Search Results for author:
Found 140 papers, 75 papers with code
SSCGAN: Facial Attribute Editing via Style Skip Connections
Each connection extracts the style feature of the latent feature maps in the encoder and then performs a residual learning based mapping function in the global information space guided by the target attributes.
Single-temporal Supervised Remote Change Detection for Domain Generalization
In this paper, we propose a multimodal contrastive learning (ChangeCLIP) based on visual-language pre-training for change detection domain generalization.
Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection
Next, a shape-aware and a brightness-aware module are designed to improve the capacity for representation learning.
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark
Moreover, current metrics such as AU-ROC have nearly reached saturation on simple datasets, which prevents a comprehensive evaluation of different methods.
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches.
Deepfake Generation and Detection: A Benchmark and Survey
Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions, which has significant application potential in fields such as entertainment, movie production, digital human creation, to name a few.
DiffFAE: Advancing High-fidelity One-shot Facial Appearance Editing with Space-sensitive Customization and Semantic Preservation
Facial Appearance Editing (FAE) aims to modify physical attributes, such as pose, expression and lighting, of human facial images while preserving attributes like identity and background, showing great importance in photograph.
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
To address this issue, we introduce a Multi-class Implicit Neural representation Transformer for unified Anomaly Detection (MINT-AD), which leverages the fine-grained category information in the training stage.
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction.
T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Image
Pixel2Mesh (P2M) is a classical approach for reconstructing 3D shapes from a single color image through coarse-to-fine mesh deformation.
TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation
Texturing 3D humans with semantic UV maps remains a challenge due to the difficulty of acquiring reasonably unfolded UV.
DMAD: Dual Memory Bank for Real-World Anomaly Detection
To address the challenge of real-world anomaly detection, we propose a new framework named Dual Memory bank enhanced representation learning for Anomaly Detection (DMAD).
HCPM: Hierarchical Candidates Pruning for Efficient Detector-Free Matching
Deep learning-based image matching methods play a crucial role in computer vision, yet they often suffer from substantial computational demands.
Tuning-Free Image Customization with Image and Text Guidance
To our knowledge, this is the first tuning-free method that concurrently utilizes text and image guidance for image customization in specific regions.
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection
Finally, we report the results of popular IAD methods on the Real-IAD dataset, providing a highly challenging benchmark to promote the development of the IAD field.
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection
In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of ``learning shortcuts'', wherein the model fails to learn the patterns of normal samples as it should, opting instead for shortcuts such as identity mapping or artificial noise elimination.
PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models
On top of that, PointSeg can incorporate with various segmentation models and even surpasses the supervised methods.
DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation
Our DiffuMatting shows several potential applications (e. g., matting-data generator, community-friendly art design and controllable generation).
Dual-path Frequency Discriminators for Few-shot Anomaly Detection
We consider anomaly detection as a discriminative classification problem, wherefore the dual-path feature discrimination module is employed to detect and locate the image-level and feature-level anomalies in the feature space.
LORS: Low-rank Residual Structure for Parameter-Efficient Network Stacking
Deep learning models, particularly those based on transformers, often employ numerous stacked structures, which possess identical architectures and perform similar functions.
Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability
In this paper, we extend auto-regressive models to 3D domains, and seek a stronger ability of 3D shape generation by improving auto-regressive models at capacity and scalability simultaneously.
UniM-OV3D: Uni-Modality Open-Vocabulary 3D Scene Understanding with Fine-Grained Feature Representation
3D open-vocabulary scene understanding aims to recognize arbitrary novel categories beyond the base label space.
Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection
Industrial anomaly detection is generally addressed as an unsupervised task that aims at locating defects with only normal training samples.
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible.
A Generalist FaceX via Learning Unified Facial Representation
This work presents FaceX framework, a novel facial generalist model capable of handling diverse facial tasks simultaneously.
Beyond Prototypes: Semantic Anchor Regularization for Better Representation Learning
By pulling the learned features to these semantic anchors, several advantages can be attained: 1) the intra-class compactness and naturally inter-class separability, 2) induced bias or errors from feature learning can be avoided, and 3) robustness to the long-tailed problem.
MatchDet: A Collaborative Framework for Image Matching and Object Detection
Image matching and object detection are two fundamental and challenging tasks, while many related applications consider them two individual tasks (i. e. task-individual).
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection
Following this spirit, this paper explores plain ViT architecture for MUAD.
PortraitBooth: A Versatile Portrait Model for Fast Identity-preserved Personalization
Moreover, these methods often grapple with identity distortion and limited expression diversity.
DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection
Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection.
AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model
Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data.
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection
Large Multimodal Model (LMM) GPT-4V(ision) endows GPT-4 with visual grounding capabilities, making it possible to handle certain tasks through the Visual Question Answering (VQA) paradigm.
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection
This paper considers zero-shot Anomaly Detection (AD), performing AD without reference images of the test objects.
Real3D-AD: A Dataset of Point Cloud Anomaly Detection
High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing.
Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region
To solve the problem, we propose Compositional Neural Painter, a novel stroke-based rendering framework which dynamically predicts the next painting region based on the current canvas, instead of dividing the image plane uniformly into painting regions.
Dynamic Frame Interpolation in Wavelet Domain
Video frame interpolation is an important low-level vision task, which can increase frame rate for more fluent visual experience.
Toward High Quality Facial Representation Learning
To improve the facial representation quality, we use feature map of a pre-trained visual backbone as a supervision item and use a partially pre-trained decoder for mask image modeling.
Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption
To address these problems, we propose a novel phasic content fusing few-shot diffusion model with directional distribution consistency loss, which targets different learning objectives at distinct training stages of the diffusion model.
IIDM: Inter and Intra-domain Mixing for Semi-supervised Domain Adaptation in Semantic Segmentation
In this paper, we highlight the significance of exploiting the intra-domain information between the labeled target data and unlabeled target data.
PVG: Progressive Vision Graph for Vision Recognition
Convolution-based and Transformer-based vision backbone networks process images into the grid or sequence structures, respectively, which are inflexible for capturing irregular objects.
Transferable Decoding with Visual Entities for Zero-Shot Image Captioning
In this paper, we propose ViECap, a transferable decoding model that leverages entity-aware decoding to generate descriptions in both seen and unseen scenarios.
RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation
Glass-like objects are widespread in daily life but remain intractable to be segmented for most existing methods.
Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection
In this paper, we revisit the importance of feature difference for change detection in RSI, and propose a series of operations to fully exploit the difference information: Alignment, Perturbation and Decoupling (APD).
Dual Path Transformer with Partition Attention
This paper introduces a novel attention mechanism, called dual attention, which is both efficient and effective.
Learning Global-aware Kernel for Image Harmonization
To address this issue, we propose a novel Global-aware Kernel Network (GKNet) to harmonize local regions with comprehensive consideration of long-distance background references.
Ranked #5 on
Image Harmonization
on iHarmony4
High-fidelity Generalized Emotional Talking Face Generation with Multi-modal Emotion Space Learning
Specifically, we supplement the emotion style in text prompts and use an Aligned Multi-modal Emotion encoder to embed the text, image, and audio emotion modality into a unified space, which inherits rich semantic prior from CLIP.
Clustered-patch Element Connection for Few-shot Learning
According to this, we propose a novel Clustered-patch Element Connection (CEC) layer to correct the mismatch problem.
Ranked #47 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
NeRF-Loc: Visual Localization with Conditional Neural Radiance Field
We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer.
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution
Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant.
Learning Versatile 3D Shape Generation with Improved AR Models
Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space.
MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection
Despite the remarkable progress made by modern detection models, this challenge is particularly evident in the semi-supervised case.
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Then we derive two specific attention modules, named SpatialFormer Semantic Attention (SFSA) and SpatialFormer Target Attention (SFTA), to enhance the target object regions while reduce the background distraction.
Calibrated Teacher for Sparsely Annotated Object Detection
Recent works on sparsely annotated object detection alleviate this problem by generating pseudo labels for the missing annotations.
Iterative Few-shot Semantic Segmentation from Image Label Text
Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images.
Ranked #40 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Multimodal Industrial Anomaly Detection via Hybrid Fusion
2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields.
Ranked #3 on
RGB+3D Anomaly Detection and Segmentation
on MVTEC 3D-AD
(using extra training data)
Contrastive Learning
RGB+3D Anomaly Detection and Segmentation
Learning from Noisy Labels with Decoupled Meta Label Purifier
Training deep neural networks(DNN) with noisy labels is challenging since DNN can easily memorize inaccurate labels, leading to poor generalization ability.
Learning with Noisy labels via Self-supervised Adversarial Noisy Masking
Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms.
Ranked #2 on
Image Classification
on Clothing1M
(using extra training data)
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
We realize that the lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications.
Deep Industrial Image Anomaly Detection: A Survey
In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets.
Rethinking Mobile Block for Efficient Attention-based Models
This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance.
Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation
In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework.
Learning Versatile 3D Shape Generation with Improved Auto-regressive Models
Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space.
Multi-Centroid Task Descriptor for Dynamic Class Incremental Inference
As a result, our dynamic inference network is trained independently of baseline and provides a flexible, efficient solution to distinguish between tasks.
Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection
Specifically, we first propose a normality recall memory (NR Memory) to strengthen the normality of student-generated features by recalling the stored normal information.
Ranked #11 on
Anomaly Detection
on MVTec AD
Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space
Specifically, we use the contrastive constraint to help CALoss learn a representation space with shape similarity, while we introduce the adversarial strategy to help CALoss mine differences between reconstructed results and ground truths.
Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild
To address this problem, this paper presents a novel Neural Proto-face Field (NPF) for unsupervised robust 3D face modeling.
Instance and Category Supervision are Alternate Learners for Continual Learning
Continual Learning (CL) is the constant development of complex behaviors by building upon previously acquired skills.
PatchMix Augmentation to Identify Causal Features in Few-shot Learning
We theoretically show that such an augmentation mechanism, different from existing ones, is able to identify the causal features.
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision
This paper builds a unified framework to perform effective noisy-proposal suppression and to interact between global and local features for robust feature learning.
Delving into Transformer for Incremental Semantic Segmentation
In contrast, a Transformer based method has a natural advantage in curbing catastrophic forgetting due to its ability to model both long-term and short-term tasks.
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective
In this paper, for the first time, we investigate the contributions of different distance metrics, and propose an adaptive fusion scheme, bringing significant improvements in few-shot classification.
tSF: Transformer-based Semantic Filter for Few-Shot Learning
Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples.
Towards Continual Adaptation in Industrial Anomaly Detection
We believe that the proposed task and benchmark will be beneficial to the field of AD.
Rethinking Dimensionality Reduction in Grid-based 3D Object Detection
In MDRNet, the Spatial-aware Dimensionality Reduction (SDR) is designed to dynamically focus on the valuable parts of the object during voxel-to-BEV feature transformation.
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances.
Ranked #3 on
Few-Shot Object Detection
on MS-COCO (1-shot)
Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution
Although some Unsupervised Degradation Prediction (UDP) methods are proposed to bypass this problem, the \textit{inconsistency} between degradation embedding and SR feature is still challenging.
Multi-Forgery Detection Challenge 2022: Push the Frontier of Unconstrained and Diverse Forgery Detection
In this paper, we present the solutions from the Top 3 teams, in order to boost the research work in the field of image forgery detection.
SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer
Point cloud completion has become increasingly popular among generation tasks of 3D point clouds, as it is a challenging yet indispensable problem to recover the complete shape of a 3D object from its partial observation.
Ranked #7 on
Point Cloud Completion
on Completion3D
Adaptive Assignment for Geometry Aware Local Feature Matching
The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance.
Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation
Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain.
Ranked #14 on
Unsupervised Domain Adaptation
on SYNTHIA-to-Cityscapes
EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm
Motivated by biological evolution, this paper explains the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derives that both have consistent mathematical formulation.
How to Reduce Change Detection to Semantic Segmentation
And most segmentation networks can be adapted to solve the CD problems with our MTF module.
Ranked #1 on
Change Detection
on PCD
IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation
Prevailing video frame interpolation algorithms, that generate the intermediate frames from consecutive inputs, typically rely on complex model architectures with heavy parameters or large delay, hindering them from diverse real-time applications.
Ranked #1 on
Video Frame Interpolation
on Middlebury
OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving
To this end, we present OpenCalib, a calibration toolbox that contains a rich set of various sensor calibration methods.
UniInst: Unique Representation for End-to-End Instance Segmentation
Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e. g., multiple boxes, grids, and anchor points) are inferred for one instance, which leads to multiple duplicated predictions.
FRIH: Fine-grained Region-aware Image Harmonization
Image harmonization aims to generate a more realistic appearance of foreground and background for a composite image.
Surface Representation for Point Clouds
Based on a simple baseline of PointNet++ (SSG version), Umbrella RepSurf surpasses the previous state-of-the-art by a large margin for classification, segmentation and detection on various benchmarks in terms of performance and efficiency.
Ranked #7 on
3D Point Cloud Classification
on ModelNet40
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
High-resolution Iterative Feedback Network for Camouflaged Object Detection
Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings.
CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learning
We propose a new encoder which embeds real faces into Z+ space and proposes a dual-path training strategy to better cope with the adapted decoder and eliminate the artifacts.
Learning Distinctive Margin toward Active Domain Adaptation
Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under unsupervised or few-shot semi-supervised settings, recently the solution of active learning started to attract more attention due to its suitability in transferring model in a more practical way with limited annotation resource on target data.
Class-Aware Contrastive Semi-Supervised Learning
Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization.
A Survey of Visual Sensory Anomaly Detection
In this survey, we are the first one to provide a comprehensive review of visual sensory AD and category into three levels according to the form of anomalies.
STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation
In contrast, we present a simple and efficient single-stage VIS framework based on the instance segmentation method CondInst by adding an extra tracking head.
Ranked #36 on
Video Instance Segmentation
on YouTube-VIS validation
ASFD: Automatic and Scalable Face Detector
Along with current multi-scale based detectors, Feature Aggregation and Enhancement (FAE) modules have shown superior performance gains for cutting-edge object detection.
Ranked #1 on
Face Detection
on WIDER Face (Medium)
CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation
Recently, there is growing attention on one-stage panoptic segmentation methods which aim to segment instances and stuff jointly within a fully convolutional pipeline efficiently.
Blind Face Restoration via Integrating Face Shape and Generative Priors
Moreover, we introduce hybrid-level losses to jointly train the shape and generative priors together with other network parts such that these two priors better adapt to our blind face restoration task.
En-Compactness: Self-Distillation Embedding & Contrastive Generation for Generalized Zero-Shot Learning
Our ICCE promotes intra-class compactness with inter-class separability on both seen and unseen classes in the embedding space and visual feature space.
ISDNet: Integrating Shallow and Deep Networks for Efficient Ultra-High Resolution Segmentation
The huge burden of computation and memory are two obstacles in ultra-high resolution image segmentation.
Learning To Restore 3D Face From In-the-Wild Degraded Images
In-the-wild 3D face modelling is a challenging problem as the predicted facial geometry and texture suffer from a lack of reliable clues or priors, when the input images are degraded.
Physically-Guided Disentangled Implicit Rendering for 3D Face Modeling
This paper presents a novel Physically-guided Disentangled Implicit Rendering (PhyDIR) framework for high-fidelity 3D face modeling.
Learning To Memorize Feature Hallucination for One-Shot Image Generation
In this paper, we propose a novel model to explicitly learn and memorize reusable features that can help hallucinate novel category images.
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization
In this paper, we propose a novel framework built upon the transformer, termed LCTR (Local Continuity TRansformer), which targets at enhancing the local perception capability of global features among long-range feature dependencies.
Ranking Distance Calibration for Cross-Domain Few-Shot Learning
The calibrated distance in this target-aware non-linear subspace is complementary to that in the pre-trained representation.
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation
In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner.
Ranked #45 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
LSTC: Boosting Atomic Action Detection with Long-Short-Term Context
We demonstrate that both temporal grains are beneficial to atomic action recognition.
Robust Learning with Adaptive Sample Credibility Modeling
For the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus to alleviate the effect from potential hard noisy samples in clean set.
Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework
In this paper, we propose a purely point-based framework for joint crowd counting and individual localization.
Ranked #2 on
Crowd Counting
on ShanghaiTech A
Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting
Therefore, we propose a novel count interval partition criterion called Uniform Error Partition (UEP), which always keeps the expected counting error contributions equal for all intervals to minimize the prediction risk.
Learning To Restore Hazy Video: A New Real-World Dataset and a New Method
On the other hand, the video dehazing algorithms, which can acquire more satisfying dehazing results by exploiting the temporal redundancy from neighborhood hazy frames, receive less attention due to the absence of the video dehazing datasets.
HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results.
Ranked #7 on
Face Swapping
on FaceForensics++
Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection
Non-parametric face modeling aims to reconstruct 3D face only from images without shape assumptions.
Context-Aware Image Inpainting with Learned Semantic Priors
Based on the semantic priors, we further propose a context-aware image inpainting model, which adaptively integrates global semantics and local features in a unified image generator.
Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model
Inspired by biological evolution, we explain the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derive that both of them have consistent mathematical representation.
SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking
In addition, we add a localization branch to predict the localization accuracy, so that it can work as the replacement of the regression assistance link during inference.
SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes
To this end, we generate self-contrastive background prototypes directly from the query image, with which we enable the construction of complete sample pairs and thus a complementary and auxiliary segmentation task to achieve the training of a better segmentation model.
Learning Dynamic Alignment via Meta-filter for Few-shot Learning
Our insight is that these methods would lead to poor adaptation with redundant matching, and leveraging channel-wise adjustment is the key to well adapting the learned knowledge to new classes.
Learning Salient Boundary Feature for Anchor-free Temporal Action Localization
Temporal action localization is an important yet challenging task in video understanding.
Learning Comprehensive Motion Representation for Action Recognition
For action recognition learning, 2D CNN-based methods are efficient but may yield redundant features due to applying the same 2D convolution kernel to each frame.
Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting System
Face authentication on mobile end has been widely applied in various scenarios.
Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework
In this paper, we propose a purely point-based framework for joint crowd counting and individual localization.
Uniformity in Heterogeneity: Diving Deep Into Count Interval Partition for Crowd Counting
Therefore, we propose a novel count interval partition criterion called Uniform Error Partition (UEP), which always keeps the expected counting error contributions equal for all intervals to minimize the prediction risk.
Frequency Consistent Adaptation for Real World Super Resolution
From this point of view, we design a novel Frequency Consistent Adaptation (FCA) that ensures the frequency domain consistency when applying existing SR methods to the real scene.
They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning
Semi-Supervised Learning (SSL) with mismatched classes deals with the problem that the classes-of-interests in the limited labeled data is only a subset of the classes in massive unlabeled data.
Adversarial Refinement Network for Human Motion Prediction
Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation.
Adversarial Semantic Data Augmentation for Human Pose Estimation
Human pose estimation is the task of localizing body keypoints from still images.
Dense Scene Multiple Object Tracking with Box-Plane Matching
Multiple Object Tracking (MOT) is an important task in computer vision.
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking
Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution.
Temporal Distinct Representation Learning for Action Recognition
Motivated by the previous success of Two-Dimensional Convolutional Neural Network (2D CNN) on image recognition, researchers endeavor to leverage it to characterize videos.
ACFD: Asymmetric Cartoon Face Detector
Cartoon face detection is a more challenging task than human face detection due to many difficult scenarios is involved.
Real-World Super-Resolution via Kernel Estimation and Noise Injection
Recent state-of-the-art super-resolution methods have achieved impressive performance on ideal datasets regardless of blur and noise.
Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation
Unsupervised learning of optical flow, which leverages the supervision from view synthesis, has emerged as a promising alternative to supervised methods.
Ranked #2 on
Optical Flow Estimation
on KITTI 2012 unsupervised
ASFD: Automatic and Scalable Face Detector
In this paper, we propose a novel Automatic and Scalable Face Detector (ASFD), which is based on a combination of neural architecture search techniques as well as a new loss design.
Adversarial Domain Adaptation with Domain Mixup
In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains.
TEINet: Towards an Efficient Architecture for Video Recognition
To relieve this problem, we propose an efficient temporal module, termed as Temporal Enhancement-and-Interaction (TEI Module), which could be plugged into the existing 2D CNNs (denoted by TEINet).
Fast Learning of Temporal Action Proposal via Dense Boundary Generator
In this paper, we propose an efficient and unified framework to generate temporal action proposals named Dense Boundary Generator (DBG), which draws inspiration from boundary-sensitive methods and implements boundary classification and action completeness regression for densely distributed proposals.
Ranked #7 on
Temporal Action Localization
on FineAction
Anti-Confusing: Region-Aware Network for Human Pose Estimation
In this work, we propose a novel framework named Region-Aware Network (RANet), which learns the ability of anti-confusing in case of heavy occlusion, nearby person and symmetric appearance, for human pose estimation.
Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection
In this paper, we propose a light reflection based face anti-spoofing method named Aurora Guard (AG), which is fast, simple yet effective that has already been deployed in real-world systems serving for millions of users.
Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos
In recent years, heatmap regression based models have shown their effectiveness in face alignment and pose estimation.
DSFD: Dual Shot Face Detector
In this paper, we propose a novel face detection network with three novel contributions that address three key aspects of face detection, including better feature learning, progressive loss design and anchor assign based data augmentation, respectively.
Ranked #1 on
Face Detection
on FDDB
