Search Results for author:
Found 245 papers, 157 papers with code
Visual Tracking via Dynamic Graph Learning
First, the graph is initialized by assigning binary weights of some image patches to indicate the object and background patches according to the predicted bounding box.
PM-GANs: Discriminative Representation Learning for Action Recognition Using Partial-modalities
As a result, using partial data channels to build a full representation of multi-modalities is clearly desired.
VITAL: VIsual Tracking via Adversarial Learning
To augment positive samples, we use a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes.
Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition
Ear recognition task is known as predicting whether two ear images belong to the same person or not.
Deep Structured Scene Parsing by Learning with Image Descriptions
This paper addresses a fundamental problem of scene understanding: How to parse the scene image into a structured configuration (i. e., a semantic object hierarchy with object interaction relations) that finely accords with human perception.
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions
Rather than relying on elaborative annotations (e. g., manually labeled semantic maps and relations), we train our deep model in a weakly-supervised learning manner by leveraging the descriptive sentences of the training images.
Enlarging Context with Low Cost: Efficient Arithmetic Coding with Trimmed Convolution
One key issue of arithmetic encoding method is to predict the probability of the current coding symbol from its context, i. e., the preceding encoded symbols, which usually can be executed by building a look-up table (LUT).
Active Self-Paced Learning for Cost-Effective and Progressive Face Identification
By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert re-certification.
Robust Online Matrix Factorization for Dynamic Background Subtraction
We propose an effective online background subtraction method, which can be robustly applied to practical videos that have variations in both foreground and background.
Deep Identity-aware Transfer of Facial Attributes
In general, our model consists of a mask network and an attribute transform network which work in synergy to generate a photo-realistic facial image with the reference attribute.
Ranked #2 on
Image-to-Image Translation
on RaFD
Convolutional Network for Attribute-driven and Identity-preserving Human Face Generation
Here we address this problem from the view of optimization, and suggest an optimization model to generate human face with the given attributes while keeping the identity of the reference image.
Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning
Cross-domain visual data matching is one of the fundamental problems in many real-world vision tasks, e. g., matching persons across ID photos and surveillance videos.
Learning Support Correlation Filters for Visual Tracking
Sampling and budgeting training examples are two essential factors in tracking algorithms based on support vector machines (SVMs) as a trade-off between accuracy and efficiency.
A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition
Understanding human activity is very challenging even with the recently developed 3D/depth sensors.
Weighted Schatten $p$-Norm Minimization for Image Denoising and Background Subtraction
However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications.
Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-identification
Furthermore, each bit of our hashing codes is unequally weighted so that we can manipulate the code lengths by truncating the insignificant bits.
Towards Effective Codebookless Model for Image Classification
The bag-of-features (BoF) model for image classification has been thoroughly studied over the last decade.
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction
It is much more promising in suppressing noise while preserving sharp features than conventional isotropic mesh smoothing.
F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation
The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignores the minimization of the radius.
Deep Joint Task Learning for Generic Object Extraction
We present a general joint task learning framework, in which each task (either object localization or object segmentation) is tackled via a multi-layer convolutional neural network, and the two networks work collaboratively to boost performance.
Iterated Support Vector Machines for Distance Metric Learning
Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification.
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks
Our model thus advances existing approaches in two aspects: (i) it acts directly on the raw inputs (grayscale-depth data) to conduct recognition instead of relying on hand-crafted features, and (ii) the model structure can be dynamically adjusted accounting for the temporal variations of human activities, i. e. the network configuration is allowed to be partially activated during inference.
On the Optimal Solution of Weighted Nuclear Norm Minimization
Nevertheless, so far the global optimal solution of WNNM problem is not completely solved yet due to its non-convexity in general cases.
A Kernel Classification Framework for Metric Learning
Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade.
Image Set based Collaborative Representation for Face Recognition
One key issue of ISFR is how to effectively and efficiently represent the query face image set by using the gallery face image sets.
Identity Preserving Face Completion for Large Ocular Region Occlusion
Different from the state-of-the-art face inpainting methods that have no control over the synthesized content and can only handle frontal face pose, our approach can faithfully recover the missing content under various head poses while preserving the identity.
Joint Representation and Truncated Inference Learning for Correlation Filter based Tracking
In existing off-line deep learning models for CF trackers, the model adaptation usually is either abandoned or has closed-form solution to make it feasible to learn deep representation in an end-to-end manner.
Unsupervised/Semi-supervised Deep Learning for Low-dose CT Enhancement
To address this issue, we propose an unsupervised DL method for LdCT enhancement that incorporates unlabeled LdCT sinograms directly into the network training.
Convolutional Neural Networks based Intra Prediction for HEVC
To overcome this problem, in this paper, an intra prediction convolutional neural network (IPCNN) is proposed for intra prediction, which exploits the rich context of the current block and therefore is capable of improving the accuracy of predicting the current block.
Model Inconsistent but Correlated Noise: Multi-view Subspace Learning with Regularized Mixture of Gaussians
Different from single view case, MSL should take both common and specific knowledge among different views into consideration.
Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation
In this paper, we present a deep convolutional neural network to capture the inherent properties of image degradation, which can handle different kernels and saturated pixels in a unified framework.
Projective dictionary pair learning for pattern classification
Discriminative dictionary learning (DL) has been widely studied in various pattern classification problems.
Generative Adversarial Learning Towards Fast Weakly Supervised Detection
Without the need of annotating bounding boxes, the existing methods usually follow a two/multi-stage pipeline with an online compulsive stage to extract object proposals, which is an order of magnitude slower than fast fully supervised object detectors such as SSD [31] and YOLO [34].
Weakly-supervised Video Summarization using Variational Encoder-Decoder and Web Prior
Video summarization is a challenging under-constrained problem because the underlying summary of a single video strongly depends on users' subjective understandings.
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
In this paper, we propose a model-based deep learning approach for merging an HrMS and LrHS images to generate a high-resolution hyperspectral (HrHS) image.
Texture Enhanced Image Denoising via Gradient Histogram Preservation
Image denoising is a classical yet fundamental problem in low level vision, as well as an ideal test bed to evaluate various statistical image modeling methods.
Weighted Nuclear Norm Minimization with Application to Image Denoising
In this paper we study the weighted nuclear norm minimization (WNNM) problem, where the singular values are assigned different weights.
Discriminative Learning of Iteration-Wise Priors for Blind Deconvolution
The maximum a posterior (MAP)-based blind deconvolution framework generally involves two stages: blur kernel estimation and non-blind restoration.
SOLD: Sub-Optimal Low-rank Decomposition for Efficient Video Segmentation
In particular, the affinity matrix with the rank fixed can be decomposed into two sub-matrices of low rank, and then we iteratively optimize them with closed-form solutions.
Joint Learning of Single-Image and Cross-Image Representations for Person Re-Identification
Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR).
Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization
Multispectral images (MSI) can help deliver more faithful representation for real scenes than the traditional image system, and enhance the performance of many computer vision tasks.
Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection
Feature representation and object category classification are two key components of most object detection methods.
A Probabilistic Collaborative Representation Based Approach for Pattern Classification
Consequently, we present a probabilistic collaborative representation based classifier (ProCRC), which jointly maximizes the likelihood that a test sample belongs to each of the multiple classes.
The Solution Path Algorithm for Identity-Aware Multi-Object Tracking
The tracker is formulated as a quadratic optimization problem with L0 norm constraints, which we propose to solve with the solution path algorithm.
RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian With Application to Material Recognition
Infinite dimensional covariance descriptors can provide richer and more discriminative information than their low dimensional counterparts.
Learning Dynamic Guidance for Depth Image Enhancement
To address these limitations, we propose a weighted analysis representation model for guided depth image enhancement, which advances the conventional methods in two aspects: (i) task driven learning and (ii) dynamic guidance.
Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising
PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior.
Convolutional Sparse Coding for Image Super-Resolution
Sparse coding (SC) plays an important role in versatile computer vision applications such as image super-resolution (SR).
Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization
The success of fine-grained visual categorization (FGVC) extremely relies on the modeling of appearance and interactions of various semantic parts.
Joint Convolutional Analysis and Synthesis Sparse Representation for Single Image Layer Separation
To exploit the complementary representation mechanisms of ASR and SSR, we integrate the two models and propose a joint convolutional analysis and synthesis (JCAS) sparse representation model.
Extreme Channel Prior Embedded Network for Dynamic Scene Deblurring
In this work, we propose an Extreme Channel Prior embedded Network (ECPeNet) to plug the extreme channel priors (i. e., priors on dark and bright channels) into a network architecture for effective dynamic scene deblurring.
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels
Convolutional neural networks have been proven effective in a variety of image restoration tasks.
Deep Learning on Image Denoising: An overview
However, there are substantial differences in the various types of deep learning methods dealing with image denoising.
Learning Context-Based Non-local Entropy Modeling for Image Compression
To address this issue, we propose a non-local operation for context modeling by employing the global similarity within the context.
Aligning Partially Overlapping Point Sets: an Inner Approximation Algorithm
Our method is also $\epsilon-$globally optimal and thus is guaranteed to be robust.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
Hybrid Trilinear and Bilinear Programming for Aligning Partially Overlapping Point Sets
The resulting lower bound problem has the merit that it can be efficiently solved via linear assignment and low dimensional convex quadratic programming.
Self Sparse Generative Adversarial Networks
In this work, we propose a Self Sparse Generative Adversarial Network (Self-Sparse GAN) that reduces the parameter space and alleviates the zero gradient problem.
Learning Scalable $\ell_\infty$-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression
To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks.
Learning Scalable lY=-Constrained Near-Lossless Image Compression via Joint Lossy Image and Residual Compression
To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks.
VirFace: Enhancing Face Recognition via Unlabeled Shallow Data
In this paper, we propose a novel face recognition method, named VirFace, to effectively apply the unlabeled shallow data for face recognition.
Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds
We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range.
Image denoising using deep CNN with batch renormalization
In this paper, we report the design of a novel network called a batch-renormalization denoising network (BRDNet).
Invertible Network for Unpaired Low-light Image Enhancement
Existing unpaired low-light image enhancement approaches prefer to employ the two-way GAN framework, in which two CNN generators are deployed for enhancement and degradation separately.
NÜWA-LIP: Language Guided Image Inpainting with Defect-free VQGAN
Language guided image inpainting aims to fill in the defective regions of an image under the guidance of text while keeping non-defective regions unchanged.
On Steering Multi-Annotations per Sample for Multi-Task Learning
Despite the remarkable progress, the challenge of optimally learning different tasks simultaneously remains to be explored.
Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment
Particularly, by treating all labeled data as positive samples, PU learning is leveraged to identify negative samples (i. e., outliers) from unlabeled data.
Generative Adversarial Networks for Image Super-Resolution: A Survey
Second, we present popular architectures for GANs in big and small samples for image applications.
Relationship Quantification of Image Degradations
To tackle the first challenge, we proposed a Degradation Relationship Index (DRI) which is defined as the mean drop rate difference in the validation loss between two models which are respectively trained using the anchor degradation and the mixture of the anchor and the auxiliary degradations.
Position-Aware Contrastive Alignment for Referring Image Segmentation
The main challenge of this task is to understand the visual and linguistic content simultaneously and to find the referred object accurately among all instances in the image.
Towards Universal Vision-language Omni-supervised Segmentation
To improve the open-world segmentation ability, we leverage omni-supervised data (i. e., panoptic segmentation data, object detection data, and image-text pairs data) into training, thus enriching the open-world segmentation ability and achieving better segmentation accuracy.
Associating Spatially-Consistent Grouping with Text-supervised Semantic Segmentation
In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs.
Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light Photography
Although deep neural networks have achieved astonishing performance in many vision tasks, existing learning-based methods are far inferior to the physical model-based solutions in extreme low-light sensor noise modeling.
Ranked #1 on
Image Denoising
on ELD SonyA7S2 x200
NUWA-LIP: Language-Guided Image Inpainting With Defect-Free VQGAN
Language-guided image inpainting aims to fill the defective regions of an image under the guidance of text while keeping the non-defective regions unchanged.
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack
Thus, we further propose an indicator to measure the local similarity of models, called robustness similitude.
Improving Transferability of Adversarial Examples via Bayesian Attacks
Our ICLR work advocated for enhancing transferability in adversarial examples by incorporating a Bayesian formulation into model parameters, which effectively emulates the ensemble of infinitely many deep neural networks, while, in this paper, we introduce a novel extension by incorporating the Bayesian formulation into the model input as well, enabling the joint diversification of both the model input and model parameters.
Rethinking Client Drift in Federated Learning: A Logit Perspective
Concretely, FedCSD introduces a class prototype similarity distillation to align the local logits with the refined global logits that are weighted by the similarity between local logits and the global prototype.
Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models
In this work, we introduce a novel Referring Diffusional segmentor (Ref-Diff) for this task, which leverages the fine-grained multi-modal information from generative models.
Cross-Consistent Deep Unfolding Network for Adaptive All-In-One Video Restoration
To empower the framework for eliminating diverse degradations, we devise a Sequence-wise Adaptive Degradation Estimator (SADE) to estimate degradation features for the input corrupted video.
TextField3D: Towards Enhancing Open-Vocabulary 3D Generation with Noisy Text Fields
To this end, an NTFGen module is proposed to model general text latent code in noisy fields.
Rethinking the BERT-like Pretraining for DNA Sequences
To address this research gap, we first conducted a series of exploratory experiments and gained several insightful observations: 1) In the fine-tuning phase of downstream tasks, when using K-mer overlapping tokenization instead of K-mer non-overlapping tokenization, both overlapping and non-overlapping pretraining weights show consistent performance improvement. 2) During the pre-training process, using K-mer overlapping tokenization quickly produces clear K-mer embeddings and reduces the loss to a very low level, while using K-mer non-overlapping tokenization results in less distinct embeddings and continuously decreases the loss.
Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning
By incorporating auxiliary information from CLIP and utilizing prompt fine-tuning, we effectively eliminate noisy samples from the clean set and mitigate confirmation bias during training.
Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection
Existing industrial anomaly detection (IAD) methods predict anomaly scores for both anomaly detection and localization.
Deep Fusion Feature Representation Learning with Hard Mining Center-Triplet Loss for Person Re-identification
The extraction of robust feature representations from pedestrian images through CNNs with a single deterministic pooling operation is problematic as the features in real pedestrian images are complex and diverse.
ConSept: Continual Semantic Segmentation via Adapter-based Vision Transformer
Empirical investigations on the adaptation of existing frameworks to vanilla ViT reveal that incorporating visual adapters into ViTs or fine-tuning ViTs with distillation terms is advantageous for enhancing the segmentation capability of novel classes.
Learning Hierarchical Color Guidance for Depth Map Super-Resolution
On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features in a residual mask manner at the low-level stages.
Dual-Camera Smooth Zoom on Mobile Phones
In this work, we introduce a new task, ie, dual-camera smooth zoom (DCSZ) to achieve a smooth zoom preview.
ShoeModel: Learning to Wear on the User-specified Shoes via Diffusion Model
Specifically, we propose a shoe-wearing system, called Shoe-Model, to generate plausible images of human legs interacting with the given shoes.
IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks
Model Weight Averaging (MWA) is a technique that seeks to enhance model's performance by averaging the weights of multiple trained models.
Latent Code Augmentation Based on Stable Diffusion for Data-free Substitute Attacks
Specifically, we augment the latent codes of the inferred member data with LCA and use them as guidance for SD.
DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection
Most existing data augmentation methods tend to find a compromise in augmenting the data, \textit{i. e.}, increasing the amplitude of augmentation carefully to avoid degrading some data too much and doing harm to the model performance.
Responsible Visual Editing
To mitigate the negative implications of harmful images on research, we create a transparent and public dataset, AltBear, which expresses harmful information using teddy bears instead of humans.
Black-Box Tuning of Vision-Language Models with Effective Gradient Approximation
Parameter-efficient fine-tuning (PEFT) methods have provided an effective way for adapting large vision-language models to specific tasks or scenarios.
Squeeze Training for Adversarial Robustness
The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community.
An Improved Normed-Deformable Convolution for Crowd Counting
However, the heads are not uniformly covered by the sampling points in the deformable convolution, resulting in loss of head information.
FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models
Few-shot class-incremental learning (FSCIL) aims to mitigate the catastrophic forgetting issue when a model is incrementally trained on limited data.
Dimensionality Reduction
Few-Shot Class-Incremental Learning
+2
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
An Intermediate-level Attack Framework on The Basis of Linear Regression
This paper substantially extends our work published at ECCV, in which an intermediate-level attack was proposed to improve the transferability of some baseline adversarial examples.
Learning Hierarchical Dynamics with Spatial Adjacency for Image Enhancement
In various real-world image enhancement applications, the degradations are always non-uniform or non-homogeneous and diverse, which challenges most deep networks with fixed parameters during the inference phase.
Ranked #14 on
Image Dehazing
on SOTS Indoor
VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering
By feeding the retrieved image and question to the VQA model, one can find the images inconsistent with relative caption when the answer by VQA is inconsistent with the answer in the QA pair.
Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images
Methods: In this paper, we propose a direct volumes prediction method based on the end-to-end deep convolutional neural networks (CNN).
Scaled Simplex Representation for Subspace Clustering
However, the negative entries in the coefficient matrix are forced to be positive when constructing the affinity matrix via exponentiation, absolute symmetrization, or squaring operations.
Crowd Counting via Perspective-Guided Fractional-Dilation Convolution
One of the main issues in this task is how to handle the dramatic scale variations of pedestrians caused by the perspective effect.
Robust Deep Ensemble Method for Real-world Image Denoising
In particular, we take well-trained CBDNet, NBNet, HINet, Uformer and GMSNet into denoiser pool, and a U-Net is adopted to predict pixel-wise weighting maps to fuse these denoisers.
Aggregating Long-term Sharp Features via Hybrid Transformers for Video Deblurring
To aggregate long-term sharp features from detected sharp frames, we utilize a global Transformer with multi-scale matching capability.
Learning Real-World Image De-Weathering with Imperfect Supervision
Real-world image de-weathering aims at removing various undesirable weather-related artifacts.
Decoupled Textual Embeddings for Customized Image Generation
To decouple irrelevant attributes (i. e., background and pose) from the subject embedding, we further present several attribute mappers that encode each image as several image-specific subject-unrelated embeddings.
NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset
Despite the significant progress in image denoising, it is still challenging to restore fine-scale details while removing noise, especially in extremely low-light environments.
Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation
The merits of the proposed MCTL are four-fold: 1) the concept of manifold criterion (MC) is first proposed as a measure validating the distribution matching across domains, and domain adaptation is achieved if the MC is satisfied; 2) the proposed MC can well guide the generation of the intermediate domain sharing similar distribution with the target domain, by minimizing the local domain discrepancy; 3) a global generative discrepancy metric (GGDM) is presented, such that both the global and local discrepancy can be effectively and positively reduced; 4) a simplified version of MCTL called MCTL-S is presented under a perfect domain generation assumption for more generic learning scenario.
Deepfake Forensics via An Adversarial Game
In this paper, we advocate adversarial training for improving the generalization ability to both unseen facial forgeries and unseen image/video qualities.
Learning Diverse Tone Styles for Image Retouching
In particular, the style encoder predicts the target style representation of an input image, which serves as the conditional information in the RetouchNet for retouching, while the TSFlow maps the style representation vector into a Gaussian distribution in the forward pass.
Learning Dual-Pixel Alignment for Defocus Deblurring
Generally, DPANet is an encoder-decoder with skip-connections, where two branches with shared parameters in the encoder are employed to extract and align deep features from left and right views, and one decoder is adopted to fuse aligned features for predicting the sharp image.
Towards Instance-adaptive Inference for Federated Learning
However, the performance of the global model is often hampered by non-i. i. d.
Human Co-Parsing Guided Alignment for Occluded Person Re-identification
Most supervised methods propose to train an extra human parsing model aside from the ReID model with cross-domain human parts annotation, suffering from expensive annotation cost and domain gap; Unsupervised methods integrate a feature clustering-based human parsing process into the ReID model, but lacking supervision signals brings less satisfactory segmentation results.
Ranked #3 on
Person Re-Identification
on Occluded-DukeMTMC
Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising
To cope with this issue, this paper presents a multi-level wavelet residual network (MWRN) architecture as well as a progressive training (PTMWRN) scheme to improve image denoising performance.
Self-Supervised Learning for Real-World Super-Resolution from Dual and Multiple Zoomed Observations
In addition, we further take multiple zoomed observations to explore self-supervised RefSR, and present a progressive fusion scheme for the effective utilization of reference images.
Self-supervised Learning to Bring Dual Reversed Rolling Shutter Images Alive
In this paper, we propose a Self-supervised learning framework for Dual reversed RS distortions Correction (SelfDRSC), where a DRSC network can be learned to generate a high framerate GS video only based on dual RS images with reversed distortions.
Remove Cosine Window from Correlation Filter-based Visual Trackers: When and How
Therefore, we in this paper investigate the feasibility to remove cosine window from CF trackers with spatial regularization.
Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression
For the former, we directly apply a CCN to the binarized representation of an image to compute the Bernoulli distribution of each code for entropy estimation.
Learning Single Image Defocus Deblurring with Misaligned Training Pairs
First, in the deblurring module, a bi-directional optical flow-based deformation is introduced to tolerate spatial misalignment between deblurred and ground-truth images.
A Heterogeneous Dynamic Convolutional Neural Network for Image Super-resolution
The lower network utilizes a symmetric architecture to enhance relations of different layers to mine more structural information, which is complementary with a upper network for image super-resolution.
Learning Content-Weighted Deep Image Compression
Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance.
Learning Class-Agnostic Pseudo Mask Generation for Box-Supervised Semantic Segmentation
For learning pseudo mask generator from the auxiliary dataset, we present a bi-level optimization formulation.
A Survey on Leveraging Pre-trained Generative Adversarial Networks for Image Editing and Restoration
Generative adversarial networks (GANs) have drawn enormous attention due to the simple yet effective training mechanism and superior image generation quality.
Inferring and Leveraging Parts from Object Shape for Improving Semantic Image Synthesis
Despite the progress in semantic image synthesis, it remains a challenging problem to generate photo-realistic parts from input semantic map.
Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning
When progressing to a new session, pseudo-features are sampled from old-class distributions combined with training images of the current session to optimize the prompt, thus enabling the model to learn new knowledge while retaining old knowledge.
Flexible Image Denoising with Multi-layer Conditional Feature Modulation
For flexible non-blind image denoising, existing deep networks usually take both noisy image and noise level map as the input to handle various noise levels with a single model.
Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking
Existing visual object tracking usually learns a bounding-box based template to match the targets across frames, which cannot accurately learn a pixel-wise representation, thereby being limited in handling severe appearance variations.
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples
In this paper, by contrast, we opt for the diversity in substitute models and advocate to attack a Bayesian model for achieving desirable transferability.
PLACE: Adaptive Layout-Semantic Fusion for Semantic Image Synthesis
Extensive experiments demonstrate that our approach performs favorably in terms of visual quality, semantic consistency, and layout alignment.
Self-Supervised Video Desmoking for Laparoscopic Surgery
On the other hand, in order to enhance the desmoking performance, we further feed the valuable information from PS frame into models, where a masking strategy and a regularization term are presented to avoid trivial solutions.
Image Inpainting with Learnable Bidirectional Attention Maps
Most convolutional network (CNN)-based inpainting methods adopt standard convolution to indistinguishably treat valid pixels and holes, making them limited in handling irregular holes and more likely to generate inpainting results with color discrepancy and blurriness.
Ranked #2 on
Image Inpainting
on Paris StreetView
Local Patch Network with Global Attention for Infrared Small Target Detection
A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images.
A self-supervised CNN for image watermark removal
Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal.
MC$^2$: Multi-concept Guidance for Customized Multi-concept Generation
Customized text-to-image generation aims to synthesize instantiations of user-specified concepts and has achieved unprecedented progress in handling individual concept.
Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks
However, both GAP and existing HOP methods assume unimodal distributions, which cannot fully capture statistics of convolutional activations, limiting representation ability of deep CNNs, especially for samples with complex contents.
MV-VTON: Multi-View Virtual Try-On with Diffusion Models
To address this challenge, we introduce Multi-View Virtual Try-ON (MV-VTON), which aims to reconstruct the dressing results of a person from multiple views using the given clothes.
Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation
The aspect ratio variation frequently appears in visual tracking and has a severe influence on performance.
Dark and Bright Channel Prior Embedded Network for Dynamic Scene Deblurring
In this work, we propose a Dark and Bright Channel Priors embedded Network (DBCPeNet) to plug the channel priors into a neural network for effective dynamic scene deblurring.
Ranked #33 on
Image Deblurring
on GoPro
(using extra training data)
Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters
In this paper, we defend the problem setting for improving localization performance by leveraging the bounding box regression knowledge from a well-annotated auxiliary dataset.
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution
On the other hand, alignment algorithms in existing VSR methods perform poorly for real-world videos, leading to unsatisfactory results.
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting
In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity in density, scene, etc.
Adaptive Network Combination for Single-Image Reflection Removal: A Domain Generalization Perspective
Meanwhile, diverse testing sets are also provided with different types of reflection and scenes.
Adversarial Contrastive Learning via Asymmetric InfoNCE
Contrastive learning (CL) has recently been applied to adversarial learning tasks.
Learning Convolutional Networks for Content-weighted Image Compression
Therefore, the encoder, decoder, binarizer and importance map can be jointly optimized in an end-to-end manner by using a subset of the ImageNet database.
Image Inpainting with Edge-guided Learnable Bidirectional Attention Maps
In this paper, we present an edge-guided learnable bidirectional attention map (Edge-LBAM) for improving image inpainting of irregular holes with several distinct merits.
Retrieval-based Spatially Adaptive Normalization for Semantic Image Synthesis
Semantic image synthesis is a challenging task with many practical applications.
Symmetry-Aware Transformer-based Mirror Detection
Mirror detection aims to identify the mirror regions in the given input image.
ImaginaryNet: Learning Object Detectors without Real Images and Annotations
Given a class label, the language model is used to generate a full description of a scene with a target object, and the text-to-image model deployed to generate a photo-realistic image.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition
In this paper, we circumvent this issue by presenting a joint framework for both unbounded recommendation of camera view and image composition (i. e., UNIC).
What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective
Recent works have demonstrated that global covariance pooling (GCP) has the ability to improve performance of deep convolutional neural networks (CNNs) on visual classification task.
Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones
However, BiRNN is intrinsically offline because it uses backward recurrent modules to propagate from the last to current frames, which causes high latency and large memory consumption.
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
Motivated by the theoretical results in \cite{mansour2009domain}, the target distribution can be represented as the weighted combination of source distributions, and, the multi-source unsupervised domain adaptation via DCTN is then performed as two alternating steps: i) It deploys multi-way adversarial learning to minimize the discrepancy between the target and each of the multiple source domains, which also obtains the source-specific perplexity scores to denote the possibilities that a target sample belongs to different source domains.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection
Generally, with given pseudo ground-truths generated from the well-trained WSOD network, we propose a two-module iterative training algorithm to refine pseudo labels and supervise better object detector progressively.
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
A heterogeneous group CNN for image super-resolution
To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN.
Semantic-shape Adaptive Feature Modulation for Semantic Image Synthesis
Obviously, a fine-grained part-level semantic layout will benefit object details generation, and it can be roughly inferred from an object's shape.
VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization
In this paper, we propose a VQGAN-based framework (i. e., VQ-Font) to enhance glyph fidelity through token prior refinement and structure-aware enhancement.
Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenes
The color component is estimated from aligned multi-exposure images, while the structure one is generated through a structure-focused network that is supervised by the color component and an input reference (\eg, medium-exposure) image.
Bringing Events Into Video Deblurring With Non-Consecutively Blurry Frames
EFM can also be easily incorporated into existing deblurring networks, making event-driven deblurring task benefit from state-of-the-art deblurring methods.
UniM$^2$AE: Multi-modal Masked Autoencoders with Unified 3D Representation for 3D Perception in Autonomous Driving
The extension makes it possible to back-project the informative features, obtained by fusing features from both modalities, into their native modalities to reconstruct the multiple masked inputs.
Simultaneous Fidelity and Regularization Learning for Image Restoration
For blind deconvolution, as estimation error of blur kernel is usually introduced, the subsequent non-blind deconvolution process does not restore the latent image well.
Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance
To be specific, the reflection layer is firstly estimated due to that it generally is much simpler and is relatively easier to estimate.
Self-Supervised Image Restoration with Blurry and Noisy Pairs
By learning in a collaborative manner, the deblurring and denoising tasks in our method can benefit each other.
Learning Federated Visual Prompt in Null Space for MRI Reconstruction
Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy.
RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution
The main challenge of BurstSR is to effectively combine the complementary information from input frames, while existing methods still struggle with it.
A cross Transformer for image denoising
To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes.
Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation
Specifically, we introduce class-specific auxiliary weights into the original MMD for exploiting the class prior probability on source and target domains, whose challenge lies in the fact that the class label in target domain is unavailable.
Learning Symmetry Consistent Deep CNNs for Face Completion
As for missing pixels on both of half-faces, we present a generative reconstruction subnet together with a perceptual symmetry loss to enforce symmetry consistency of recovered structures.
Ranked #1 on
Facial Inpainting
on VggFace2
Sentence-level Prompts Benefit Composed Image Retrieval
Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption.
Ranked #1 on
Image Retrieval
on CIRR
Exposure Bracketing is All You Need for Unifying Image Restoration and Enhancement Tasks
It is highly desired but challenging to acquire high-quality photos with clear content in low-light environments.
Perspective-Guided Convolution Networks for Crowd Counting
In this paper, we propose a novel perspective-guided convolution (PGC) for convolutional neural network (CNN) based crowd counting (i. e. PGCNet), which aims to overcome the dramatic intra-scene scale variations of people due to the perspective effect.
Self-Promoted Supervision for Few-Shot Transformer
The few-shot learning ability of vision transformers (ViTs) is rarely investigated though heavily desired.
Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks
In this paper, we present a novel one-shot generative domain adaption method, i. e., DiFa, for diverse generation and faithful adaptation.
Designing and Training of A Dual CNN for Image Denoising
The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network.
LPT: Long-tailed Prompt Tuning for Image Classification
For better effectiveness, we divide prompts into two groups: 1) a shared prompt for the whole long-tailed dataset to learn general features and to adapt a pretrained model into target domain; and 2) group-specific prompts to gather group-specific features for the samples which have similar features and also to empower the pretrained model with discrimination ability.
Ranked #1 on
Long-tail Learning
on CIFAR-100-LT (ρ=100)
(using extra training data)
Improving Image Restoration through Removing Degradations in Textual Representations
To address the cross-modal assistance, we propose to map the degraded images into textual representations for removing the degradations, and then convert the restored textual representations into a guidance image for assisting image restoration.
SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models
In the rapidly evolving landscape of Large Language Models (LLMs), ensuring robust safety measures is paramount.
Learning Semantic Person Image Generation by Region-Adaptive Normalization
In the first stage, we predict the target semantic parsing maps to eliminate the difficulties of pose transfer and further benefit the latter translation of per-region appearance style.
Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning
In this paper, we focus on a particular setting of learning adaptive prompts on the fly for each test sample from an unseen new domain, which is known as test-time prompt tuning (TPT).
DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior
The priors are then regarded as input conditions to maintain reasonable geometries, in which conditional LoRA and weighted score are further proposed to optimize detailed textures.
Multi-stage image denoising with the wavelet transform
In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i. e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB).
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces
To evaluate our proposed MetaF2N, we have collected a real-world low-quality dataset with one or multiple faces in each image, and our MetaF2N achieves superior performance on both synthetic and real-world datasets.
SmartControl: Enhancing ControlNet for Handling Rough Visual Conditions
The key idea of our SmartControl is to relax the visual condition on the areas that are conflicted with text prompts.
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time
Moreover, on the seemingly implausible x16 interpolation task, our method outperforms existing methods by more than 1. 5 dB in terms of PSNR.
Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser
To begin with, the pre-trained denoiser is used to generate the pseudo clean images for the test images.
Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks
In this work, aiming at a general and comprehensive way for neural network acceleration, we develop a Wavelet-like Auto-Encoder (WAE) that decomposes the original input image into two low-resolution channels (sub-images) and incorporate the WAE into the classification neural networks for joint training.
Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations
For this purpose, we take the telephoto image instead of an additional high-resolution image as the supervision information and select a center patch from it as the reference to super-resolve the corresponding short-focus image patch.
Image Super-resolution with An Enhanced Group Convolutional Neural Network
In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR).
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation
It simply encourages the variation of output caused by perturbations on different latent dimensions to be orthogonal, and the Jacobian with respect to the input is calculated to represent this variation.
Is Second-order Information Helpful for Large-scale Visual Recognition?
The main challenges involved are robust covariance estimation given a small sample of large-dimensional features and usage of the manifold structure of covariance matrices.
Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising
And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas.
Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision
To diminish the effect of color inconsistency in image alignment, we introduce to use a global color mapping (GCM) module to generate an initial sRGB image given the input raw image, which can keep the spatial location of the pixels unchanged, and the target sRGB image is utilized to guide GCM for converting the color towards it.
HS-Diffusion: Semantic-Mixing Diffusion for Head Swapping
We blend the semantic layouts of source head and source body, and then inpaint the transition region by the semantic layout generator, achieving a coarse-grained head swapping.
Data Augmentation for Object Detection via Progressive and Selective Instance-Switching
For guiding our IS to obtain better object performance, we explore issues of instance imbalance and class importance in datasets, which frequently occur and bring adverse effect on detection performance.
Texts as Images in Prompt Tuning for Multi-Label Image Recognition
Nonetheless, visual data (e. g., images) is by default prerequisite for learning prompts in existing methods.
Contrastive Learning
Multi-label Image Recognition with Partial Labels
Asymmetric CNN for image super-resolution
In this paper, we propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a memory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution.
