Search Results for author:
Found 46 papers, 16 papers with code
Beyond 3DMM Space: Towards Fine-grained 3D Face Reconstruction
However, most of their training data is constructed by 3D Morphable Model, whose space spanned is only a small part of the shape space.
Neural Program Synthesis with Query
In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space.
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement
To address the two issues above, this paper proposes a novel framework, namely Attentive Cross-modal Interaction Network with Motion Enhancement (MEACI-Net).
Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection
Specifically, TRKP adopts the teacher-student framework, where the multi-head teacher network is built to extract knowledge from labeled source domains and guide the student network to learn detectors in unlabeled target domain.
Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint
Active learning is a promising alternative to alleviate the issue of high annotation cost in the computer vision tasks by consciously selecting more informative samples to label.
Beyond 3DMM: Learning to Capture High-fidelity 3D Face Shape
3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori.
CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection
In autonomous driving, LiDAR point-clouds and RGB images are two major data modalities with complementary cues for 3D object detection.
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
Precise representations of 3D faces are beneficial to various computer vision and graphics applications.
ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo
The network is mainly composed of two components: a context-aware local retouching layer (LRL) and an adaptive blend pyramid layer (BPL).
ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization
Weakly-supervised temporal action localization (WTAL) in untrimmed videos has emerged as a practical but challenging task since only video-level labels are available.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
UFPMP-Det: Toward Accurate and Efficient Object Detection on Drone Imagery
This paper proposes a novel approach to object detection on drone imagery, namely Multi-Proxy Detection Network with Unified Foreground Packing (UFPMP-Det).
Segmentation-Reconstruction-Guided Facial Image De-occlusion
The proposed model consists of a 3D face reconstruction module, a face segmentation module, and an image generation module.
Boundary Guided Context Aggregation for Semantic Segmentation
The recent studies on semantic segmentation are starting to notice the significance of the boundary information, where most approaches see boundaries as the supplement of semantic details.
Identity-aware Graph Memory Network for Action Detection
Action detection plays an important role in high-level video understanding and media interpretation.
iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization
Differentiable ARchiTecture Search (DARTS) uses a continuous relaxation of network representation and dramatically accelerates Neural Architecture Search (NAS) by almost thousands of times in GPU-day.
PR-GCN: A Deep Graph Convolutional Network with Point Refinement for 6D Pose Estimation
This paper proposes a novel deep learning approach, namely Graph Convolutional Network with Point Refinement (PR-GCN), to simultaneously address the issues above in a unified way.
Image Inpainting via Conditional Texture and Structure Dual Generation
Deep generative approaches have recently made considerable progress in image inpainting by introducing structure priors.
Recurrent Graph Neural Networks for Rumor Detection in Online Forums
The widespread adoption of online social networks in daily life has created a pressing need for effectively classifying user-generated content.
Double-Dot Network for Antipodal Grasp Detection
This paper proposes a new deep learning approach to antipodal grasp detection, named Double-Dot Network (DD-Net).
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction
Missing textures in the incomplete UV map are further full-filled by the UV generator.
Pixel Sampling for Style Preserving Face Pose Editing
The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image style properly, which refers to the color, brightness, saturation, etc.
Magnifying Subtle Facial Motions for Effective 4D Expression Recognition
In this paper, an effective pipeline to automatic 4D Facial Expression Recognition (4D FER) is proposed.
Student-Teacher Feature Pyramid Matching for Anomaly Detection
Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies.
Ranked #20 on
Anomaly Detection
on MVTec AD
(using extra training data)
MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images
Objects in aerial images usually have arbitrary orientations and are densely located over the ground, making them extremely challenge to be detected.
PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection
LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds of distant and occluded objects.
Ranked #4 on
3D Object Detection
on KITTI Cars Easy val
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited.
Ranked #10 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Improving Object Detection with Selective Self-supervised Self-training
They fail to improve object detectors in their vanilla forms due to the domain gap between the Web images and curated datasets.
Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation
Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred.
DWM: A Decomposable Winograd Method for Convolution Acceleration
In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions.
A Feasible Framework for Arbitrary-Shaped Scene Text Recognition
Deep learning based methods have achieved surprising progress in Scene Text Recognition (STR), one of classic problems in computer vision.
Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism
Recently, Human Attribute Recognition (HAR) has become a hot topic due to its scientific challenges and application potentials, where localizing attributes is a crucial stage but not well handled.
Learning Spatial Fusion for Single-Shot Object Detection
Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection.
Ranked #119 on
Object Detection
on COCO test-dev
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Due to the lack of suitable datasets, previous research has only examined deep learning on controlled synthetic label noise, and real-world label noise has never been studied in a controlled setting.
Ranked #9 on
Image Classification
on WebVision-1000
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers.
Synthetic vs Real: Deep Learning on Controlled Noise
Performing controlled experiments on noisy data is essential in thoroughly understanding deep learning across a spectrum of noise levels.
ArduCode: Predictive Framework for Automation Engineering
We show that machine learning can be leveraged to assist the automation engineer in classifying automation, finding similar code snippets, and reasoning about the hardware selection of sensors and actuators.
Graph Representation Ensemble Learning
In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently.
Benchmarks for Graph Embedding Evaluation
We use the comparisons on our 100 benchmark graphs to define GFS-score, that can be applied to any embedding method to quantify its performance.
CompactNet: Platform-Aware Automatic Optimization for Convolutional Neural Networks
Convolutional Neural Network (CNN) based Deep Learning (DL) has achieved great progress in many real-life applications.
Adaptive NMS: Refining Pedestrian Detection in a Crowd
Pedestrian detection in a crowd is a very challenging issue.
Ranked #9 on
Object Detection
on CrowdHuman (full body)
Learning Continuous Face Age Progression: A Pyramid of GANs
The two underlying requirements of face age progression, i. e. aging accuracy and identity permanence, are not well studied in the literature.
Learning Face Age Progression: A Pyramid Architecture of GANs
The two underlying requirements of face age progression, i. e. aging accuracy and identity permanence, are not well studied in the literature.
Feature Map Pooling for Cross-View Gait Recognition Based on Silhouette Sequence Images
In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images.
Receptive Field Block Net for Accurate and Fast Object Detection
Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs.
Defect detection for patterned fabric images based on GHOG and low-rank decomposition
In order to accurately detect defects in patterned fabric images, a novel detection algorithm based on Gabor-HOG (GHOG) and low-rank decomposition is proposed in this paper.
Face Aging Effect Simulation using Hidden Factor Analysis Joint Sparse Representation
Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images.
