Search Results for author:
Found 177 papers, 70 papers with code
Learning Attentive and Hierarchical Representations for 3D Shape Recognition
This paper proposes a novel method for 3D shape representation learning, namely Hyperbolic Embedded Attentive Representation (HEAR).
Region Graph Embedding Network for Zero-Shot Learning
To exploit the progressive interactions among these regions, we represent them as a region graph, on which the parts relation reasoning is performed with graph convolutions, thus leading to our PRR branch.
DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
We present DFormer, a novel RGB-D pretraining framework to learn transferable representations for RGB-D segmentation tasks.
Ranked #1 on
RGB-D Salient Object Detection
on DES
RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision
3D occupancy prediction holds significant promise in the fields of robot perception and autonomous driving, which quantifies 3D scenes into grid cells with semantic labels.
Fast Sparse PCA via Positive Semidefinite Projection for Unsupervised Feature Selection
A standard convex SPCA-based model with PSD constraint for unsupervised feature selection is proposed.
Grouping Boundary Proposals for Fast Interactive Image Segmentation
These boundary proposals are then incorporated into the proposed image segmentation model, such that the target segmentation contours are made up of a set of selected boundary proposals and the corresponding geodesic paths linking them.
A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation
In summary, this survey paper provides a comprehensive understanding of deep multi-modal learning for various BL generations and recognitions for the first time.
Few-shot Class-incremental Learning: A Survey
In our in-depth examination, we delve into various facets of FSCIL, encompassing the problem definition, the discussion of primary challenges of unreliable empirical risk minimization and the stability-plasticity dilemma, general schemes, and relevant problems of incremental learning and few-shot learning.
class-incremental learning
Few-Shot Class-Incremental Learning
+4
Pareto Invariant Representation Learning for Multimedia Recommendation
Multimedia recommendation involves personalized ranking tasks, where multimedia content is usually represented using a generic encoder.
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation
To address this limitation, we present Masked Multi-view with Swin Transformers (SwinMM), a novel multi-view pipeline for enabling accurate and data-efficient self-supervised medical image analysis.
ROFusion: Efficient Object Detection using Hybrid Point-wise Radar-Optical Fusion
In this paper, we propose a hybrid point-wise Radar-Optical fusion approach for object detection in autonomous driving scenarios.
Unbiased Scene Graph Generation via Two-stage Causal Modeling
To remedy this, we propose Two-stage Causal Modeling (TsCM) for the SGG task, which takes the long-tailed distribution and semantic confusion as confounders to the Structural Causal Model (SCM) and then decouples the causal intervention into two stages.
All in One: Exploring Unified Vision-Language Tracking with Multi-Modal Alignment
This approach achieves feature integration in a unified backbone, removing the need for carefully-designed fusion modules and resulting in a more effective and efficient VL tracking framework.
Revisiting Computer-Aided Tuberculosis Diagnosis
This dataset enables the training of sophisticated detectors for high-quality CTD.
UniOcc: Unifying Vision-Centric 3D Occupancy Prediction with Geometric and Semantic Rendering
In this technical report, we present our solution, named UniOCC, for the Vision-Centric 3D occupancy prediction track in the nuScenes Open Dataset Challenge at CVPR 2023.
Ranked #3 on
Prediction Of Occupancy Grid Maps
on Occ3D-nuScenes
SplatFlow: Learning Multi-frame Optical Flow via Splatting
Occlusion problem remains a key challenge in Optical Flow Estimation (OFE) despite the recent significant progress brought by deep learning in the field.
Emotional Talking Head Generation based on Memory-Sharing and Attention-Augmented Networks
Given an audio clip and a reference face image, the goal of the talking head generation is to generate a high-fidelity talking head video.
A Novel Interpretable and Generalizable Re-synchronization Model for Cued Speech based on a Multi-Cuer Corpus
Cued Speech (CS) is a multi-modal visual coding system combining lip reading with several hand cues at the phonetic level to make the spoken language visible to the hearing impaired.
MAVD: The First Open Large-Scale Mandarin Audio-Visual Dataset with Depth Information
Audio-visual speech recognition (AVSR) gains increasing attention from researchers as an important part of human-computer interaction.
Robust Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers
These triggers have demonstrated strong attack performance even under backdoor defense, which aims to eliminate or suppress the backdoor effect in the model.
X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models
This paper introduces a novel explainable image quality evaluation approach called X-IQE, which leverages visual large language models (LLMs) to evaluate text-to-image generation methods by generating textual explanations.
FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning
We introduce FedAds, the first benchmark for CVR estimation with vFL, to facilitate standardized and systematical evaluations for vFL algorithms.
A Comprehensive Survey on Segment Anything Model for Vision and Beyond
As the first to comprehensively review the progress of segmenting anything task for vision and beyond based on the foundation model of SAM, this work focuses on its applications to various tasks and data types by discussing its historical development, recent progress, and profound impact on broad applications.
Deep Intellectual Property Protection: A Survey
Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made revolutionary progress in recent years, and are widely used in various fields.
Few-shot Class-incremental Pill Recognition
In practice, the expensive cost of data annotation and the continuously increasing categories of new pills make it meaningful to develop a few-shot class-incremental pill recognition system.
class-incremental learning
Few-Shot Class-Incremental Learning
+3
Boosting Convolutional Neural Networks with Middle Spectrum Grouped Convolution
This paper proposes a novel module called middle spectrum grouped convolution (MSGC) for efficient deep convolutional neural networks (DCNNs) with the mechanism of grouped convolution.
Learning Invariant Representation via Contrastive Feature Alignment for Clutter Robust SAR Target Recognition
The proposed method contributes a mixed clutter variants generation strategy and a new inference branch equipped with channel-weighted mean square error (CWMSE) loss for invariant representation learning.
Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision
Interestingly, during the training phase supervised by point labels, we discover that CNNs first learn to segment a cluster of pixels near the targets, and then gradually converge to predict groundtruth point labels.
Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR
Therefore, the degree of overfitting for clutter reflects the non-causality of deep learning in SAR ATR.
FER-former: Multi-modal Transformer for Facial Expression Recognition
The ever-increasing demands for intuitive interactions in Virtual Reality has triggered a boom in the realm of Facial Expression Recognition (FER).
Facial Expression Recognition
Facial Expression Recognition (FER)
From Local Binary Patterns to Pixel Difference Networks for Efficient Visual Representation Learning
LBP is a successful hand-crafted feature descriptor in computer vision.
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
Deep learning has been highly successful in computer vision with large amounts of labeled data, but struggles with limited labeled training data.
Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification
Experiments on two medical image datasets (i. e., ISIC 2018 challenge and ChestX-ray14) show that our method outperforms state-of-the-art SSL methods.
Adversarial Machine Learning: A Systematic Survey of Backdoor Attack, Weight Attack and Adversarial Example
Some paradigms have been recently developed to explore this adversarial phenomenon occurring at different stages of a machine learning system, such as training-time adversarial attack (i. e., backdoor attack), deployment-time adversarial attack (i. e., weight attack), and inference-time adversarial attack (i. e., adversarial example).
Generalized Few-Shot Continual Learning with Contrastive Mixture of Adapters
The goal of Few-Shot Continual Learning (FSCL) is to incrementally learn novel tasks with limited labeled samples and preserve previous capabilities simultaneously, while current FSCL methods are all for the class-incremental purpose.
Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning
Given a model well-trained with a large-scale base dataset, Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding overfitting, without catastrophically forgetting all encountered classes previously.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
Enabling Augmented Segmentation and Registration in Ultrasound-Guided Spinal Surgery via Realistic Ultrasound Synthesis from Diagnostic CT Volume
While the US is not a standard paradigm for spinal surgery, the scarcity of intra-operative clinical US data is an insurmountable bottleneck in training a neural network.
BS3D: Building-scale 3D Reconstruction from RGB-D Images
Various datasets have been proposed for simultaneous localization and mapping (SLAM) and related problems.
WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning
Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space.
TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning
In this paper, we propose the learning scheme of Target Inner-Geometry from the LiDAR modality into camera-based BEV detectors for both dense depth and BEV features, termed as TiG-BEV.
AMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition
In this paper, we propose a deep model called Attention-based Multiple Dimensions EEG Transformer (AMDET), which can exploit the complementarity among the spectral-spatial-temporal features of EEG data by employing the multi-dimensional global attention mechanism.
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning
In this work, we pre-train DNNs on ultrasound (US) domains instead of ImageNet to reduce the domain gap in medical US applications.
Cross-Modal Mutual Learning for Cued Speech Recognition
To our knowledge, this is the first work on ACSR for Mandarin Chinese.
Rethinking Two Consensuses of the Transferability in Deep Learning
Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans.
TAOTF: A Two-stage Approximately Orthogonal Training Framework in Deep Neural Networks
In the first stage, we propose a novel algorithm called polar decomposition-based orthogonal initialization (PDOI) to find a good initialization for the orthogonal optimization.
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.
Boosting Binary Neural Networks via Dynamic Thresholds Learning
For ViTs, DyBinaryCCT presents the superiority of the convolutional embedding layer in fully binarized ViTs and achieves 56. 1% on the ImageNet dataset, which is nearly 9% higher than the baseline.
Dynamic Event-Triggered Discrete-Time Linear Time-Varying System with Privacy-Preservation
Moreover, the privacy of the system is analyzed to ensure the security of the real data.
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining
This work proposes a hierarchical contrastive learning (HiCo) method to improve the transferability for the US video model pretraining.
On Clustering Trend in Language Evolution Based on Dynamical Behaviors of Multi-Agent Model
Computer model has been extensively adopted to overcome the time limitation of language evolution by transforming language theory into physical modeling mechanism, which helps to explore the general laws of the evolution.
SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation
Precisely, the presence of scalar features makes the major part of the network binarizable, while vector features serve to retain rich structural information and ensure SO(3) equivariance.
Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense
Toward building more robust DNN-based SAR ATR models, this article explores the domain knowledge of SAR imaging process and proposes a novel Scattering Model Guided Adversarial Attack (SMGAA) algorithm which can generate adversarial perturbations in the form of electromagnetic scattering response (called adversarial scatterers).
Sim2Word: Explaining Similarity with Representative Attribute Words via Counterfactual Explanations
Different from existing models, in this paper, we propose a new interpretation method that explains the image similarity models by salience maps and attribute words.
Bag of Tricks for FGSM Adversarial Training
Adversarial training (AT) with samples generated by Fast Gradient Sign Method (FGSM), also known as FGSM-AT, is a computationally simple method to train robust networks.
Cross-Domain Few-Shot Classification via Inter-Source Stylization
The goal of Cross-Domain Few-Shot Classification (CDFSC) is to accurately classify a target dataset with limited labelled data by exploiting the knowledge of a richly labelled auxiliary dataset, despite the differences between the domains of the two datasets.
KL-divergence Based Deep Learning for Discrete Time Model
Neural Network (Deep Learning) is a modern model in Artificial Intelligence and it has been exploited in Survival Analysis.
Data-free Backdoor Removal based on Channel Lipschitzness
Pruning these channels was then shown to be effective in mitigating the backdoor behaviors.
Graph Signal Processing for Heterogeneous Change Detection Part II: Spectral Domain Analysis
Then, we propose a regression model for the HCD, which decomposes the source signal into the regressed signal and changed signal, and requires the regressed signal have the same spectral property as the target signal on the same graph.
Graph Signal Processing for Heterogeneous Change Detection Part I: Vertex Domain Filtering
In this first part, we analyze the HCD with GSP from the vertex domain.
Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation
Particularly, the model integrates the macro-level guided-category knowledge and micro-level open-domain dialogue data for the training, leveraging the priori knowledge into the latent space, which enables the model to disentangle the latent variables within the mesoscopic scale.
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry
Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding the overfitting and catastrophic forgetting simultaneously.
class-incremental learning
Few-Shot Class-Incremental Learning
+3
HQANN: Efficient and Robust Similarity Search for Hybrid Queries with Structured and Unstructured Constraints
The in-memory approximate nearest neighbor search (ANNS) algorithms have achieved great success for fast high-recall query processing, but are extremely inefficient when handling hybrid queries with unstructured (i. e., feature vectors) and structured (i. e., related attributes) constraints.
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging
In this work, we propose a meta-learning-based LR tuner, named MetaLR, to make different layers automatically co-adapt to downstream tasks based on their transferabilities across domains.
Median Pixel Difference Convolutional Network for Robust Face Recognition
Face recognition is one of the most active tasks in computer vision and has been widely used in the real world.
Deep Learning for Visual Speech Analysis: A Survey
Visual speech, referring to the visual domain of speech, has attracted increasing attention due to its wide applications, such as public security, medical treatment, military defense, and film entertainment.
Acoustic-to-articulatory Inversion based on Speech Decomposition and Auxiliary Feature
Acoustic-to-articulatory inversion (AAI) is to obtain the movement of articulators from speech signals.
Residual-guided Personalized Speech Synthesis based on Face Image
Experimental results show that the speech synthesized by our model is comparable to the personalized speech synthesized by training a large amount of audio data in previous works.
Exploring Inter-Channel Correlation for Diversity-preserved KnowledgeDistillation
Extensive experiments on two vision tasks, includ-ing ImageNet classification and Pascal VOC segmentation, demonstrate the superiority of our ICKD, which consis-tently outperforms many existing methods, advancing thestate-of-the-art in the fields of Knowledge Distillation.
WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking
In this work, we propose WebUAV-3M, the largest public UAV tracking benchmark to date, to facilitate both the development and evaluation of deep UAV trackers.
Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery
Aircraft detection in Synthetic Aperture Radar (SAR) imagery is a challenging task in SAR Automatic Target Recognition (SAR ATR) areas due to aircraft's extremely discrete appearance, obvious intraclass variation, small size and serious background's interference.
Improved (Related-key) Differential-based Neural Distinguishers for SIMON and SIMECK Block Ciphers
In CRYPTO 2019, Gohr made a pioneering attempt and successfully applied deep learning to the differential cryptanalysis against NSA block cipher SPECK32/64, achieving higher accuracy than the pure differential distinguishers.
Decoupling Makes Weakly Supervised Local Feature Better
Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences.
Ranked #1 on
Camera Localization
on Aachen Day-Night benchmark
Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-Resolution
Specifically, motivated by the local motion prior in the spatio-temporal dimension, we propose a local spatio-temporal attention module to perform implicit frame alignment and incorporate the local spatio-temporal information to enhance the local features (especially for small targets).
Learnable Lookup Table for Neural Network Quantization
Since a linear quantizer (i. e., round(*) function) cannot well fit the bell-shaped distributions of weights and activations, many existing methods use pre-defined functions (e. g., exponential function) with learnable parameters to build the quantizer for joint optimization.
Highly-Efficient Incomplete Large-Scale Multi-View Clustering With Consensus Bipartite Graph
Multi-view clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations.
Efficient Video Transformers with Spatial-Temporal Token Selection
Video transformers have achieved impressive results on major video recognition benchmarks, which however suffer from high computational cost.
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
Social network alignment aims at aligning person identities across social networks.
Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework
Ultrasound (US) imaging is commonly used to assist in the diagnosis and interventions of spine diseases, while the standardized US acquisitions performed by manually operating the probe require substantial experience and training of sonographers.
Transient Synchronization Stability Analysis of Wind Farms with MMC-HVDC Integration Under Offshore AC Grid Fault
The MMC-HVDC connected offshore wind farms (OWFs) could suffer short circuit fault (SCF), whereas their transient stability is not well analysed.
Unsupervised cross-user adaptation in taste sensation recognition based on surface electromyography with conformal prediction and domain regularized component analysis
The results show that DRCA improved the classification accuracy on six subjects (p < 0. 05), compared with the baseline models trained only with the source domain data;, while CPSC did not guarantee the accuracy improvement.
Dynamic Binary Neural Network by learning channel-wise thresholds
The experimental results prove that our method is an effective and straightforward way to reduce information loss and enhance performance of BNNs.
J-Score: A Robust Measure of Clustering Accuracy
It then computes two weighted sums of Jaccard indices measuring the reconciliation from classes to clusters and vice versa.
Pixel Difference Networks for Efficient Edge Detection
A faster version of PiDiNet with less than 0. 1M parameters can still achieve comparable performance among state of the arts with 200 FPS.
Ranked #2 on
Edge Detection
on BRIND
CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose
As an effective framework to quantify the prediction reliability, conformal prediction (CP) was developed with the CPKNN (CP with kNN).
Investigate the Essence of Long-Tailed Recognition from a Unified Perspective
Specifically, we demonstrate that long-tailed recognition suffers from both sample number and category similarity.
Multi-modal Entity Alignment in Hyperbolic Space
To mitigate this problem, a viable approach is to integrate complementary knowledge from other MMKGs.
Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification
In each hypergraph, different temporal granularities are captured by hyperedges that connect a set of graph nodes (i. e., part-based features) across different temporal ranges.
Ranked #5 on
Person Re-Identification
on iLIDS-VID
Learning Multi-Attention Context Graph for Group-Based Re-Identification
Extensive experiments on the novel dataset as well as three existing datasets clearly demonstrate the effectiveness of the proposed framework for both group-based re-id tasks.
Classifying herbal medicine origins by temporal and spectral data mining of electronic nose
In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms.
Anchor-Free Person Search
Person search aims to simultaneously localize and identify a query person from realistic, uncropped images, which can be regarded as the unified task of pedestrian detection and person re-identification (re-id).
Ranked #10 on
Person Search
on CUHK-SYSU
Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning
Furthermore, we propose a confidence-based approach to encode the optimization of image quality in the learning process.
Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose
Furthermore, this study provides a systematic analysis of different augmentation strategies.
Deep Learning for Instance Retrieval: A Survey
In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics.
Deep Learning for Scene Classification: A Survey
Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challenging problem in computer vision.
How to Train Your Agent to Read and Write
Typically, this requires an agent to fully understand the knowledge from the given text materials and generate correct and fluent novel paragraphs, which is very challenging in practice.
Ranked #3 on
KG-to-Text Generation
on AGENDA
The Unreasonable Effectiveness of the Class-reversed Sampling in Tail Sample Memorization
Long-tailed visual class recognition poses significant challenges to traditional machine learning and emerging deep networks due to its inherent class imbalance.
One-Pass Multi-View Clustering for Large-Scale Data
In this way, the generated partition can guide multi-view matrix factorization to produce more purposive coefficient matrix which, as a feedback, improves the quality of partition.
Exploring Inter-Channel Correlation for Diversity-Preserved Knowledge Distillation
Extensive experiments on two vision tasks, including ImageNet classification and Pascal VOC segmentation, demonstrate the superiority of our ICKD, which consistently outperforms many existing methods, advancing the state-of-the-art in the fields of Knowledge Distillation.
Ranked #17 on
Knowledge Distillation
on ImageNet
Localized Simple Multiple Kernel K-Means
After that, we theoretically show that the objective of SimpleMKKM is a special case of this local kernel alignment criterion with normalizing each base kernel matrix.
Distributed Fusion Estimation for Stochastic Uncertain Systems with Network-Induced Complexity and Multiple Noise
This paper investigates an issue of distributed fusion estimation under network-induced complexity and stochastic parameter uncertainties.
USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning
To alleviate this problem, an US dataset named US-4 is constructed for direct pretraining on the same domain.
Learning Class Unique Features in Fine-Grained Visual Classification
A major challenge in Fine-Grained Visual Classification (FGVC) is distinguishing various categories with high inter-class similarity by learning the feature that differentiate the details.
Ranked #10 on
Fine-Grained Image Classification
on FGVC Aircraft
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes.
Learning to Respond with Your Favorite Stickers: A Framework of Unifying Multi-Modality and User Preference in Multi-Turn Dialog
Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context and sticker using history of user.
Multi-Modal Active Learning for Automatic Liver Fibrosis Diagnosis based on Ultrasound Shear Wave Elastography
With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD).
Developing Univariate Neurodegeneration Biomarkers with Low-Rank and Sparse Subspace Decomposition
With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25$\%$ reduction in the mean annual change with 80$\%$ power and two-tailed $P=0. 05$ are 116, 279 and 387 for the longitudinal $A\beta+$ AD, $A\beta+$ mild cognitive impairment (MCI) and $A\beta+$ CU groups, respectively.
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going Beyond
Binary neural networks (BNNs), where both weights and activations are binarized into 1 bit, have been widely studied in recent years due to its great benefit of highly accelerated computation and substantially reduced memory footprint that appeal to the development of resource constrained devices.
New Ideas and Trends in Deep Multimodal Content Understanding: A Review
The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text.
Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition
To this end, in this work, we present a novel end-to-end 3D lip motion Network (3LMNet) by utilizing the sentence-level 3D lip motion (S3DLM) to recognize speakers in both the text-independent and text-dependent contexts.
A Real-time Contribution Measurement Method for Participants in Federated Learning
However, there is a lack of a sufficiently reasonable contribution measurement mechanism to distribute the reward for each agent.
Group Whitening: Balancing Learning Efficiency and Representational Capacity
Results show that GW consistently improves the performance of different architectures, with absolute gains of $1. 02\%$ $\sim$ $1. 49\%$ in top-1 accuracy on ImageNet and $1. 82\%$ $\sim$ $3. 21\%$ in bounding box AP on COCO.
Normalization Techniques in Training DNNs: Methodology, Analysis and Application
Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs), and have successfully been used in various applications.
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification
Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic.
Counterfactual-based minority oversampling for imbalanced classification
A key challenge of oversampling in imbalanced classification is that the generation of new minority samples often neglects the usage of majority classes, resulting in most new minority sampling spreading the whole minority space.
Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection
(1) The teacher model serves a dual role as a teacher and a student, such that the teacher predictions on unlabeled images may be very close to those of student, which limits the upper-bound of the student.
Attention-based Residual Speech Portrait Model for Speech to Face Generation
Given a speaker's speech, it is interesting to see if it is possible to generate this speaker's face.
JGR-P2O: Joint Graph Reasoning based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image
The key ideas are two-fold: a) explicitly modeling the dependencies among joints and the relations between the pixels and the joints for better local feature representation learning; b) unifying the dense pixel-wise offset predictions and direct joint regression for end-to-end training.
Dynamic Group Convolution for Accelerating Convolutional Neural Networks
In this paper, we propose dynamic group convolution (DGC) that adaptively selects which part of input channels to be connected within each group for individual samples on the fly.
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
This work introduces pyramidal convolution (PyConv), which is capable of processing the input at multiple filter scales.
Ranked #69 on
Semantic Segmentation
on ADE20K val
Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
Compared with static views, abundant dynamic properties between video frames are beneficial to refined depth estimation, especially for dynamic objects.
Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution
This work studies black-box adversarial attacks against deep neural networks (DNNs), where the attacker can only access the query feedback returned by the attacked DNN model, while other information such as model parameters or the training datasets are unknown.
Improved Residual Networks for Image and Video Recognition
We successfully train a 404-layer deep CNN on the ImageNet dataset and a 3002-layer network on CIFAR-10 and CIFAR-100, while the baseline is not able to converge at such extreme depths.
Controllable Orthogonalization in Training DNNs
Orthogonality is widely used for training deep neural networks (DNNs) due to its ability to maintain all singular values of the Jacobian close to 1 and reduce redundancy in representation.
An Investigation into the Stochasticity of Batch Whitening
Our work originates from the observation that while various whitening transformations equivalently improve the conditioning, they show significantly different behaviors in discriminative scenarios and training Generative Adversarial Networks (GANs).
Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog
Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances.
Trajectory Grouping with Curvature Regularization for Tubular Structure Tracking
Tubular structure tracking is a crucial task in the fields of computer vision and medical image analysis.
Auto-Encoding Twin-Bottleneck Hashing
One bottleneck (i. e., binary codes) conveys the high-level intrinsic data structure captured by the code-driven graph to the other (i. e., continuous variables for low-level detail information), which in turn propagates the updated network feedback for the encoder to learn more discriminative binary codes.
Layer-wise Conditioning Analysis in Exploring the Learning Dynamics of DNNs
To this end, we propose layer-wise conditioning analysis, which explores the optimization landscape with respect to each layer independently.
Deep Video Super-Resolution using HR Optical Flow Estimation
The key challenge for video SR lies in the effective exploitation of temporal dependency between consecutive frames.
Ranked #6 on
Video Super-Resolution
on MSU Super-Resolution for Video Compression
(BSQ-rate over ERQA metric)
Deep Learning for 3D Point Clouds: A Survey
To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds.
Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test
DEAN can be interpreted as a GOF game between two generative networks, where one explicit generative network learns an energy-based distribution that fits the real data, and the other implicit generative network is trained by minimizing a GOF test statistic between the energy-based distribution and the generated data, such that the underlying distribution of the generated data is close to the energy-based distribution.
Nonconvex Nonsmooth Low-Rank Minimization for Generalized Image Compressed Sensing via Group Sparse Representation
The popular L_2-norm and M-estimator are employed for standard image CS and robust CS problem to fit the data respectively.
Predicting Long-Term Skeletal Motions by a Spatio-Temporal Hierarchical Recurrent Network
The primary goal of skeletal motion prediction is to generate future motion by observing a sequence of 3D skeletons.
Fast Large-Scale Discrete Optimization Based on Principal Coordinate Descent
Binary optimization, a representative subclass of discrete optimization, plays an important role in mathematical optimization and has various applications in computer vision and machine learning.
Embarrassingly Simple Binary Representation Learning
Recent binary representation learning models usually require sophisticated binary optimization, similarity measure or even generative models as auxiliaries.
RANet: Ranking Attention Network for Fast Video Object Segmentation
Specifically, to integrate the insights of matching based and propagation based methods, we employ an encoder-decoder framework to learn pixel-level similarity and segmentation in an end-to-end manner.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition
It is computationally efficient and only marginally increases the cost of computing LBPTOP, yet is extremely effective for ME recognition.
NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising
This is motivated by the fact that finding closely similar pixels is more feasible than similar patches in natural images, which can be used to enhance image denoising performance.
Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image
A simple but useful observation on our NAC is: as long as the noise is weak, it is feasible to learn a self-supervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and clean images.
STAR: A Structure and Texture Aware Retinex Model
A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image.
Dynamically Visual Disambiguation of Keyword-based Image Search
To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation.
Iterative Normalization: Beyond Standardization towards Efficient Whitening
With the support of SND, we provide natural explanations to several phenomena from the perspective of optimization, e. g., why group-wise whitening of DBN generally outperforms full-whitening and why the accuracy of BN degenerates with reduced batch sizes.
Approach for Semi-automatic Construction of Anti-infective Drug Ontology Based on Entity Linking
Experiments show that our selected features have achieved a precision rate of 86. 77%, a recall rate of 89. 03% and an F1 score of 87. 89%.
Generative Model for Material Experiments Based on Prior Knowledge and Attention Mechanism
In this paper, we propose a generative adversarial model based on prior knowledge and attention mechanism to achieve the generation of irradiated material images (data-to-image model), and a prediction model for corresponding industrial performance (image-to-data model).
Highly-Economized Multi-View Binary Compression for Scalable Image Clustering
How to economically cluster large-scale multi-view images is a long-standing problem in computer vision.
Deep Learning for Generic Object Detection: A Survey
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images.
TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights
Despite the remarkable success of Convolutional Neural Networks (CNNs) on generalized visual tasks, high computational and memory costs restrict their comprehensive applications on consumer electronics (e. g., portable or smart wearable devices).
Generative Domain-Migration Hashing for Sketch-to-Image Retrieval
The generative model learns a mapping that the distributions of sketches can be indistinguishable from the distribution of natural images using an adversarial loss, and simultaneously learns an inverse mapping based on the cycle consistency loss in order to enhance the indistinguishability.
Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States
To advance subtle expression recognition, we contribute a Large-scale Subtle Emotions and Mental States in the Wild database (LSEMSW).
Automatic Derivation Of Formulas Using Reforcement Learning
First, the formula is abstractly expressed as a multiway tree model, and then each step of the formula derivation transformation is abstracted as a mapping of multiway trees.
Pixel-level Semantics Guided Image Colorization
While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from the problems of context confusion and edge color bleeding.
Fractional Wavelet Scattering Network and Applications
Results: The error rates for different fractional orders of FrScatNet are examined and show that the classification accuracy is significantly improved in fractional scattering domain.
SCAN: Sliding Convolutional Attention Network for Scene Text Recognition
Scene text recognition has drawn great attentions in the community of computer vision and artificial intelligence due to its challenges and wide applications.
Ro-SOS: Metric Expression Network (MEnet) for Robust Salient Object Segmentation
In this paper, we propose an end-to-end generic salient object segmentation model called Metric Expression Network (MEnet) to deal with saliency detection with the tolerance of distortion.
Zero-Shot Sketch-Image Hashing
As an important part of ZSIH, we formulate a generative hashing scheme in reconstructing semantic knowledge representations for zero-shot retrieval.
Texture Classification in Extreme Scale Variations using GANet
Because extreme scale variations are not necessarily present in most standard texture databases, to support the proposed extreme-scale aspects of texture understanding we are developing a new dataset, the Extreme Scale Variation Textures (ESVaT), to test the performance of our framework.
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Texture is a fundamental characteristic of many types of images, and texture representation is one of the essential and challenging problems in computer vision and pattern recognition which has attracted extensive research attention.
Quantifying Facial Age by Posterior of Age Comparisons
We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels.
Ranked #6 on
Age Estimation
on MORPH Album2
(using extra training data)
Towards Automatic Construction of Diverse, High-quality Image Dataset
To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries.
Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual Cross Retrieval
Cross-modal hashing is usually regarded as an effective technique for large-scale textual-visual cross retrieval, where data from different modalities are mapped into a shared Hamming space for matching.
Fast Person Re-Identification via Cross-Camera Semantic Binary Transformation
Numerous methods have been proposed for person re-identification, most of which however neglect the matching efficiency.
Zero-Shot Action Recognition With Error-Correcting Output Codes
Our ZSECOC equips the conventional ECOC with the additional capability of ZSAR, by addressing the domain shift problem.
Ranked #4 on
Zero-Shot Action Recognition
on Olympics
