Search Results for author:
Found 60 papers, 19 papers with code
Blur-Countering Keypoint Detection via Eigenvalue Asymmetry
To settle this issue, we propose a blur-countering method for detecting valid keypoints for various types and degrees of blurred images.
Unconstrained Facial Action Unit Detection via Latent Feature Domain
Due to the combination of source AU-related information and target AU-free information, the latent feature domain with transferred source label can be learned by maximizing the target-domain AU detection performance.
Blur Removal via Blurred-Noisy Image Pair
Complex blur such as the mixup of space-variant and space-invariant blur, which is hard to model mathematically, widely exists in real images.
A Probabilistic Bitwise Genetic Algorithm for B-Spline based Image Deformation Estimation
We propose a novel genetic algorithm to solve the image deformation estimation problem by preserving the genetic diversity.
HLO: Half-kernel Laplacian Operator for Surface Smoothing
This paper presents a simple yet effective method for feature-preserving surface smoothing.
Computational Geometry Graphics
Indoor image representation by high-level semantic features
Nevertheless, most of the existing feature extraction methods, which extract features based on pixels, color, shape/object parts or objects on images, suffer from limited capabilities in describing semantic information (e. g., object association).
Explicit Facial Expression Transfer via Fine-Grained Representations
Instead of using an intermediate estimated guidance, we propose to explicitly transfer facial expression by directly mapping two unpaired input images to two synthesized images with swapped expressions.
Tag-based Semantic Features for Scene Image Classification
In this paper, we introduce novel semantic features of an image based on the annotations and descriptions of its similar images available on the web.
Unsupervised Deep Features for Privacy Image Classification
Sharing images online poses security threats to a wide range of users due to the unawareness of privacy information.
G2MF-WA: Geometric Multi-Model Fitting with Weakly Annotated Data
The WA data can be naturally obtained in an interactive way for specific tasks, for example, in the case of homography estimation, one can easily annotate points on the same plane/object with a single label by observing the image.
Pointfilter: Point Cloud Filtering via Encoder-Decoder Modeling
In this paper, we propose a novel deep learning approach that automatically and robustly filters point clouds with removing noise and preserving sharp features and geometric details.
Graphics
HDF: Hybrid Deep Features for Scene Image Representation
In this paper, we propose a novel type of features -- hybrid deep features, for scene images.
SHX: Search History Driven Crossover for Real-Coded Genetic Algorithm
In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history.
DMT: Dynamic Mutual Training for Semi-Supervised Learning
Instead, leveraging inter-model disagreement between different models is a key to locate pseudo label errors.
Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation
Guided by this mask, we propose a ClassOut strategy to realize effective regional consistency in a fine-grained manner.
Deep Feature-preserving Normal Estimation for Point Cloud Filtering
In this paper, we propose a novel feature-preserving normal estimation method for point cloud filtering with preserving geometric features.
Spoof Face Detection Via Semi-Supervised Adversarial Training
To capture the underlying structure of live faces data in latent representation space, we propose to train the live face data only, with a convolutional Encoder-Decoder network acting as a Generator.
Content and Context Features for Scene Image Representation
Existing research in scene image classification has focused on either content features (e. g., visual information) or context features (e. g., annotations).
Scene Image Representation by Foreground, Background and Hybrid Features
In this paper, we propose to use hybrid features in addition to foreground and background features to represent scene images.
Deep Patch-based Human Segmentation
3D human segmentation has seen noticeable progress in re-cent years.
Example-based Color Transfer with Gaussian Mixture Modeling
In particular, we relate the transferred image with the example image under the Gaussian Mixture Model (GMM) and regard the transferred image color as the GMM centroids.
Deep Detection for Face Manipulation
It has become increasingly challenging to distinguish real faces from their visually realistic fake counterparts, due to the great advances of deep learning based face manipulation techniques in recent years.
Chaotic-to-Fine Clustering for Unlabeled Plant Disease Images
Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts, which is time-consuming and labour-intensive.
I-Nema: A Biological Image Dataset for Nematode Recognition
In this paper, we identify two main bottlenecks: (1) the lack of a publicly available imaging dataset for diverse species of nematodes (especially the species only found in natural environment) which requires considerable human resources in field work and experts in taxonomy, and (2) the lack of a standard benchmark of state-of-the-art deep learning techniques on this dataset which demands the discipline background in computer science.
DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning
Extensive experiments show that our unsupervised learning method enables comparable detection performance to state-of-the-art supervised techniques, in both the intra- and inter-dataset settings.
Self-Adversarial Disentangling for Specific Domain Adaptation
In this paper, we study a practical setting called Specific Domain Adaptation (SDA) that aligns the source and target domains in a demanded-specific dimension.
Image-to-Image Translation on Cityscapes-to-Foggy Cityscapes
object-detection
+3
Context-Aware Mixup for Domain Adaptive Semantic Segmentation
The generated contextual mask is critical in this work and will guide the context-aware domain mixup on three different levels.
Ranked #5 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation
Extensive experiments demonstrate that our method can soundly boost the performance on both cross-domain object detection and segmentation for state-of-the-art techniques.
3D Face Recognition: A Survey
Face recognition is one of the most studied research topics in the community.
Automated Security Assessment for the Internet of Things
This security model automatically assesses the security of the IoT network by capturing potential attack paths.
A Robust Scheme for 3D Point Cloud Copy Detection
In this paper, we focus on a fundamental and practical research problem: judging whether a point cloud is plagiarized or copied to another point cloud in the presence of several manipulations (e. g., similarity transformation, smoothing).
Anatomical Landmarks Localization for 3D Foot Point Clouds
3D anatomical landmarks play an important role in health research.
Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization
In this paper, we propose a novel and fully end-to-end trainable approach, called regional contrastive consistency regularization (RCCR) for domain adaptive semantic segmentation.
Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data
Though a number of point cloud learning methods have been proposed to handle unordered points, most of them are supervised and require labels for training.
Rethinking Point Cloud Filtering: A Non-Local Position Based Approach
We then map the non-local similar patches into a canonical space and aggregate the non-local information.
Deep Point Cloud Normal Estimation via Triplet Learning
It consists of two phases: (a) feature encoding which learns representations of local patches, and (b) normal estimation that takes the learned representation as input and regresses the normal vector.
Towards Uniform Point Distribution in Feature-preserving Point Cloud Filtering
As a popular representation of 3D data, point cloud may contain noise and need to be filtered before use.
3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-branch Learning
While most existing deep learning research focused on medical images in a supervised way, we introduce an unsupervised method for the detection of intracranial aneurysms based on 3D point cloud data.
AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review
In this systematic literature review, we present a comprehensive review of the state-of-the-art approaches reported in carcinoma diagnosis using histopathological images.
CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping
In this paper, we empirically prove that this problem is associated with the mixup of the activation values between less discriminative foreground regions and the background.
Masked Autoencoders in 3D Point Cloud Representation Learning
We first split the input point cloud into patches and mask a portion of them, then use our Patch Embedding Module to extract the features of unmasked patches.
Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering
Existing methods usually perform normal estimation and filtering separately and often show sensitivity to noise and/or inability to preserve sharp geometric features such as corners and edges.
SO(3)-Pose: SO(3)-Equivariance Learning for 6D Object Pose Estimation
The SO(3)-equivariant features communicate with RGB features to deduce the (missed) geometry for detecting keypoints of an object with the reflective surface from the depth channel.
SPCNet: Stepwise Point Cloud Completion Network
We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings.
Graph Classification via Discriminative Edge Feature Learning
Three new graph datasets are constructed based on ModelNet40, ModelNet10 and ShapeNet Part datasets.
Deepfake Detection via Joint Unsupervised Reconstruction and Supervised Classification
Most of the previous methods use the backbone network to extract global features for making predictions and only employ binary supervision (i. e., indicating whether the training instances are fake or authentic) to train the network.
Snow Removal in Video: A New Dataset and A Novel Method
We also develop a deep learning framework for video snow removal.
Skeleton-based Action Recognition through Contrasting Two-Stream Spatial-Temporal Networks
For this, we propose a novel Contrastive GCN-Transformer Network (ConGT) which fuses the spatial and temporal modules in a parallel way.
Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
ANL introduces the additional negative pseudo-label for all unlabeled data to leverage low-confidence examples.
IterativePFN: True Iterative Point Cloud Filtering
Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud filtering or denoising.
Instance-Aware Domain Generalization for Face Anti-Spoofing
To address these issues, we propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels.
Don't worry about mistakes! Glass Segmentation Network via Mistake Correction
The IS is designed to simulate the detection procedure of human recognition for identifying transparent glass by global context and edge information.
Weighted Point Cloud Normal Estimation
Existing normal estimation methods for point clouds are often less robust to severe noise and complex geometric structures.
Generalized Category Discovery in Semantic Segmentation
This paper explores a novel setting called Generalized Category Discovery in Semantic Segmentation (GCDSS), aiming to segment unlabeled images given prior knowledge from a labeled set of base classes.
BA-SAM: Scalable Bias-Mode Attention Mask for Segment Anything Model
To this end, we propose Scalable Bias-Mode Attention Mask (BA-SAM) to enhance SAM's adaptability to varying image resolutions while eliminating the need for structure modifications.
DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learning
Recent works attempt to extend Graph Convolution Networks (GCNs) to point clouds for classification and segmentation tasks.
Rethinking Impersonation and Dodging Attacks on Face Recognition Systems
Then, Biased Gradient Adaptation is presented to adapt the adversarial examples to traverse the decision boundaries of both the attacker and victim by adding perturbations favoring dodging attacks on the vacated regions, preserving the prioritized features of the original perturbations while boosting dodging performance.
SimAda: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes
In this paper, we aim to investigate the impact of the general vision modules on finetuning SAM and enable them to generalize across all downstream tasks.
Test-Time Domain Generalization for Face Anti-Spoofing
Our method, consisting of Test-Time Style Projection (TTSP) and Diverse Style Shifts Simulation (DSSS), effectively projects the unseen data to the seen domain space.
DGMamba: Domain Generalization via Generalized State Space Model
SPR strives to encourage the model to concentrate more on objects rather than context, consisting of two designs: Prior-Free Scanning~(PFS), and Domain Context Interchange~(DCI).
