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Found 55 papers, 28 papers with code
Beyond Monocular Deraining: Stereo Image Deraining via Semantic Understanding
Moreover, even for single image based monocular deraining, many current methods fail to complete the task satisfactorily because they mostly rely on per pixel loss functions and ignoring semantic information.
Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification
On the other hand, mutual instance selection further selects reliable and informative instances for training according to the peer-confidence and relationship disagreement of the networks.
Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News
In this paper, we present a novel benchmark for Emotion Recognition using facial landmarks extracted from realistic news videos.
Homography Guided Temporal Fusion for Road Line and Marking Segmentation
Reliable segmentation of road lines and markings is critical to autonomous driving.
OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion Models
We also observe that the initiation denoising timestep for noise blending is the key to identity preservation and layout.
AS-FIBA: Adaptive Selective Frequency-Injection for Backdoor Attack on Deep Face Restoration
Extensive experiments demonstrate the efficacy of the degradation objective on state-of-the-art face restoration models.
Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration
Specifically, we utilize sparse self-attention to filter out redundant information and noise, directing the model's attention to focus on the features more relevant to the degraded regions in need of reconstruction.
Adversarial Purification and Fine-tuning for Robust UDC Image Restoration
This study delves into the enhancement of Under-Display Camera (UDC) image restoration models, focusing on their robustness against adversarial attacks.
PromptRR: Diffusion Models as Prompt Generators for Single Image Reflection Removal
For the prompt generation, we first propose a prompt pre-training strategy to train a frequency prompt encoder that encodes the ground-truth image into LF and HF prompts.
LRDif: Diffusion Models for Under-Display Camera Emotion Recognition
This study introduces LRDif, a novel diffusion-based framework designed specifically for facial expression recognition (FER) within the context of under-display cameras (UDC).
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing
In this paper, we propose a domain adversarial attack (DAA) method to mitigate the training instability problem by adding perturbations to the input images, which makes them indistinguishable across domains and enables domain alignment.
Towards Real-World Blind Face Restoration with Generative Diffusion Prior
We propose BFRffusion which is thoughtfully designed to effectively extract features from low-quality face images and could restore realistic and faithful facial details with the generative prior of the pretrained Stable Diffusion.
Deep Video Restoration for Under-Display Camera
With the pipeline, we build the first large-scale UDC video restoration dataset called PexelsUDC, which includes two subsets named PexelsUDC-T and PexelsUDC-P corresponding to different displays for UDC.
MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing
To address this issue, we propose a new Transformer variant, which applies the Taylor expansion to approximate the softmax-attention and achieves linear computational complexity.
Blind Face Restoration for Under-Display Camera via Dictionary Guided Transformer
However, due to the characteristics of the display, images taken by UDC suffer from significant quality degradation.
InterTracker: Discovering and Tracking General Objects Interacting with Hands in the Wild
Understanding human interaction with objects is an important research topic for embodied Artificial Intelligence and identifying the objects that humans are interacting with is a primary problem for interaction understanding.
Benchmarking Ultra-High-Definition Image Reflection Removal
To the best of our knowledge, these two datasets are the first largest-scale UHD datasets for SIRR.
LLDiffusion: Learning Degradation Representations in Diffusion Models for Low-Light Image Enhancement
In this paper, we address this limitation by proposing a degradation-aware learning scheme for LLIE using diffusion models, which effectively integrates degradation and image priors into the diffusion process, resulting in improved image enhancement.
HTNet for micro-expression recognition
The transformer layer is used to focus on representing local minor muscle movement with local self-attention in each area.
Ranked #1 on
Micro-Expression Recognition
on CASME II
Model Calibration in Dense Classification with Adaptive Label Perturbation
To improve model calibration, we propose Adaptive Stochastic Label Perturbation (ASLP) which learns a unique label perturbation level for each training image.
GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions
Second, we introduce a residual dense transformer block (RDTB) as the final GridFormer layer.
Towards an Effective and Efficient Transformer for Rain-by-snow Weather Removal
Rain-by-snow weather removal is a specialized task in weather-degraded image restoration aiming to eliminate coexisting rain streaks and snow particles.
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives
Multi-object tracking (MOT) aims to build moving trajectories for number-agnostic objects.
Robust Single Image Reflection Removal Against Adversarial Attacks
This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks.
Ranked #2 on
Reflection Removal
on Real20
Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning
HCD consists of a hierarchical dehazing network (HDN) and a novel hierarchical contrastive loss (HCL).
A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal
Second, we discuss the challenges of face restoration.
Generalised Co-Salient Object Detection
We propose a new setting that relaxes an assumption in the conventional Co-Salient Object Detection (CoSOD) setting by allowing the presence of "noisy images" which do not show the shared co-salient object.
Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration
To this end, we propose a Face Restoration Searching Network (FRSNet) to adaptively search the suitable feature extraction architecture within our specified search space, which can directly contribute to the restoration quality.
Vicinity Vision Transformer
Based on this observation, we present a Vicinity Attention that introduces a locality bias to vision transformers with linear complexity.
Blind Face Restoration: Benchmark Datasets and a Baseline Model
To address this problem, we first synthesize two blind face restoration benchmark datasets called EDFace-Celeb-1M (BFR128) and EDFace-Celeb-150K (BFR512).
From heavy rain removal to detail restoration: A faster and better network
This approach comprises two key modules, a rain streaks removal network (R$^2$Net) focusing on accurate rain removal, and a details reconstruction network (DRNet) designed to recover the textural details of rain-free images.
Deep Image Deblurring: A Survey
Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image.
MC-Blur: A Comprehensive Benchmark for Image Deblurring
Blur artifacts can seriously degrade the visual quality of images, and numerous deblurring methods have been proposed for specific scenarios.
Dense Uncertainty Estimation
Deep neural networks can be roughly divided into deterministic neural networks and stochastic neural networks. The former is usually trained to achieve a mapping from input space to output space via maximum likelihood estimation for the weights, which leads to deterministic predictions during testing.
EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset
It is thus unclear how these algorithms perform on public face hallucination datasets.
Ranked #1 on
Image Super-Resolution
on WLFW
T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing
In order to reduce network parameters, the intra-stage recursive computation of ResNet is adopted in our Stack T-Net.
Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding
Single-image super-resolution (SR) and multi-frame SR are two ways to super resolve low-resolution images.
Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior
We first use a coarse deraining network to reduce the rain streaks on the input images, and then adopt a pre-trained semantic segmentation network to extract semantic features from the coarse derained image.
Deep Two-View Structure-from-Motion Revisited
Two-view structure-from-motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM.
Ranked #25 on
Monocular Depth Estimation
on KITTI Eigen split
Enhanced Spatio-Temporal Interaction Learning for Video Deraining: A Faster and Better Framework
Video deraining is an important task in computer vision as the unwanted rain hampers the visibility of videos and deteriorates the robustness of most outdoor vision systems.
Deep Dense Multi-scale Network for Snow Removal Using Semantic and Geometric Priors
Images captured in snowy days suffer from noticeable degradation of scene visibility, which degenerates the performance of current vision-based intelligent systems.
Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop Removal
In addition, to further refine the result, a Differential-driven Dual Attention-in-Attention Model (D-DAiAM) is proposed with a "heavy-to-light" scheme to remove rain via addressing the unsatisfying deraining regions.
ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring
Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions.
Pyramid Architecture Search for Real-Time Image Deblurring
Multi-scale and multi-patch deep models have been shown effective in removing blurs of dynamic scenes.
Displacement-Invariant Cost Computation for Efficient Stereo Matching
A common way to speed up the computation is to downsample the feature volume, but this loses high-frequency details.
Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency
This paper proposes a human parsing based texture transfer model via cross-view consistency learning to generate the texture of 3D human body from a single image.
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
Learning matching costs has been shown to be critical to the success of the state-of-the-art deep stereo matching methods, in which 3D convolutions are applied on a 4D feature volume to learn a 3D cost volume.
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation
Sign language translation (SLT) aims to interpret sign video sequences into text-based natural language sentences.
Single Image Super-Resolution via a Holistic Attention Network
Informative features play a crucial role in the single image super-resolution task.
Ranked #2 on
Image Super-Resolution
on Urban100 - 8x upscaling
Deblurring by Realistic Blurring
To address this problem, we propose a new method which combines two GAN models, i. e., a learning-to-Blur GAN (BGAN) and learning-to-DeBlur GAN (DBGAN), in order to learn a better model for image deblurring by primarily learning how to blur images.
Ranked #17 on
Deblurring
on HIDE (trained on GOPRO)
STH: Spatio-Temporal Hybrid Convolution for Efficient Action Recognition
Such a design enables efficient spatio-temporal modeling and maintains a small model scale.
Learning Joint Gait Representation via Quintuplet Loss Minimization
Gait recognition is an important biometric method popularly used in video surveillance, where the task is to identify people at a distance by their walking patterns from video sequences.
Adversarial Spatio-Temporal Learning for Video Deblurring
To tackle the second challenge, we leverage the developed DBLRNet as a generator in the GAN (generative adversarial network) architecture, and employ a content loss in addition to an adversarial loss for efficient adversarial training.
