Search Results for author:
Found 160 papers, 49 papers with code
SECOND: Sparsely Embedded Convolutional Detection
LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision.
MIM4DD: Mutual Information Maximization for Dataset Distillation
Thus, we introduce mutual information (MI) as the metric to quantify the shared information between the synthetic and the real datasets, and devise MIM4DD numerically maximizing the MI via a newly designed optimizable objective within a contrastive learning framework to update the synthetic dataset.
Style Aggregated Network for Facial Landmark Detection
In this work, we propose a style-aggregated approach to deal with the large intrinsic variance of image styles for facial landmark detection.
Ranked #2 on
Facial Landmark Detection
on AFLW-Front
(Mean NME metric)
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
To handle the limitation, in this paper we propose a novel Attention-Guided Generative Adversarial Network (AGGAN), which can detect the most discriminative semantic object and minimize changes of unwanted part for semantic manipulation problems without using extra data and models.
Ranked #1 on
Facial Expression Translation
on CelebA
Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation
In this paper, we propose a novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that makes it possible to generate images of natural scenes in arbitrary viewpoints, based on an image of the scene and a novel semantic map.
Bird View Synthesis
Cross-View Image-to-Image Translation
+1
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification
Our studies demonstrate that the proposed DAM method improves the performance of optimizing cross-entropy loss by a large margin, and also achieves better performance than optimizing the existing AUC square loss on these medical image classification tasks.
Ranked #2 on
Multi-Label Classification
on CheXpert
Unsupervised High-Resolution Portrait Gaze Correction and Animation
Solving this problem using an unsupervised method remains an open problem, especially for high-resolution face images in the wild, which are not easy to annotate with gaze and head pose labels.
GestureGAN for Hand Gesture-to-Gesture Translation in the Wild
Therefore, this task requires a high-level understanding of the mapping between the input source gesture and the output target gesture.
Ranked #1 on
Gesture-to-Gesture Translation
on NTU Hand Digit
Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection
Non-parametric face modeling aims to reconstruct 3D face only from images without shape assumptions.
Discrete Contrastive Diffusion for Cross-Modal Music and Image Generation
Diffusion probabilistic models (DPMs) have become a popular approach to conditional generation, due to their promising results and support for cross-modal synthesis.
LLM Inference Unveiled: Survey and Roofline Model Insights
Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model for systematic analysis of LLM inference techniques.
Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild
In this paper we address the problem of unsupervised gaze correction in the wild, presenting a solution that works without the need for precise annotations of the gaze angle and the head pose.
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
To tackle this issue, in this work we consider learning the scene generation in a local context, and correspondingly design a local class-specific generative network with semantic maps as a guidance, which separately constructs and learns sub-generators concentrating on the generation of different classes, and is able to provide more scene details.
Post-training Quantization on Diffusion Models
These approaches define a forward diffusion process for transforming data into noise and a backward denoising process for sampling data from noise.
Quantized GAN for Complex Music Generation from Dance Videos
We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates complex musical samples conditioned on dance videos.
Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation
In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (C$^2$GAN) for the task of keypoint-guided image generation.
Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data.
Generative Adversarial Network
Image-to-Image Translation
+1
Deep Micro-Dictionary Learning and Coding Network
In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN).
Cross-View Panorama Image Synthesis
In this paper, we tackle the problem of synthesizing a ground-view panorama image conditioned on a top-view aerial image, which is a challenging problem due to the large gap between the two image domains with different view-points.
ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models
This paper explores a new post-hoc training-free compression paradigm for compressing Large Language Models (LLMs) to facilitate their wider adoption in various computing environments.
TSTTC: A Large-Scale Dataset for Time-to-Contact Estimation in Driving Scenarios
Time-to-Contact (TTC) estimation is a critical task for assessing collision risk and is widely used in various driver assistance and autonomous driving systems.
QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning
Diffusion models have achieved remarkable success in image generation tasks, yet their practical deployment is restrained by the high memory and time consumption.
Cascade Attention Guided Residue Learning GAN for Cross-Modal Translation
However, in machine learning, this cross-modal learning is a nontrivial task because different modalities have no homogeneous properties.
Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos
Towards this end, in this work, we propose a Multi-modal Interaction Graph Convolutional Network (MIGCN), which jointly explores the complex intra-modal relations and inter-modal interactions residing in the video and sentence query to facilitate the understanding and semantic correspondence capture of the video and sentence query.
Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition
Extensive experiments on both in-the-lab and in-the-wild compound expression datasets demonstrate the superiority of our proposed CDNet against several state-of-the-art FSL methods.
cross-domain few-shot learning
Facial Expression Recognition
+1
A Simple and Effective Framework for Pairwise Deep Metric Learning
To tackle this issue, we propose a simple and effective framework to sample pairs in a batch of data for updating the model.
Network Binarization via Contrastive Learning
Neural network binarization accelerates deep models by quantizing their weights and activations into 1-bit.
Person Re-Identification via Recurrent Feature Aggregation
We address the person re-identification problem by effectively exploiting a globally discriminative feature representation from a sequence of tracked human regions/patches.
Attribute-Guided Sketch Generation
To this end, we propose a novel Attribute-Guided Sketch Generative Adversarial Network (ASGAN) which is an end-to-end framework and contains two pairs of generators and discriminators, one of which is used to generate faces with attributes while the other one is employed for image-to-sketch translation.
Segmenting Objects in Day and Night:Edge-Conditioned CNN for Thermal Image Semantic Segmentation
These advantages of thermal infrared cameras make the segmentation of semantic objects in day and night.
Seeing your sleep stage: cross-modal distillation from EEG to infrared video
$S^3VE$ is a large-scale dataset including synchronized infrared video and EEG signal for sleep stage classification, including 105 subjects and 154, 573 video clips that is more than 1100 hours long.
Towards Saner Deep Image Registration
Moreover, we derive a set of theoretical guarantees for our sanity-checked image registration method, with experimental results supporting our theoretical findings and their effectiveness in increasing the sanity of models without sacrificing any performance.
Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix Completion
A very popular approach for transductive multi-label recognition under linear classification settings is matrix completion.
Causal-DFQ: Causality Guided Data-free Network Quantization
Inspired by the causal understanding, we propose the Causality-guided Data-free Network Quantization method, Causal-DFQ, to eliminate the reliance on data via approaching an equilibrium of causality-driven intervened distributions.
High-Order Structure Based Middle-Feature Learning for Visible-Infrared Person Re-Identification
Existing VI-ReID methods ignore high-order structure information of features while being relatively difficult to learn a reasonable common feature space due to the large modality discrepancy between VIS and IR images.
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis
Safe deployment of deep neural networks in high-stake real-world applications requires theoretically sound uncertainty quantification.
Describing Unseen Videos via Multi-Modal Cooperative Dialog Agents
With the arising concerns for the AI systems provided with direct access to abundant sensitive information, researchers seek to develop more reliable AI with implicit information sources.
Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression
The experimental results running on an AMD server with four Geforce RTX 2080Ti GPUs show that our algorithm can achieve 3x speedup plus 19% energy savings on VGG distillation, and 3. 5x speedup plus 29% energy savings on ResNet distillation, both with negligible accuracy loss.
Saying the Unseen: Video Descriptions via Dialog Agents
Current vision and language tasks usually take complete visual data (e. g., raw images or videos) as input, however, practical scenarios may often consist the situations where part of the visual information becomes inaccessible due to various reasons e. g., restricted view with fixed camera or intentional vision block for security concerns.
Lipschitz Continuity Retained Binary Neural Network
Relying on the premise that the performance of a binary neural network can be largely restored with eliminated quantization error between full-precision weight vectors and their corresponding binary vectors, existing works of network binarization frequently adopt the idea of model robustness to reach the aforementioned objective.
Supplementing Missing Visions via Dialog for Scene Graph Generations
Most current AI systems rely on the premise that the input visual data are sufficient to achieve competitive performance in various computer vision tasks.
Dynamic Time Warping based Adversarial Framework for Time-Series Domain
Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain.
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach
Experiments on diverse real-world benchmarks demonstrate that the SRS method is well-suited for time-series OOD detection when compared to baseline methods.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+2
Adversarial-Metric Learning for Audio-Visual Cross-Modal Matching
AML aims to generate a modality-independent representation for each person in each modality via adversarial learning, while simultaneously learns a robust similarity measure for cross-modality matching via metric learning.
3D Cross-Pseudo Supervision (3D-CPS): A semi-supervised nnU-Net architecture for abdominal organ segmentation
Large curated datasets are necessary, but annotating medical images is a time-consuming, laborious, and expensive process.
Spatial-Contextual Discrepancy Information Compensation for GAN Inversion
SDIC follows a "compensate-and-edit" paradigm and successfully bridges the gap in image details between the original image and the reconstructed/edited image.
Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation
In recent years the ubiquitous deployment of AI has posed great concerns in regards to algorithmic bias, discrimination, and fairness.
Cross-modal Knowledge Distillation for Vision-to-Sensor Action Recognition
Human activity recognition (HAR) based on multi-modal approach has been recently shown to improve the accuracy performance of HAR.
Distribution-based Label Space Transformation for Multi-label Learning
This endows DLST the capability to handle label set sparsity and training data sparsity in multi-label learning problems.
Multi-task Learning of Cascaded CNN for Facial Attribute Classification
In order to address the above problems, we propose a novel multi-task learning of cas- caded convolutional neural network method, termed MCFA, for predicting multiple facial attributes simultaneously.
Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification
To address the above problem, we propose a novel deep transfer neural network method based on multi-label learning for facial attribute classification, termed FMTNet, which consists of three sub-networks: the Face detection Network (FNet), the Multi-label learning Network (MNet) and the Transfer learning Network (TNet).
Superpixel-guided Two-view Deterministic Geometric Model Fitting
Specifically, SDF includes three main parts: a deterministic sampling algorithm, a model hypothesis updating strategy and a novel model selection algorithm.
A Fast Face Detection Method via Convolutional Neural Network
Current face or object detection methods via convolutional neural network (such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features based on an image pyramid.
A New Target-specific Object Proposal Generation Method for Visual Tracking
Then, we apply the proposed TOPG method to the task of visual tracking and propose a TOPG-based tracker (called as TOPGT), where TOPG is used as a sample selection strategy to select a small number of high-quality target candidates from the generated object proposals.
Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: the Benefit of Target Expectation Maximization
Second, we estimate the posterior label distribution of the unlabeled data for target network training.
Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting
We cast the task of geometric model fitting as a representative mode-seeking problem on hypergraphs.
Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression
The deep CNN model is then designed to extract features from several image cropping datasets, upon which the cropping bounding boxes are predicted by the proposed CCR method.
Revisiting Graph Construction for Fast Image Segmentation
In this paper, we propose a simple but effective method for fast image segmentation.
From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach
One key challenge in talent search is to translate complex criteria of a hiring position into a search query, while it is relatively easy for a searcher to list examples of suitable candidates for a given position.
Optimizing Gross Merchandise Volume via DNN-MAB Dynamic Ranking Paradigm
With the transition from people's traditional `brick-and-mortar' shopping to online mobile shopping patterns in web 2. 0 $\mathit{era}$, the recommender system plays a critical role in E-Commerce and E-Retails.
Object Discovery via Cohesion Measurement
Based on the new Cohesion Measurement, a novel object discovery method is proposed to discover objects latent in an image by utilizing the eigenvectors of the affinity matrix.
Learn to Model Motion from Blurry Footages
It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects.
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/ε)$
In this work, we will show that the proposed HOPS achieved a lower iteration complexity of $\widetilde O(1/\epsilon^{1-\theta})$\footnote{$\widetilde O()$ suppresses a logarithmic factor.}
Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data
The feature appearances are beneficial to reduce the computational complexity for deterministic fitting methods.
Learning Hough Regression Models via Bridge Partial Least Squares for Object Detection
And the appropriate value of the only parameter used in PLS (i. e., the number of latent components) can be determined by using a cross-validation procedure.
Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting
In addition to the mode seeking algorithm, MSH includes a similarity measure between vertices on the hypergraph and a weight-aware sampling technique.
An Effective Unconstrained Correlation Filter and Its Kernelization for Face Recognition
In this paper, an effective unconstrained correlation filter called Uncon- strained Optimal Origin Tradeoff Filter (UOOTF) is presented and applied to robust face recognition.
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes.
Multi-Subregion Based Correlation Filter Bank for Robust Face Recognition
In this paper, we propose an effective feature extraction algorithm, called Multi-Subregion based Correlation Filter Bank (MS-CFB), for robust face recognition.
Search by Ideal Candidates: Next Generation of Talent Search at LinkedIn
This new system only needs the searcher to input one or several examples of suitable candidates for the position.
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes.
A Unified Analysis of Stochastic Momentum Methods for Deep Learning
However, their theoretical analysis of convergence of the training objective and the generalization error for prediction is still under-explored.
Learning Discriminators as Energy Networks in Adversarial Learning
In this work, we propose to refine the predictions of structured prediction models by effectively integrating discriminative models into the prediction.
DSNet: Deep and Shallow Feature Learning for Efficient Visual Tracking
In recent years, Discriminative Correlation Filter (DCF) based tracking methods have achieved great success in visual tracking.
Stagewise Training Accelerates Convergence of Testing Error Over SGD
For convex loss functions and two classes of "nice-behaviored" non-convex objectives that are close to a convex function, we establish faster convergence of stagewise training than the vanilla SGD under the PL condition on both training error and testing error.
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon)
To the best of our knowledge, this is the lowest iteration complexity achieved so far for the considered non-smooth optimization problems without strong convexity assumption.
Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning
We focus on the one-shot learning for video-based person re-Identification (re-ID).
Optimal Graph Learning With Partial Tags and Multiple Features for Image and Video Annotation
In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available.
Recurrent Face Aging
Modeling the aging process of human face is important for cross-age face verification and recognition.
Weakly Supervised Actor-Action Segmentation via Robust Multi-Task Ranking
However, current methods for detailed understanding of actor and action have significant limitations: they require large amounts of finely labeled data, and they fail to capture any internal relationship among actors and actions.
Localize Me Anywhere, Anytime: A Multi-Task Point-Retrieval Approach
The main contribution of our paper is that we use a 3D model reconstructed by a short video as the query to realize 3D-to-3D localization under a multi-task point retrieval framework.
Stochastic Primal-Dual Algorithms with Faster Convergence than $O(1/\sqrt{T})$ for Problems without Bilinear Structure
The main contribution of this paper is the design and analysis of new stochastic primal-dual algorithms that use a mixture of stochastic gradient updates and a logarithmic number of deterministic dual updates for solving a family of convex-concave problems with no bilinear structure assumed.
Joint Learning of Self-Representation and Indicator for Multi-View Image Clustering
Multi-view subspace clustering aims to divide a set of multisource data into several groups according to their underlying subspace structure.
Structured Discriminative Tensor Dictionary Learning for Unsupervised Domain Adaptation
However, the number of labeled source samples are always limited due to expensive annotation cost in practice, making sub-optimal performance been observed.
Expression Conditional GAN for Facial Expression-to-Expression Translation
In this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one image domain to another one based on an additional expression attribute.
GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks
Gaze correction aims to redirect the person's gaze into the camera by manipulating the eye region, and it can be considered as a specific image resynthesis problem.
Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation
In this paper, we propose a novel Pattern-Affinitive Propagation (PAP) framework to jointly predict depth, surface normal and semantic segmentation.
Ranked #51 on
Semantic Segmentation
on NYU Depth v2
Hallucinated Adversarial Learning for Robust Visual Tracking
Incorporating such an ability to hallucinate diverse new samples of the tracked instance can help the trackers alleviate the over-fitting problem in the low-data tracking regime.
Hierarchical Bayesian Personalized Recommendation: A Case Study and Beyond
Items in modern recommender systems are often organized in hierarchical structures.
Stochastic Optimization for Non-convex Inf-Projection Problems
In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint function over another variable.
Time-weighted Attentional Session-Aware Recommender System
Session-based Recurrent Neural Networks (RNNs) are gaining increasing popularity for recommendation task, due to the high autocorrelation of user's behavior on the latest session and the effectiveness of RNN to capture the sequence order information.
Adversarial Partial Multi-Label Learning
Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community.
Adversarial Paritial Multi-label Learning
Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community.
Joint Deep Learning of Facial Expression Synthesis and Recognition
Firstly, a facial expression synthesis generative adversarial network (FESGAN) is pre-trained to generate facial images with different facial expressions.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Adaptive Deep Metric Embeddings for Person Re-Identification under Occlusions
In this paper, we propose a novel person ReID method, which learns the spatial dependencies between the local regions and extracts the discriminative feature representation of the pedestrian image based on Long Short-Term Memory (LSTM), dealing with the problem of occlusions.
Object-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation
This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classification-based tracking methods.
Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification
Two different network architectures are respectively designed to extract features for two groups of attributes, and a novel dynamic weighting scheme is proposed to automatically assign the loss weight to each facial attribute during training.
Exocentric to Egocentric Image Generation via Parallel Generative Adversarial Network
In this paper, we investigate exocentric (third-person) view to egocentric (first-person) view image generation.
Hypergraph Optimization for Multi-structural Geometric Model Fitting
Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between data points.
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization
In this paper, we bridge this gap by providing a sharp analysis of epoch-wise stochastic gradient descent ascent method (referred to as Epoch-GDA) for solving strongly convex strongly concave (SCSC) min-max problems, without imposing any additional assumption about smoothness or the function's structure.
Learning Object Scale With Click Supervision for Object Detection
Finally, we fuse these CAMs together to generate pseudoground-truths and train a fully-supervised object detector withthese ground-truths.
Revisiting SGD with Increasingly Weighted Averaging: Optimization and Generalization Perspectives
It remains unclear how these averaging schemes affect the convergence of {\it both optimization error and generalization error} (two equally important components of testing error) for {\bf non-strongly convex objectives, including non-convex problems}.
Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes
Meanwhile, a Spatial detail-Preserving Network (SPN) with shallow convolutional layers is designed to generate high-resolution feature maps preserving the detailed spatial information.
Incorporating Multiple Cluster Centers for Multi-Label Learning
In this article, we propose to leverage the data augmentation technique to improve the performance of multi-label learning.
Multi-Level Generative Models for Partial Label Learning with Non-random Label Noise
Partial label (PL) learning tackles the problem where each training instance is associated with a set of candidate labels that include both the true label and irrelevant noise labels.
Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition
However, most of the existing algorithms are slow in practice, and their analysis revolves around the convergence to a nearly stationary point. We consider leveraging the Polyak-Lojasiewicz (PL) condition to design faster stochastic algorithms with stronger convergence guarantee.
Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise
In this paper, we propose robust stochastic algorithms for solving convex compositional problems of the form $f(\E_\xi g(\cdot; \xi)) + r(\cdot)$ by establishing {\bf sub-Gaussian confidence bounds} under weak assumptions about the tails of noise distribution, i. e., {\bf heavy-tailed noise} with bounded second-order moments.
Correlation filter tracking with adaptive proposal selection for accurate scale estimation
In this paper, we propose an adaptive proposal selection algorithm which can generate a small number of high-quality proposals to handle the problem of scale variations for visual object tracking.
Audio-Visual Event Localization via Recursive Fusion by Joint Co-Attention
Recent works have shown that attention mechanism is beneficial to the fusion process.
Hierarchical HMM for Eye Movement Classification
In this work, we tackle the problem of ternary eye movement classification, which aims to separate fixations, saccades and smooth pursuits from the raw eye positional data.
Revisiting Optical Flow Estimation in 360 Videos
Second, we refine the network by training with augmented data in a supervised manner.
Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset
To bridge this gap, in this paper we propose a novel Egocentric (first-person) 360{\deg} Kinetic human activity video dataset (EgoK360).
Robust Visual Tracking via Statistical Positive Sample Generation and Gradient Aware Learning
In this paper, we propose a robust tracking method via Statistical Positive sample generation and Gradient Aware learning (SPGA) to address the above two limitations.
Photometric and Spectroscopic Study of Flares on Ross 15
We conducted photometric and spectroscopic observations for Ross 15 in order to further study the flare properties of this less observed flare star.
Solar and Stellar Astrophysics High Energy Astrophysical Phenomena
How does stock market reflect the change in economic demand? A study on the industry-specific volatility spillover networks of China's stock market during the outbreak of COVID-19
At the beginning of the outbreak of COVID-19, in China's stock market, spillover effects from industry indices of sectors meeting the investment demand to those meeting the consumption demands rose significantly.
Hierarchical Representation via Message Propagation for Robust Model Fitting
In this paper, we propose a novel hierarchical representation via message propagation (HRMP) method for robust model fitting, which simultaneously takes advantages of both the consensus analysis and the preference analysis to estimate the parameters of multiple model instances from data corrupted by outliers, for robust model fitting.
Learning Audio-Visual Correlations from Variational Cross-Modal Generation
People can easily imagine the potential sound while seeing an event.
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice
This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation.
Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition
In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Learning Spatial-Semantic Relationship for Facial Attribute Recognition With Limited Labeled Data
First, three auxiliary tasks, consisting of a Patch Rotation Task (PRT), a Patch Segmentation Task (PST), and a Patch Classification Task (PCT), are jointly developed to learn the spatial-semantic relationship from large-scale unlabeled facial data.
Ranked #3 on
Facial Attribute Classification
on LFWA
Cross-View Exocentric to Egocentric Video Synthesis
Particularly, we propose a novel Bi-directional Spatial Temporal Attention Fusion Generative Adversarial Network (STA-GAN) to learn both spatial and temporal information to generate egocentric video sequences from the exocentric view.
Lipschitz Continuity Guided Knowledge Distillation
Knowledge distillation has become one of the most important model compression techniques by distilling knowledge from larger teacher networks to smaller student ones.
Contrastive Mutual Information Maximization for Binary Neural Networks
Neural network binarization accelerates deep models by quantizing their weights and activations into 1-bit.
Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks
Algorithmic bias is of increasing concern, both to the research community, and society at large.
Event Data Association via Robust Model Fitting for Event-based Object Tracking
In EDA, we first asynchronously fuse the event data based on its information entropy.
When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework
To alleviate the problem of limited base classes in our FER task, we propose a novel Emotion Guided Similarity Network (EGS-Net), consisting of an emotion branch and a similarity branch, based on a two-stage learning framework.
cross-domain few-shot learning
Facial Expression Recognition
+1
Win the Lottery Ticket via Fourier Analysis: Frequencies Guided Network Pruning
Extensive experiments on CIFAR-10 and CIFAR-100 demonstrate the superiority of our novel Fourier analysis based MBP compared to other traditional MBP algorithms.
Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition
To solve the problem, we propose a multi-modality feature fusion network to combine the modalities of the skeleton sequence and RGB frame instead of the RGB video, as the key information contained by the combination of skeleton sequence and RGB frame is close to that of the skeleton sequence and RGB video.
Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes
This shows that DMA-Net provides a good tradeoff between segmentation quality and speed for semantic segmentation in street scenes.
Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes
In this paper, we present a novel Stage-aware Feature Alignment Network (SFANet) based on the encoder-decoder structure for real-time semantic segmentation of street scenes.
Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition
The proposed work is the first investigation in the emotion recognition oriented EEG topological feature analysis, which brought a novel insight into the brain neural system nonlinear dynamics analysis and feature extraction.
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition
Given this observation, we devised a graph-inspired deep learning approach toward the sensor-based HAR tasks, which was further used to build a deep transfer learning model toward giving a tentative solution for these two challenging problems.
HiTPR: Hierarchical Transformer for Place Recognition in Point Cloud
In the SRT, we extract the local feature for each point cell.
A Proposal-Based Paradigm for Self-Supervised Sound Source Localization in Videos
Humans can easily recognize where and how the sound is produced via watching a scene and listening to corresponding audio cues.
Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis
Despite the success of deep neural networks (DNNs) for real-world applications over time-series data such as mobile health, little is known about how to train robust DNNs for time-series domain due to its unique characteristics compared to images and text data.
MLP-GAN for Brain Vessel Image Segmentation
Brain vessel image segmentation can be used as a promising biomarker for better prevention and treatment of different diseases.
Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem
Several studies have recently pointed that existing Visual Question Answering (VQA) models heavily suffer from the language prior problem, which refers to capturing superficial statistical correlations between the question type and the answer whereas ignoring the image contents.
Learning Omnidirectional Flow in 360-degree Video via Siamese Representation
Optical flow estimation in omnidirectional videos faces two significant issues: the lack of benchmark datasets and the challenge of adapting perspective video-based methods to accommodate the omnidirectional nature.
Semi-Supervised Video Inpainting with Cycle Consistency Constraints
Specifically, in this work, we propose an end-to-end trainable framework consisting of completion network and mask prediction network, which are designed to generate corrupted contents of the current frame using the known mask and decide the regions to be filled of the next frame, respectively.
Progressive Cross-modal Knowledge Distillation for Human Action Recognition
However, the accuracy performance of wearable sensor-based HAR is still far behind the ones from the visual modalities-based system (i. e., RGB video, skeleton, and depth).
DPTNet: A Dual-Path Transformer Architecture for Scene Text Detection
We further propose a parallel design that integrates the convolutional network with a powerful self-attention mechanism to provide complementary clues between the attention path and convolutional path.
Vision+X: A Survey on Multimodal Learning in the Light of Data
We are perceiving and communicating with the world in a multisensory manner, where different information sources are sophisticatedly processed and interpreted by separate parts of the human brain to constitute a complex, yet harmonious and unified sensing system.
Few-shot Medical Image Segmentation with Cycle-resemblance Attention
Recently, due to the increasing requirements of medical imaging applications and the professional requirements of annotating medical images, few-shot learning has gained increasing attention in the medical image semantic segmentation field.
Optical Flow Estimation in 360$^\circ$ Videos: Dataset, Model and Application
Moreover, we present a novel Siamese representation Learning framework for Omnidirectional Flow (SLOF) estimation, which is trained in a contrastive manner via a hybrid loss that combines siamese contrastive and optical flow losses.
BPT: Binary Point Cloud Transformer for Place Recognition
To solve this issue, we propose a binary point cloud transformer for place recognition.
MRCN: A Novel Modality Restitution and Compensation Network for Visible-Infrared Person Re-identification
Furthermore, to better disentangle the modality-relevant features and the modality-irrelevant features, we propose a novel Center-Quadruplet Causal (CQC) loss to encourage the network to effectively learn the modality-relevant features and the modality-irrelevant features.
Deep Stereo Video Inpainting
Stereo video inpainting aims to fill the missing regions on the left and right views of the stereo video with plausible content simultaneously.
A Decision Making Framework for Recommended Maintenance of Road Segments
Due to limited budgets allocated for road maintenance projects in various countries, road management departments face difficulties in making scientific maintenance decisions.
Probabilistically robust conformal prediction
We propose a novel adaptive PRCP (aPRCP) algorithm to achieve probabilistically robust coverage.
The Compatibility between the Pangu Weather Forecasting Model and Meteorological Operational Data
Recently, multiple data-driven models based on machine learning for weather forecasting have emerged.
Unseen Image Synthesis with Diffusion Models
While the current trend in the generative field is scaling up towards larger models and more training data for generalized domain representations, we go the opposite direction in this work by synthesizing unseen domain images without additional training.
Frequency Domain Nuances Mining for Visible-Infrared Person Re-identification
Specifically, we propose a novel Frequency Domain Nuances Mining (FDNM) method to explore the cross-modality frequency domain information, which mainly includes an amplitude guided phase (AGP) module and an amplitude nuances mining (ANM) module.
Online Multi-spectral Neuron Tracing
In this paper, we propose an online multi-spectral neuron tracing method with uniquely designed modules, where no offline training are required.
FBPT: A Fully Binary Point Transformer
This paper presents a novel Fully Binary Point Cloud Transformer (FBPT) model which has the potential to be widely applied and expanded in the fields of robotics and mobile devices.
Token Transformation Matters: Towards Faithful Post-hoc Explanation for Vision Transformer
To incorporate the influence of token transformation into interpretation, we propose TokenTM, a novel post-hoc explanation method that utilizes our introduced measurement of token transformation effects.
MaskSAM: Towards Auto-prompt SAM with Mask Classification for Medical Image Segmentation
Each pair of auxiliary mask and box prompts, which can solve the requirements of extra prompts, is associated with class label predictions by the sum of the auxiliary classifier token and the learnable global classifier tokens in the mask decoder of SAM to solve the predictions of semantic labels.
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
Based on this, we propose PruMerge, a novel adaptive visual token reduction approach, which largely reduces the number of visual tokens while maintaining comparable model performance.
On the Faithfulness of Vision Transformer Explanations
In contrast, our proposed SaCo offers a reliable faithfulness measurement, establishing a robust metric for interpretations.
Versatile Navigation under Partial Observability via Value-guided Diffusion Policy
To address these deficiencies, we propose a versatile diffusion-based approach for both 2D and 3D route planning under partial observability.
