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Found 57 papers, 17 papers with code
Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-Order Feature Analysis
The proposed network exploits joint-aware features that are crucial for both tasks, with which gesture recognition and 3D hand pose estimation boost each other to learn highly discriminative features and models.
I2CANSAY:Inter-Class Analogical Augmentation and Intra-Class Significance Analysis for Non-Exemplar Online Task-Free Continual Learning
Online task-free continual learning (OTFCL) is a more challenging variant of continual learning which emphasizes the gradual shift of task boundaries and learns in an online mode.
MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection
Moreover, MoE-FFD leverages the expressivity of transformers and local priors of CNNs to simultaneously extract global and local forgery clues.
Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining
Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars.
Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models
Denoising Diffusion Probabilistic Model (DDPM) has shown great competence in image and audio generation tasks.
SinSR: Diffusion-Based Image Super-Resolution in a Single Step
Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method can achieve comparable or even superior performance compared to both previous SOTA methods and the teacher model, in just one sampling step, resulting in a remarkable up to x10 speedup for inference.
Pixel-Inconsistency Modeling for Image Manipulation Localization
Digital image forensics plays a crucial role in image authentication and manipulation localization.
Forgery-aware Adaptive Vision Transformer for Face Forgery Detection
To circumvent these issues, we propose a novel Forgery-aware Adaptive Vision Transformer (FA-ViT).
ExposureDiffusion: Learning to Expose for Low-light Image Enhancement
Different from a vanilla diffusion model that has to perform Gaussian denoising, with the injected physics-based exposure model, our restoration process can directly start from a noisy image instead of pure noise.
Ranked #1 on
Image Denoising
on Image Denoising on SID x300
One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton Matching
The first is multi-scale matching which captures the scale-wise semantic relevance of skeleton data at multiple spatial and temporal scales simultaneously.
Enhancing Low-Light Images Using Infrared-Encoded Images
In this work, we propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter, which allows for the capture of more photons and results in improved signal-to-noise ratio due to the inclusion of information from the IR spectrum.
Beyond Learned Metadata-based Raw Image Reconstruction
Besides, we propose a novel design of the context model, which can better predict the order masks of encoding/decoding based on both the sRGB image and the masks of already processed features.
Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection
Face forgery detection is essential in combating malicious digital face attacks.
Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization
We investigate self-supervised representation learning and design a novel skeleton cloud colorization technique that is capable of learning spatial and temporal skeleton representations from unlabeled skeleton sequence data.
Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation
The second module compares the outputs of augmented data from the current model to the outputs of weakly augmented data from the source model, forming a novel consistency regularization on the model to alleviate the accumulation of prediction errors.
Unsupervised Deep Digital Staining For Microscopic Cell Images Via Knowledge Distillation
In this work, we propose a novel unsupervised deep learning framework for the digital staining of cell images using knowledge distillation and generative adversarial networks (GANs).
Temporal Coherent Test-Time Optimization for Robust Video Classification
To exploit information in video with self-supervised learning, TeCo uses global content from video clips and optimizes models for entropy minimization.
Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger
Extensive experiments show that with our trained trigger injection models and simple modification of encoder parameters (of the compression model), the proposed attack can successfully inject several backdoors with corresponding triggers in a single image compression model.
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.
Removing Image Artifacts From Scratched Lens Protectors
Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts.
Virtual Try-On with Pose-Garment Keypoints Guided Inpainting
In our method, human pose and garment keypoints are extracted from source images and constructed as graphs to predict the garment keypoints at the target pose.
Forensicability Assessment of Questioned Images in Recapturing Detection
We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks.
Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues
In this paper, we establish the first joint face spoofing and forgery detection benchmark using both visual appearance and physiological rPPG cues.
Adversarial Pairwise Reverse Attention for Camera Performance Imbalance in Person Re-identification: New Dataset and Metrics
Existing evaluation metrics for Person Re-Identification (Person ReID) models focus on system-wide performance.
One-Class Knowledge Distillation for Face Presentation Attack Detection
Under this framework, a teacher network is trained with source domain samples to provide discriminative feature representations for face PAD.
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond
Finally, we examine various types of adversarial attacks that are specific to deraining problems and their effects on both human and machine vision tasks, including 1) rain region attacks, adding perturbations only in the rain regions to make the perturbations in the attacked rain images less visible; 2) object-sensitive attacks, adding perturbations only in regions near the given objects.
DEX: Domain Embedding Expansion for Generalized Person Re-identification
With DEX and DEXLite, existing methods can gain significant improvements when tested on other unseen datasets, thereby demonstrating the general applicability of our method.
Asymmetric Modality Translation For Face Presentation Attack Detection
In this paper, we propose a novel framework based on asymmetric modality translation for face presentation attack detection in bi-modality scenarios.
Disentangled Feature Representation for Few-shot Image Classification
In this work, we propose a novel Disentangled Feature Representation framework, dubbed DFR, for few-shot learning applications.
Low-Light Image Enhancement with Normalizing Flow
To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many.
Ranked #3 on
Low-Light Image Enhancement
on Sony-Total-Dark
Variational Disentanglement for Domain Generalization
Domain generalization aims to learn an invariant model that can generalize well to the unseen target domain.
Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning
We investigate unsupervised representation learning for skeleton action recognition, and design a novel skeleton cloud colorization technique that is capable of learning skeleton representations from unlabeled skeleton sequence data.
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation
Though convolutional neural networks are widely used in different tasks, lack of generalization capability in the absence of sufficient and representative data is one of the challenges that hinder their practical application.
Single Image Reflection Removal With Absorption Effect
In this paper, we consider the absorption effect for the problem of single image reflection removal.
Panoramic Image Reflection Removal
This paper studies the problem of panoramic image reflection removal, aiming at reliving the content ambiguity between reflection and transmission scenes.
Multi-Domain Adversarial Feature Generalization for Person Re-Identification
To make Person Re-ID systems more practical and scalable, several cross-dataset domain adaptation methods have been proposed, which achieve high performance without the labeled data from the target domain.
Skeleton-based Relational Reasoning for Group Activity Analysis
In this paper, we leverage the skeleton information to learn the interactions between the individuals straight from it.
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization
Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks.
Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems
In this paper, we propose a novel black-box backdoor attack technique on face recognition systems, which can be conducted without the knowledge of the targeted DNN model.
Heterogeneous Domain Generalization via Domain Mixup
One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is that they lack generalization capability.
Interaction Relational Network for Mutual Action Recognition
Our solution is able to achieve state-of-the-art performance on the traditional interaction recognition datasets SBU and UT, and also on the mutual actions from the large-scale dataset NTU RGB+D.
Ranked #1 on
Human Interaction Recognition
on UT-Interaction
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition.
Ranked #5 on
One-Shot 3D Action Recognition
on NTU RGB+D 120
SPLINE-Net: Sparse Photometric Stereo through Lighting Interpolation and Normal Estimation Networks
This paper solves the Sparse Photometric stereo through Lighting Interpolation and Normal Estimation using a generative Network (SPLINE-Net).
Face Image Reflection Removal
Face images captured through the glass are usually contaminated by reflections.
Skeleton-Based Online Action Prediction Using Scale Selection Network
Since there are significant temporal scale variations in the observed part of the ongoing action at different time steps, a novel window scale selection method is proposed to make our network focus on the performed part of the ongoing action and try to suppress the possible incoming interference from the previous actions at each step.
Ranked #64 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Feature Boosting Network For 3D Pose Estimation
Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation.
Intermediate Deep Feature Compression: the Next Battlefield of Intelligent Sensing
The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical.
Attention to Head Locations for Crowd Counting
Occlusions, complex backgrounds, scale variations and non-uniform distributions present great challenges for crowd counting in practical applications.
SSNet: Scale Selection Network for Online 3D Action Prediction
As there are significant temporal scale variations of the observed part of the ongoing action at different progress levels, we propose a novel window scale selection scheme to make our network focus on the performed part of the ongoing action and try to suppress the noise from the previous actions at each time step.
Domain Generalization With Adversarial Feature Learning
In this paper, we tackle the problem of domain generalization: how to learn a generalized feature representation for an âunseenâ target domain by taking the advantage of multiple seen source-domain data.
Ranked #49 on
Domain Generalization
on PACS
CRRN: Multi-Scale Guided Concurrent Reflection Removal Network
Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks.
Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification
Typical person re-identification (ReID) methods usually describe each pedestrian with a single feature vector and match them in a task-specific metric space.
Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation
In our work, we focus on the weakly supervised semantic segmentation with image label annotations.
Benchmarking Single-Image Reflection Removal Algorithms
Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing the efficiency of visual computing systems.
Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition.
Ranked #62 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Global Context-Aware Attention LSTM Networks for 3D Action Recognition
Hence we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for 3D action recognition, which is able to selectively focus on the informative joints in the action sequence with the assistance of global contextual information.
Ranked #7 on
One-Shot 3D Action Recognition
on NTU RGB+D 120
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates
Skeleton-based human action recognition has attracted a lot of research attention during the past few years.
Ranked #6 on
One-Shot 3D Action Recognition
on NTU RGB+D 120
