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Found 49 papers, 10 papers with code
Pneumonia App: a mobile application for efficient pediatric pneumonia diagnosis using explainable convolutional neural networks (CNN)
Mycoplasma pneumoniae pneumonia (MPP) poses significant diagnostic challenges in pediatric healthcare, especially in regions like China where it's prevalent.
Superior and Pragmatic Talking Face Generation with Teacher-Student Framework
Talking face generation technology creates talking videos from arbitrary appearance and motion signal, with the "arbitrary" offering ease of use but also introducing challenges in practical applications.
Instance by Instance: An Iterative Framework for Multi-instance 3D Registration
Multi-instance registration is a challenging problem in computer vision and robotics, where multiple instances of an object need to be registered in a standard coordinate system.
Relative Pose for Nonrigid Multi-Perspective Cameras: The Static Case
Multi-perspective cameras with potentially non-overlapping fields of view have become an important exteroceptive sensing modality in a number of applications such as intelligent vehicles, drones, and mixed reality headsets.
Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis
One-shot 3D talking portrait generation aims to reconstruct a 3D avatar from an unseen image, and then animate it with a reference video or audio to generate a talking portrait video.
Learning transformer-based heterogeneously salient graph representation for multimodal fusion classification of hyperspectral image and LiDAR data
Data collected by different modalities can provide a wealth of complementary information, such as hyperspectral image (HSI) to offer rich spectral-spatial properties, synthetic aperture radar (SAR) to provide structural information about the Earth's surface, and light detection and ranging (LiDAR) to cover altitude information about ground elevation.
MV-ROPE: Multi-view Constraints for Robust Category-level Object Pose and Size Estimation
The object pose predictor then generates canonical object representations from RGB images.
Revisiting Event-based Video Frame Interpolation
We therefore propose to incorporate RGB information in an event-guided optical flow refinement strategy.
3D Registration with Maximal Cliques
The key insight is to loosen the previous maximum clique constraint, and mine more local consensus information in a graph for accurate pose hypotheses generation: 1) A compatibility graph is constructed to render the affinity relationship between initial correspondences.
Hierarchical Prior Mining for Non-local Multi-View Stereo
3) A Hierarchical Prior Mining (HPM) framework, which is used to mine extensive non-local prior information at different scales to assist 3D model recovery, this strategy can achieve a considerable balance between the reconstruction of details and low-textured areas.
MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency
Specifically, we introduce two cycle-consistency strategies for supervision: 1) Self tracking cycles, which leverage labels to help the model converge better in the early stages of training; 2) forward-backward cycles, which strengthen the tracker's robustness to motion variations and the template noise caused by the template update strategy.
PSNet: a deep learning model based digital phase shifting algorithm from a single fringe image
As the gold standard for phase retrieval, phase-shifting algorithm (PS) has been widely used in optical interferometry, fringe projection profilometry, etc.
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased
A crucial limitation of this framework is that every policy in the pool is optimized w. r. t.
iQuery: Instruments as Queries for Audio-Visual Sound Separation
To generalize to a new instrument or event class, drawing inspiration from the text-prompt design, we insert an additional query as an audio prompt while freezing the attention mechanism.
Multi-view Inverse Rendering for Large-scale Real-world Indoor Scenes
We present a efficient multi-view inverse rendering method for large-scale real-world indoor scenes that reconstructs global illumination and physically-reasonable SVBRDFs.
VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM
Event cameras have recently gained in popularity as they hold strong potential to complement regular cameras in situations of high dynamics or challenging illumination.
Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning
It has been a recent trend to leverage the power of supervised learning (SL) towards more effective reinforcement learning (RL) methods.
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning
Despite all the advantages, we revisit these two principles and show that in certain scenarios, e. g., environments with a highly multi-modal reward landscape, value decomposition, and parameter sharing can be problematic and lead to undesired outcomes.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Nearly Minimax Algorithms for Linear Bandits with Shared Representation
We give novel algorithms for multi-task and lifelong linear bandits with shared representation.
Unsupervised Learning of 3D Semantic Keypoints with Mutual Reconstruction
To the best of our knowledge, the proposed method is the first to mine 3D semantic consistent keypoints from a mutual reconstruction view.
A Topology-Attention ConvLSTM Network and Its Application to EM Images
We propose a novel TopologyAttention ConvLSTM Network (TACNet) for 3D image segmentation in order to achieve high structural accuracy for 3D segmentation tasks.
DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions
We present a novel real-time visual odometry framework for a stereo setup of a depth and high-resolution event camera.
PhyIR: Physics-Based Inverse Rendering for Panoramic Indoor Images
In this paper, we present PhyIR, a neural inverse rendering method with a more completed SVBRDF representation and a physics-based in-network rendering layer, which can handle complex material and incorporate physical constraints by re-rendering realistic and detailed specular reflectance.
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).
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
While the theory of RL has traditionally focused on linear function approximation (or eluder dimension) approaches, little is known about nonlinear RL with neural net approximations of the Q functions.
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
This work considers a large family of bandit problems where the unknown underlying reward function is non-concave, including the low-rank generalized linear bandit problems and two-layer neural network with polynomial activation bandit problem.
Visual Odometry with an Event Camera Using Continuous Ray Warping and Volumetric Contrast Maximization
We present a new solution to tracking and mapping with an event camera.
Dynamic Image Restoration and Fusion Based on Dynamic Degradation
In addition, a dynamic degradation kernel is proposed to improve the robustness of image restoration and fusion.
Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery
Considering that public transportation was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature
This paper studies model-based bandit and reinforcement learning (RL) with nonlinear function approximations.
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
With the new confidence sets, we obtain the follow regret bounds: For linear bandits, we obtain an $\tilde{O}(poly(d)\sqrt{1 + \sum_{k=1}^{K}\sigma_k^2})$ data-dependent regret bound, where $d$ is the feature dimension, $K$ is the number of rounds, and $\sigma_k^2$ is the \emph{unknown} variance of the reward at the $k$-th round.
Fully Gap-Dependent Bounds for Multinomial Logit Bandit
We study the multinomial logit (MNL) bandit problem, where at each time step, the seller offers an assortment of size at most $K$ from a pool of $N$ items, and the buyer purchases an item from the assortment according to a MNL choice model.
On Efficient and Robust Metrics for RANSAC Hypotheses and 3D Rigid Registration
This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration.
Impact of Representation Learning in Linear Bandits
For the finite-action setting, we present a new algorithm which achieves $\widetilde{O}(T\sqrt{kN} + \sqrt{dkNT})$ regret, where $N$ is the number of rounds we play for each bandit.
AE-Netv2: Optimization of Image Fusion Efficiency and Network Architecture
Finally, we explore the commonness and characteristics of different image fusion tasks, which provides a research basis for further research on the continuous learning characteristics of human brain in the field of image fusion.
3D Correspondence Grouping with Compatibility Features
We present a simple yet effective method for 3D correspondence grouping.
AE-Net: Autonomous Evolution Image Fusion Method Inspired by Human Cognitive Mechanism
Firstly, the relationship between human brain cognitive mechanism and image fusion task is analyzed and a physical model is established to simulate human brain cognitive mechanism.
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design
Motivated by practical needs such as large-scale learning, we study the impact of adaptivity constraints to linear contextual bandits, a central problem in online active learning.
LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching
The local reference frame (LRF) acts as a critical role in 3D local shape description and matching.
Non-linear and Selective Fusion of Cross-Modal Images
The human visual perception system has strong robustness in image fusion.
A Cross-Modal Image Fusion Method Guided by Human Visual Characteristics
The characteristics of feature selection, nonlinear combination and multi-task auxiliary learning mechanism of the human visual perception system play an important role in real-world scenarios, but the research of image fusion theory based on the characteristics of human visual perception is less.
Rotation Invariant Point Cloud Classification: Where Local Geometry Meets Global Topology
To the best of our knowledge, this work is the first principled approach toward adaptively combining global and local information under the context of RI point cloud analysis.
Iterative Clustering with Game-Theoretic Matching for Robust Multi-consistency Correspondence
Matching corresponding features between two images is a fundamental task to computer vision with numerous applications in object recognition, robotics, and 3D reconstruction.
A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching
Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision.
Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching
The outcomes present interesting findings that may shed new light on this community and provide complementary perspectives to existing evaluations on the topic of local geometric feature description.
Comparative evaluation of 2D feature correspondence selection algorithms
Correspondence selection aiming at seeking correct feature correspondences from raw feature matches is pivotal for a number of feature-matching-based tasks.
Learning to Fuse Local Geometric Features for 3D Rigid Data Matching
This paper presents a simple yet very effective data-driven approach to fuse both low-level and high-level local geometric features for 3D rigid data matching.
NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences
Feature correspondence selection is pivotal to many feature-matching based tasks in computer vision.
Performance Evaluation of 3D Correspondence Grouping Algorithms
This paper presents a thorough evaluation of several widely-used 3D correspondence grouping algorithms, motived by their significance in vision tasks relying on correct feature correspondences.
