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Found 40 papers, 9 papers with code
Learning Explicit Contact for Implicit Reconstruction of Hand-held Objects from Monocular Images
In the second part, we introduce a novel method to diffuse estimated contact states from the hand mesh surface to nearby 3D space and leverage diffused contact probabilities to construct the implicit neural representation for the manipulated object.
Poses as Queries: Image-to-LiDAR Map Localization with Transformers
High-precision vehicle localization with commercial setups is a crucial technique for high-level autonomous driving tasks.
CASIA-Iris-Africa: A Large-scale African Iris Image Database
The database contains 28, 717 images of 1023 African subjects (2046 iris classes) with age, gender, and ethnicity attributes that can be useful in demographically sensitive studies of Africans.
RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions
With model explanations of the proxy model, we curated a rubric to adjust text prompts to optimize image generation for precise emotion expression.
FusionMotion: Multi-Sensor Asynchronous Fusion for Continuous Occupancy Prediction via Neural-ODE
This paper investigates multi-sensor spatio-temporal fusion strategies for continuous occupancy prediction in a systematic manner.
Perturbation Inactivation Based Adversarial Defense for Face Recognition
A straightforward approach is to inactivate the adversarial perturbations so that they can be easily handled as general perturbations.
FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR
In the past decade, with the development of big data technology, an increasing amount of patient information has been stored as electronic health records (EHRs).
SRCN3D: Sparse R-CNN 3D Surround-View Camera Object Detection and Tracking for Autonomous Driving
Compared to prior arts, our novel sparse feature sampling module only utilizes local 2D region of interest (RoI) features calculated by projection of 3D proposal boxes for further box refinement, leading to an effective, fast and lightweight pipeline.
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring
Attributes skew hinders the current federated learning (FL) frameworks from consistent optimization directions among the clients, which inevitably leads to performance reduction and unstable convergence.
Multi-Domain Joint Training for Person Re-Identification
Our discovery is that training with such an adaptive model can better benefit from more training samples.
BE-STI: Spatial-Temporal Integrated Network for Class-Agnostic Motion Prediction With Bidirectional Enhancement
In this paper, we propose a novel Spatial-Temporal Integrated network with Bidirectional Enhancement, BE-STI, to improve the temporal motion prediction performance by spatial semantic features, which points out an efficient way to combine semantic segmentation and motion prediction.
PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving
The accelerating development of autonomous driving technology has placed greater demands on obtaining large amounts of high-quality data.
Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning
To make further efforts on accurate and reliable iris segmentation, we propose a bilateral self-attention module and design Bilateral Transformer (BiTrans) with hierarchical architecture by exploring spatial and visual relationships.
Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation
We propose Interpretable Directed Diversity to automatically predict ideation quality and diversity scores, and provide AI explanations - Attribution, Contrastive Attribution, and Counterfactual Suggestions - to feedback on why ideations were scored (low), and how to get higher scores.
SalienTrack: providing salient information for semi-automated self-tracking feedback with model explanations
Finally, we created interfaces to present salient information and conducted a formative user study to gain insights about how SalienTrack could be integrated into an interface for reflection.
Learning Instance-level Spatial-Temporal Patterns for Person Re-identification
In this paper, we propose a novel Instance-level and Spatial-Temporal Disentangled Re-ID method (InSTD), to improve Re-ID accuracy.
Ranked #14 on
Person Re-Identification
on DukeMTMC-reID
An End-to-End Autofocus Camera for Iris on the Move
To accommodate users at different distances, it is necessary to control focus quickly and accurately.
CASIA-Face-Africa: A Large-scale African Face Image Database
The proposed database along with its face landmark annotations, evaluation protocols and preliminary results form a good benchmark to study the essential aspects of face biometrics for African subjects, especially face image preprocessing, face feature analysis and matching, facial expression recognition, sex/age estimation, ethnic classification, face image generation, etc.
Center Prediction Loss for Re-identification
In this paper, we propose a new loss based on center predictivity, that is, a sample must be positioned in a location of the feature space such that from it we can roughly predict the location of the center of same-class samples.
Instance and Pair-Aware Dynamic Networks for Re-Identification
The proposed model is composed of three main branches where a self-guided dynamic branch is constructed to strengthen instance-specific features, focusing on every single image.
Retrieving High-Dimensional Quantum Steering From a Noisy Environment with N Measurement Settings
One of the most often implied benefits of high-dimensional (HD) quantum systems is to lead to stronger forms of correlations, featuring increased robustness to noise.
Quantum Physics
eTREE: Learning Tree-structured Embeddings
In many applications, the categories of items exhibit a hierarchical tree structure.
All-in-Focus Iris Camera With a Great Capture Volume
In this study, we develop a novel all-in-focus iris imaging system using a focus-tunable lens and a 2D steering mirror to greatly extend capture volume by spatiotemporal multiplexing method.
Recognition Oriented Iris Image Quality Assessment in the Feature Space
The quality metric proposed in this paper can significantly improve the performance of the recognition algorithm while reducing the number of images discarded for recognition, which is advantageous over hand-crafted factors based iris quality assessment methods.
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.
Learn Electronic Health Records by Fully Decentralized Federated Learning
Federated learning opens a number of research opportunities due to its high communication efficiency in distributed training problems within a star network.
Dynamic Graph Representation for Partially Occluded Biometrics
During dynamic graph matching, we propose a novel strategy to measure the distances of both nodes and adjacent matrixes.
Alignment Free and Distortion Robust Iris Recognition
And illumination variations may cause irregular distortion of iris texture.
NaMemo: Enhancing Lecturers' Interpersonal Competence of Remembering Students' Names
Addressing students by their names helps a teacher to start building rapport with students and thus facilitates their classroom participation.
No-regret Non-convex Online Meta-Learning
The online meta-learning framework is designed for the continual lifelong learning setting.
Representation Learning of EHR Data via Graph-Based Medical Entity Embedding
Automatic representation learning of key entities in electronic health record (EHR) data is a critical step for healthcare informatics that turns heterogeneous medical records into structured and actionable information.
Enhancing Model Interpretability and Accuracy for Disease Progression Prediction via Phenotype-Based Patient Similarity Learning
Models have been proposed to extract temporal patterns from longitudinal electronic health records (EHR) for clinical predictive models.
Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks
Rare diseases affecting 350 million individuals are commonly associated with delay in diagnosis or misdiagnosis.
Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model
Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare.
CoachAI: A Conversational Agent Assisted Health Coaching Platform
This research provides three main contributions to the preventive healthcare and healthy lifestyle promotion: (1) it presents the conversational agent to aid the caregiver; (2) it aims to decrease caregiver's workload and enhance care given to users, by handling (automating) repetitive caregiver tasks; and (3) it presents a domain independent mobile health conversational agent for health intervention delivery.
Modeling Treatment Delays for Patients using Feature Label Pairs in a Time Series
Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning.
Semi-supervised Rare Disease Detection Using Generative Adversarial Network
Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures.
End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN
Limited angular resolution has become the main bottleneck of microlens-based plenoptic cameras towards practical vision applications.
Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility
Considering temporal constraints in human mobility, we introduce an effective clustering approach - namely POI clustering with temporal constraints (PC-TC) - to extract POIs from spatio-temporal data of human mobility.
Rare Disease Physician Targeting: A Factor Graph Approach
For a specified rare disease, only a small number of patients are affected and a fractional of physicians are involved.
