Search Results for author:
Found 54 papers, 23 papers with code
Large-Scale Few-Shot Learning via Multi-Modal Knowledge Discovery
It is a challenging problem since (1) the identifying process is susceptible to over-fitting with limited samples of an object, and (2) the sample imbalance between a base (known knowledge) category and a novel category is easy to bias the recognition results.
Wavelet-Based Dual-Branch Network for Image Demoiréing
When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.
API-Net: Robust Generative Classifier via a Single Discriminator
On the contrary, a discriminative classifier only models the conditional distribution of labels given inputs, but benefits from effective optimization owing to its succinct structure.
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection
To tackle this problem, we propose a depth solving system that fully explores the visual clues from the subtasks in M3OD and generates multiple estimations for the depth of each target.
Prompt Distribution Learning
We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks.
Learning Enriched Illuminants for Cross and Single Sensor Color Constancy
Color constancy aims to restore the constant colors of a scene under different illuminants.
Differentiated Relevances Embedding for Group-based Referring Expression Comprehension
In particular, based on the visual and textual semantic features, RMSL conducts an adaptive learning cycle upon triplet ranking, where (1) the target-negative region-expression pairs with low within-group relevances are used preferentially in model training to distinguish the primary semantics of the target objects, and (2) an across-group relevance regularization is integrated into model training to balance the bias of group priority.
IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization
In this paper, we observe an interesting phenomenon of intra-class heterogeneity in real data and show that existing methods fail to retain this property in their synthetic images, which causes a limited performance increase.
Motion Deblurring with Real Events
In this paper, we propose an end-to-end learning framework for event-based motion deblurring in a self-supervised manner, where real-world events are exploited to alleviate the performance degradation caused by data inconsistency.
Prioritized Subnet Sampling for Resource-Adaptive Supernet Training
Then, the sampling will gradually be prone to sampling subnets from the subnet pools.
Wavelet-Based Network For High Dynamic Range Imaging
The frequency-guided upsampling module reconstructs details from multiple frequency-specific components with rich details.
Training Compact CNNs for Image Classification using Dynamic-coded Filter Fusion
Each filter in our DCFF is firstly given an inter-similarity distribution with a temperature parameter as a filter proxy, on top of which, a fresh Kullback-Leibler divergence based dynamic-coded criterion is proposed to evaluate the filter importance.
Multi-Target Domain Adaptation with Collaborative Consistency Learning
To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights.
Uformer: A General U-Shaped Transformer for Image Restoration
Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.
Ranked #1 on
Deblurring
on RealBlur-R (trained on GoPro)
Towards Compact CNNs via Collaborative Compression
Channel pruning and tensor decomposition have received extensive attention in convolutional neural network compression.
Multiple instance active learning for object detection
Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection.
Ranked #1 on
Active Object Detection
on PASCAL VOC 07+12
ReCU: Reviving the Dead Weights in Binary Neural Networks
We prove that reviving the "dead weights" by ReCU can result in a smaller quantization error.
Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
To address the task of SSDA, a novel framework based on dual-level domain mixing is proposed.
Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation
Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain.
Multi-Source Unsupervised Domain Adaptation
Semantic Segmentation
+1
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
In this paper, we present a very simple yet effective method named Neighbor2Neighbor to train an effective image denoising model with only noisy images.
Active Learning for Lane Detection: A Knowledge Distillation Approach
The experiments show that our method achieves new state-of-the-art on the lane detection benchmarks.
TRAR: Routing the Attention Spans in Transformer for Visual Question Answering
Due to the superior ability of global dependency modeling, Transformer and its variants have become the primary choice of many vision-and-language tasks.
Occlude Them All: Occlusion-Aware Attention Network for Occluded Person Re-ID
To avoid such problematic models in occluded person ReID, we propose the Occlusion-Aware Mask Network (OAMN).
Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs
In this paper, we propose a self-adaptive learning method for demoiréing a high-frequency image, with the help of an additional defocused moiré-free blur image.
Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs
In this paper, we propose a self-adaptive learning method for demoireing a high-frequency image, with the help of an additional defocused moire-free blur image.
Binarized Neural Architecture Search for Efficient Object Recognition
In this paper, binarized neural architecture search (BNAS), with a search space of binarized convolutions, is introduced to produce extremely compressed models to reduce huge computational cost on embedded devices for edge computing.
Light Field View Synthesis via Aperture Disparity and Warping Confidence Map
This paper presents a learning-based approach to synthesize the view from an arbitrary camera position given a sparse set of images.
Dual Distribution Alignment Network for Generalizable Person Re-Identification
Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID), which trains the model using labels from the source domain alone, and then directly adopts the trained model to the target domain without model updating.
Domain Generalization
Generalizable Person Re-identification
Wavelet-Based Dual-Branch Network for Image Demoireing
When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.
Projection & Probability-Driven Black-Box Attack
For reducing the solution space, we first model the adversarial perturbation optimization problem as a process of recovering frequency-sparse perturbations with compressed sensing, under the setting that random noise in the low-frequency space is more likely to be adversarial.
High-Order Residual Network for Light Field Super-Resolution
In this paper, we propose a novel high-order residual network to learn the geometric features hierarchically from the LF for reconstruction.
Context-Transformer: Tackling Object Confusion for Few-Shot Detection
Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors.
SketchyCOCO: Image Generation from Freehand Scene Sketches
We introduce the first method for automatic image generation from scene-level freehand sketches.
Ranked #2 on
Sketch-to-Image Translation
on SketchyCOCO
Filter Sketch for Network Pruning
Our approach, referred to as FilterSketch, encodes the second-order information of pre-trained weights, which enables the representation capacity of pruned networks to be recovered with a simple fine-tuning procedure.
Multiple Anchor Learning for Visual Object Detection
In this paper, we propose a Multiple Instance Learning (MIL) approach that selects anchors and jointly optimizes the two modules of a CNN-based object detector.
Ranked #87 on
Object Detection
on COCO test-dev
Binarized Neural Architecture Search
A variant, binarized neural architecture search (BNAS), with a search space of binarized convolutions, can produce extremely compressed models.
GBCNs: Genetic Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs
The BGA method is proposed to modify the binary process of GBCNs to alleviate the local minima problem, which can significantly improve the performance of 1-bit DCNNs.
An End-to-End Foreground-Aware Network for Person Re-Identification
By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to the backgrounds, which greatly reduces the negative impacts of changing backgrounds on matching an identical across different camera views.
Circulant Binary Convolutional Networks: Enhancing the Performance of 1-bit DCNNs with Circulant Back Propagation
The CiFs can be easily incorporated into existing deep convolutional neural networks (DCNNs), which leads to new Circulant Binary Convolutional Networks (CBCNs).
Unsupervised Image Super-Resolution with an Indirect Supervised Path
The task of single image super-resolution (SISR) aims at reconstructing a high-resolution (HR) image from a low-resolution (LR) image.
RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs
Binarized convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications.
Bayesian Optimized 1-Bit CNNs
Deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models.
Multinomial Distribution Learning for Effective Neural Architecture Search
Therefore, NAS can be transformed to a multinomial distribution learning problem, i. e., the distribution is optimized to have a high expectation of the performance.
Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression
The relationship between the input feature maps and 2D kernels is revealed in a theoretical framework, based on which a kernel sparsity and entropy (KSE) indicator is proposed to quantitate the feature map importance in a feature-agnostic manner to guide model compression.
Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation
The advancement of deep convolutional neural networks (DCNNs) has driven significant improvement in the accuracy of recognition systems for many computer vision tasks.
Modulated Convolutional Networks
In this paper, we propose new Modulated Convolutional Networks (MCNs) to improve the portability of CNNs via binarized filters.
Memory Attention Networks for Skeleton-based Action Recognition
Specifically, the TARM is deployed in a residual learning module that employs a novel attention learning network to recalibrate the temporal attention of frames in a skeleton sequence.
Ranked #62 on
Skeleton Based Action Recognition
on NTU RGB+D
One-Two-One Networks for Compression Artifacts Reduction in Remote Sensing
Compression artifacts reduction (CAR) is a challenging problem in the field of remote sensing.
Gabor Convolutional Networks
Steerable properties dominate the design of traditional filters, e. g., Gabor filters, and endow features the capability of dealing with spatial transformations.
A Maximum Entropy Feature Descriptor for Age Invariant Face Recognition
In this paper, we propose a new approach to overcome the representation and matching problems in age invariant face recognition.
Transitive Distance Clustering with K-Means Duality
We propose a very intuitive and simple approximation for the conventional spectral clustering methods.
Separation of Line Drawings Based on Split Faces for 3D Object Reconstruction
Reconstructing 3D objects from single line drawings is often desirable in computer vision and graphics applications.
The BeiHang Keystroke Dynamics Authentication System
Keystroke Dynamics is an important biometric solution for person authentication.
