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Found 61 papers, 13 papers with code
Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression Recognition
This indicates the superiority of hypergraph modeling for uncertainty estimation and label refinement on the personalized federated FER task.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Augmentation Matters: A Mix-Paste Method for X-Ray Prohibited Item Detection under Noisy Annotations
However, obtaining correct annotations is extremely hard if not impossible for large-scale X-ray images, where item overlapping is ubiquitous. As a result, X-ray images are easily contaminated with noisy annotations, leading to performance deterioration of existing methods. In this paper, we address the challenging problem of training a robust prohibited item detector under noisy annotations (including both category noise and bounding box noise) from a novel perspective of data augmentation, and propose an effective label-aware mixed patch paste augmentation method (Mix-Paste).
Video-to-Task Learning via Motion-Guided Attention for Few-Shot Action Recognition
In this paper, we propose a novel Dual Motion-Guided Attention Learning method (called DMGAL) for few-shot action recognition, aiming to learn the spatio-temporal relationships from the video-specific to the task-specific level.
Transitive Vision-Language Prompt Learning for Domain Generalization
The vision-language pre-training has enabled deep models to make a huge step forward in generalizing across unseen domains.
Dynamically Anchored Prompting for Task-Imbalanced Continual Learning
We find that imbalanced tasks significantly challenge the capability of models to control the trade-off between stability and plasticity from the perspective of recent prompt-based continual learning methods.
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.
Federated Learning with Extremely Noisy Clients via Negative Distillation
The model trained on noisy labels serves as a `bad teacher' in knowledge distillation, aiming to decrease the risk of providing incorrect information.
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.
Towards Unseen Triples: Effective Text-Image-joint Learning for Scene Graph Generation
In this work, we propose a Text-Image-joint Scene Graph Generation (TISGG) model to resolve the unseen triples and improve the generalisation capability of the SGG models.
PARFormer: Transformer-based Multi-Task Network for Pedestrian Attribute Recognition
Pedestrian attribute recognition (PAR) has received increasing attention because of its wide application in video surveillance and pedestrian analysis.
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation
To address this problem, state-of-the-art methods usually adopt a mixture of experts (MoE) to focus on different parts of the long-tailed distribution.
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation
Personalized Federated Learning (PFL) aims to learn personalized models for each client based on the knowledge across all clients in a privacy-preserving manner.
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.
Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-identification
The proposed DEEN can effectively generate diverse embeddings to learn the informative feature representations and reduce the modality discrepancy between the VIS and IR images.
Ranked #1 on
Cross-Modal Person Re-Identification
on SYSU-MM01
Federated Semi-Supervised Learning with Annotation Heterogeneity
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data.
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.
Label-Noise Learning with Intrinsically Long-Tailed Data
In this case, it is hard to distinguish clean samples from noisy samples on the intrinsic tail classes with the unknown intrinsic class distribution.
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
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation
Federated learning provides a privacy guarantee for generating good deep learning models on distributed clients with different kinds of data.
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features
Experiments on several benchmark datasets show that the proposed CReFF is an effective solution to obtain a promising FL model under heterogeneous and long-tailed data.
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.
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
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.
TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency
Inspired by those observations, we propose a novel visual saliency method, termed Target-Selective Gradient Backprop (TSGB), which leverages rectification operations to effectively emphasize target classes and further efficiently propagate the saliency to the image space, thereby generating target-selective and fine-grained saliency maps.
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
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
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.
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.
Dual Semantic Fusion Network for Video Object Detection
Video object detection is a tough task due to the deteriorated quality of video sequences captured under complex environments.
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.
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.
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.
End-to-end Learning of Object Motion Estimation from Retinal Events for Event-based Object Tracking
We feed the sequence of TSLTD frames to a novel Retinal Motion Regression Network (RMRNet) to perform an end-to-end 5-DoF object motion regression.
Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking
To achieve this goal, we present an Adaptive Time-Surface with Linear Time Decay (ATSLTD) event-to-frame conversion algorithm, which asynchronously and effectively warps the spatio-temporal information of asynchronous retinal events to a sequence of ATSLTD frames with clear object contours.
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.
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.
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.
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.
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.
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.
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).
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.
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network
To improve information flow and to capture sufficient knowledge for reconstructing the high-frequency details, we propose a cascaded multi-scale cross network (CMSC) in which a sequence of subnetworks is cascaded to infer high resolution features in a coarse-to-fine manner.
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.
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.
Revisiting Graph Construction for Fast Image Segmentation
In this paper, we propose a simple but effective method for fast image segmentation.
Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data
The feature appearances are beneficial to reduce the computational complexity for deterministic fitting methods.
Hypergraph Modelling for Geometric Model Fitting
The hypergraph, with large and "data-determined" degrees of hyperedges, can express the complex relationships between model hypotheses and data points.
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.
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.
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.
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.
Robust Scene Text Recognition Using Sparse Coding based Features
For character detection, we use the HSC features instead of using the Histograms of Oriented Gradients (HOG) features.
Distortion-driven Turbulence Effect Removal using Variational Model
To solve, or at least reduce these effects, we propose a new scheme to recover a latent image from observed frames by integrating a new variational model and distortion-driven spatial-temporal kernel regression.
The Ordered Residual Kernel for Robust Motion Subspace Clustering
The kernel permits the application of well-established statistical learning methods for effective outlier rejection, automatic recovery of the number of motions and accurate segmentation of the point trajectories.
