can be used as an integrative platform to classify and analyze HCI and disease outcomes.
Other Quantitative Biology
Approximation in shift-invariant spaces with deep ReLU neural networks
We also give lower bounds of the $L^p (1\le p \le \infty)$ approximation error for Sobolev spaces, which show that our construction of neural network is asymptotically optimal up to a logarithmic factor.
Try This Instead: Personalized and Interpretable Substitute Recommendation
Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i. e., personalization) and item-item relationships (i. e., substitution) for recommendation.
SimpleMKKM: Simple Multiple Kernel K-means
We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM).
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression
BERT is a cutting-edge language representation model pre-trained by a large corpus, which achieves superior performances on various natural language understanding tasks.
Convex Shape Representation with Binary Labels for Image Segmentation: Models and Fast Algorithms
We present a novel and effective binary representation for convex shapes.
RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework
Real-time traffic accident forecasting is increasingly important for public safety and urban management (e. g., real-time safe route planning and emergency response deployment).
Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning
We consider the problem of few-shot scene adaptive crowd counting.
Dual Convolutional LSTM Network for Referring Image Segmentation
Given an input image and a referring expression in the form of a natural language sentence, the goal is to segment the object of interest in the image referred by the linguistic query.
Deep Learning-based Image Compression with Trellis Coded Quantization
Recently many works attempt to develop image compression models based on deep learning architectures, where the uniform scalar quantizer (SQ) is commonly applied to the feature maps between the encoder and decoder.
Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition
To construct FDRN, we propose a new fast residual dense block (f-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts at the same time.
Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network
Although supervised deep deraining networks have obtained impressive results on synthetic datasets, they still cannot obtain satisfactory results on real images due to weak generalization of rain removal capacity, i. e., the pre-trained models usually cannot handle new shapes and directions that may lead to over-derained/under-derained results.
Unsupervised Learning of Camera Pose with Compositional Re-estimation
We consider the problem of unsupervised camera pose estimation.
Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space
Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient.
Convolutional Dictionary Pair Learning Network for Image Representation Learning
To address this issue, we propose a novel generalized end-to-end representation learning architecture, dubbed Convolutional Dictionary Pair Learning Network (CDPL-Net) in this paper, which integrates the learning schemes of the CNN and dictionary pair learning into a unified framework.
Compressed DenseNet for Lightweight Character Recognition
LDB is a convolutional block similarly as dense block, but it can reduce the computation cost and weight size to (1/L, 2/L), compared with original ones, where L is the number of layers in blocks.
DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking
However, in practice it is rather common to have no un-paired images in real deraining task, in such cases how to remove the rain streaks in an unsupervised way will be a very challenging task due to lack of constraints between images and hence suffering from low-quality recovery results.
Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition
But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable; 2) the tradi-tional convolution operation optimizes slowly and the hierar-chical features from different layers are not fully utilized.
Multilayer Collaborative Low-Rank Coding Network for Robust Deep Subspace Discovery
In this paper, we explore the deep multi-subspace recovery problem by designing a multilayer architecture for latent LRR.
Learning to Recommend via Meta Parameter Partition
The fixed part, capturing user invariant features, is shared by all users and is learned during offline meta learning stage.
Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition
We intuitively find that M$^2$Net can essentially promote the diversity of the inference path (selected blocks subset) selection, so as to enhance the recognition accuracy.
Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement
Contrast enhancement and noise removal are coupled problems for low-light image enhancement.
Kernelized Multiview Subspace Analysis by Self-weighted Learning
With the popularity of multimedia technology, information is always represented or transmitted from multiple views.
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation
In this paper, we consider the sigmoid Gaussian Hawkes process model: the baseline intensity and triggering kernel of Hawkes process are both modeled as the sigmoid transformation of random trajectories drawn from Gaussian processes (GP).
Region Mutual Information Loss for Semantic Segmentation
In this paper, we develop a region mutual information (RMI) loss to model the dependencies among pixels more simply and efficiently.
Future Frame Prediction Using Convolutional VRNN for Anomaly Detection
Anomaly detection in videos aims at reporting anything that does not conform the normal behaviour or distribution.
A Convolutional Neural Network with Mapping Layers for Hyperspectral Image Classification
In this paper, we propose a convolutional neural network with mapping layers (MCNN) for hyperspectral image (HSI) classification.
Multi-Task Deep Learning with Dynamic Programming for Embryo Early Development Stage Classification from Time-Lapse Videos
Accurate classification of embryo early development stages can provide embryologists valuable information for assessing the embryo quality, and hence is critical to the success of IVF.
Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery
Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part.
Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification
In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner.
A Targeted Acceleration and Compression Framework for Low bit Neural Networks
In this paper, we propose a novel Targeted Acceleration and Compression (TAC) framework to improve the performance of 1 bit deep neural networks W e consider that the acceleration and compression effects of binarizing fully connected layer s are not sufficient to compensate for the accuracy loss caused by it In the proposed framework, t he convolutional and fully connected layer are separated and optimized i ndividually .
Optimal low rank tensor recovery
We investigate the sample size requirement for exact recovery of a high order tensor of low rank from a subset of its entries.
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos
In this paper, we propose UnOS, an unified system for unsupervised optical flow and stereo depth estimation using convolutional neural network (CNN) by taking advantages of their inherent geometrical consistency based on the rigid-scene assumption.
Efficient EM-Variational Inference for Hawkes Process
In classical Hawkes process, the baseline intensity and triggering kernel are assumed to be a constant and parametric function respectively, which limits the model flexibility.
Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
Embeddings are a fundamental component of many modern machine learning and natural language processing models.
Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
Embeddings are a fundamental component of many modern machine learning and natural language processing models.
Attentive Action and Context Factorization
This is a challenging task due to the subtlety of human actions in video and the co-occurrence of contextual elements.
Convolutional Temporal Attention Model for Video-based Person Re-identification
A common approach for person re-identification is to first extract image features for all frames in the video, then aggregate all the features to form a video-level feature.
Knowledge Distillation for Human Action Anticipation
In this paper, we propose a novel knowledge distillation framework that uses an action recognition network to supervise the training of an action anticipation network, guiding the latter to attend to the relevant information needed for correctly anticipating the future actions.
Cross-Modal Self-Attention Network for Referring Image Segmentation
This module controls the information flow of features at different levels.
Ranked #5 on
Referring Video Object Segmentation
on Refer-YouTube-VOS
(using extra training data)
Contextual Attention for Hand Detection in the Wild
We also conduct ablation studies on hand detection to show the effectiveness of the proposed contextual attention module.
When AWGN-based Denoiser Meets Real Noises
In this paper, we propose a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN.
Ranked #1 on
Denoising
on Darmstadt Noise Dataset
Cross-Entropy Adversarial View Adaptation for Person Re-identification
The main contribution is to learn coupled asymmetric mappings regarding view characteristics which are adversarially trained to address the view discrepancy by optimising the cross-entropy view confusion objective.
Few-Shot Deep Adversarial Learning for Video-based Person Re-identification
Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages.
Crowd Counting Using Scale-Aware Attention Networks
In this paper, we consider the problem of crowd counting in images.
Wasserstein-Wasserstein Auto-Encoders
To address the challenges in learning deep generative models (e. g., the blurriness of variational auto-encoder and the instability of training generative adversarial networks, we propose a novel deep generative model, named Wasserstein-Wasserstein auto-encoders (WWAE).
Deep Discriminative Representation Learning with Attention Map for Scene Classification
However, most recent approaches to remote sensing scene classification are based on Convolutional Neural Networks (CNNs).
Deep Generative Learning via Variational Gradient Flow
We propose a general framework to learn deep generative models via \textbf{V}ariational \textbf{Gr}adient Fl\textbf{ow} (VGrow) on probability spaces.
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images
In this paper, we propose GIF2Video, the first learning-based method for enhancing the visual quality of GIFs in the wild.
A Remote Sensing Image Dataset for Cloud Removal
Removing clouds is an indispensable pre-processing step in remote sensing image analysis.
3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification
In this paper, we introduce a global video representation to video-based person re-identification (re-ID) that aggregates local 3D features across the entire video extent.
Multi-view Laplacian Eigenmaps Based on Bag-of-Neighbors For RGBD Human Emotion Recognition
As RGB view and depth view lie in different spaces, a new distance metric bag of neighbors (BON) used in MvLE can get the similar distributions of the two views.