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Found 19 papers, 7 papers with code
A New Image Quality Database for Multiple Industrial Processes
Recent years have witnessed a broader range of applications of image processing technologies in multiple industrial processes, such as smoke detection, security monitoring, and workpiece inspection.
SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis
A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses.
BeatDance: A Beat-Based Model-Agnostic Contrastive Learning Framework for Music-Dance Retrieval
Dance and music are closely related forms of expression, with mutual retrieval between dance videos and music being a fundamental task in various fields like education, art, and sports.
STDG: Semi-Teacher-Student Training Paradigram for Depth-guided One-stage Scene Graph Generation
Scene Graph Generation is a critical enabler of environmental comprehension for autonomous robotic systems.
SelfTalk: A Self-Supervised Commutative Training Diagram to Comprehend 3D Talking Faces
To enhance the visual accuracy of generated lip movement while reducing the dependence on labeled data, we propose a novel framework SelfTalk, by involving self-supervision in a cross-modals network system to learn 3D talking faces.
Generalizable synthetic MRI with physics-informed convolutional networks
In this study, we develop a physics-informed deep learning-based method to synthesize multiple brain magnetic resonance imaging (MRI) contrasts from a single five-minute acquisition and investigate its ability to generalize to arbitrary contrasts to accelerate neuroimaging protocols.
EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation
Specifically, we introduce the emotion disentangling encoder (EDE) to disentangle the emotion and content in the speech by cross-reconstructed speech signals with different emotion labels.
Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation
In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning.
SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation
In stage 1, a novel devices detection and tracking scheme is introduced, which accurately locate the height limit devices in the left or right image.
FuRPE: Learning Full-body Reconstruction from Part Experts
These novel elements of FuRPE not only serve to further refine the model but also to reduce potential biases that may arise from inaccuracies in pseudo labels, thereby optimizing the network's training process and enhancing the robustness of the model.
A Cascaded Multi-Task Generative Framework for Detecting Aortic Dissection on 3D Non-contrast-enhanced Computed Tomography
Experiments also showed that the proposed framework could achieve an average accuracy of 0. 831, a sensitivity of 0. 938, and an F1-score of 0. 847 in comparison with seven state-of-the-art classification models used by three radiologists with junior, intermediate, and senior experiences, respectively.
GIDP: Learning a Good Initialization and Inducing Descriptor Post-enhancing for Large-scale Place Recognition
Large-scale place recognition is a fundamental but challenging task, which plays an increasingly important role in autonomous driving and robotics.
Human Pose Driven Object Effects Recommendation
In this paper, we research the new topic of object effects recommendation in micro-video platforms, which is a challenging but important task for many practical applications such as advertisement insertion.
Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion
Real-world sensors often produce incomplete, irregular, and noisy point clouds, making point cloud completion increasingly important.
Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image
Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications.
ACR-Pose: Adversarial Canonical Representation Reconstruction Network for Category Level 6D Object Pose Estimation
ACR-Pose consists of a Reconstructor and a Discriminator.
RPR-Net: A Point Cloud-based Rotation-aware Large Scale Place Recognition Network
Third, we apply these kernels to previous point cloud features to generate new features, which is the well-known SO(3) mapping process.
Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview
Therefore, this study presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route.
SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition
Point cloud-based large scale place recognition is fundamental for many applications like Simultaneous Localization and Mapping (SLAM).
Ranked #2 on
3D Place Recognition
on Oxford RobotCar Dataset
