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Found 39 papers, 16 papers with code
Adaptive Mesh Representation and Restoration of Biomedical Images
The triangulation of images has become an active research area in recent years for its compressive representation and ease of image processing and visualization.
Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories
However, none of existing approaches addresses the problem of identifying network-wide important crossroads in real road network.
Variance reduction properties of the reparameterization trick
From this, we show that the marginal variances of the reparameterization gradient estimator are smaller than those of the score function gradient estimator.
Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers.
EPINE: Enhanced Proximity Information Network Embedding
Unsupervised homogeneous network embedding (NE) represents every vertex of networks into a low-dimensional vector and meanwhile preserves the network information.
Triaging moderate COVID-19 and other viral pneumonias from routine blood tests
The COVID-19 is sweeping the world with deadly consequences.
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition
Visual Place Recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world.
Ranked #1 on
Visual Localization
on RobotCar Seasons v2
Probabilistic Visual Place Recognition for Hierarchical Localization
In this letter, we propose two methods which adapt image retrieval techniques used for visual place recognition to the Bayesian state estimation formulation for localization.
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction.
Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness
Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exteroceptive sensor data.
Interactive Model with Structural Loss for Language-based Abductive Reasoning
Therefore, we propose to group instead of ranking the hypotheses and design a structural loss called ``joint softmax focal loss'' in this paper.
Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise
We show that our method successfully alleviates the damage of both label noise and confirmation bias.
Ranked #2 on
Image Classification
on mini WebVision 1.0
Sharing Behavior in Ride-hailing Trips: A Machine Learning Inference Approach
Using ensemble machine learning models, we find that the travel impedance variables (trip cost, distance, and duration) collectively contribute to 95% and 91% of the predictive power in determining whether a trip is requested to share and whether it is successfully shared, respectively.
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction
Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data.
Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference
Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions.
Improving Road Segmentation in Challenging Domains Using Similar Place Priors
These approaches share one or more of three significant limitations: a reliance on large amounts of annotated training data that can be costly to obtain, both anticipation of and training data from the type of environmental conditions expected at inference time, and/or imagery captured from a previous visit to the location.
Improving Worst Case Visual Localization Coverage via Place-specific Sub-selection in Multi-camera Systems
6-DoF visual localization systems utilize principled approaches rooted in 3D geometry to perform accurate camera pose estimation of images to a map.
3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization
Although deep-learning based methods for monocular pedestrian detection have made great progress, they are still vulnerable to heavy occlusions.
Ranked #2 on
Multiview Detection
on Wildtrack
(using extra training data)
Neuro-Symbolic Learning: Principles and Applications in Ophthalmology
Neural networks have been rapidly expanding in recent years, with novel strategies and applications.
Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics
Furthermore, the downstream RL agent is limited to learning structurally similar tasks to those used to construct the skill space.
Futuristic Variations and Analysis in Fundus Images Corresponding to Biological Traits
Our proposed DL models, named FAG-Net and FGC-Net, correspondingly estimate biological traits (age and gender) and generates fundus images.
Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation
Extensive simulation results conducted on a public dataset demonstrate that our proposed single-user and multi-user cognitive semantic communication systems are superior to benchmark communication systems in terms of the data compression rate and communication reliability.
Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths
An interesting byproduct of this formulation is that DecDTW outputs the optimal warping path between two time series as opposed to a soft approximation, recoverable from Soft-DTW.
TraffNet: Learning Causality of Traffic Generation for What-if Prediction
Real-time what-if traffic prediction is crucial for decision making in intelligent traffic management and control.
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph Fusion
In this framework, we first develop an embedding module that contains a sampling algorithm (MGWalk) and an encoder network to learn latent representation for each road segment.
Adaptive Frequency Green Light Optimal Speed Advisory based on Hybrid Actor-Critic Reinforcement Learning
Green Light Optimal Speed Advisory (GLOSA) system suggests speeds to vehicles to assist them in passing through intersections during green intervals, thus reducing traffic congestion and fuel consumption by minimizing the number of stops and idle times at intersections.
Towards Understanding Gradient Approximation in Equality Constrained Deep Declarative Networks
We explore conditions for when the gradient of a deep declarative node can be approximated by ignoring constraint terms and still result in a descent direction for the global loss function.
Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and Reconstruction
We test our MC-CDic model on multi-contrast MRI SR and reconstruction tasks.
GAME: Generalized deep learning model towards multimodal data integration for early screening of adolescent mental disorders
The timely identification of mental disorders in adolescents is a global public health challenge. Single factor is difficult to detect the abnormality due to its complex and subtle nature.
Global Feature Pyramid Network
The visual feature pyramid has proven its effectiveness and efficiency in target detection tasks.
Empowering Working Memory for Large Language Model Agents
The limitations of traditional LLM memory designs are analyzed, including their isolation of distinct dialog episodes and lack of persistent memory links.
A clean-label graph backdoor attack method in node classification task
To explore the backdoor vulnerability of GNNs and create a more stealthy backdoor attack method, a clean-label graph backdoor attack method(CGBA) in the node classification task is proposed in this paper.
Advanced Unstructured Data Processing for ESG Reports: A Methodology for Structured Transformation and Enhanced Analysis
In the evolving field of corporate sustainability, analyzing unstructured Environmental, Social, and Governance (ESG) reports is a complex challenge due to their varied formats and intricate content.
Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection
Feature shaping refers to a family of methods that exhibit state-of-the-art performance for out-of-distribution (OOD) detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Dual-Space Optimization: Improved Molecule Sequence Design by Latent Prompt Transformer
Designing molecules with desirable properties, such as drug-likeliness and high binding affinities towards protein targets, is a challenging problem.
Study of the mechanism of electroacupuncture regulating ferroptosis, inhibiting bladder neck fibrosis, and improving bladder urination function after suprasacral spinal cord injury using proteomics
Purpose The aim of this study was to explore whether electroacupuncture regulates phenotypic transformation of smooth muscle cells by inhibiting ferroptosis and inhibiting fibrosis, thereby improving bladder urination function after suprasacral spinal cord injury (SSCI).
The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?
This study uses linear probing to shed light on the hidden knowledge at the output layer of LVLMs.
Hierarchical Spatial Proximity Reasoning for Vision-and-Language Navigation
Most Vision-and-Language Navigation (VLN) algorithms tend to make decision errors, primarily due to a lack of visual common sense and insufficient reasoning capabilities.
Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentation
We evaluate our segmentation approach and unsupervised learning pipeline on the Breakfast, 50-Salads, YouTube Instructions and Desktop Assembly datasets, yielding state-of-the-art results for the unsupervised video action segmentation task.
