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Found 17 papers, 4 papers with code
Differential contributions of machine learning and statistical analysis to language and cognitive sciences
Data-driven approaches have revolutionized scientific research.
TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches
This study employs deep learning techniques to explore four speaker profiling tasks on the TIMIT dataset, namely gender classification, accent classification, age estimation, and speaker identification, highlighting the potential and challenges of multi-task learning versus single-task models.
Attention-aware semantic relevance predicting Chinese sentence reading
Our approach underscores the potential of these metrics to advance our comprehension of how humans understand and process language, ultimately leading to a better understanding of language comprehension and processing.
Computational Sentence-level Metrics Predicting Human Sentence Comprehension
Our results indicate that these computational sentence-level metrics are exceptionally effective at predicting and elucidating the processing difficulties encountered by readers in comprehending sentences as a whole across a variety of languages.
Comprehensive Reassessment of Large-Scale Evaluation Outcomes in LLMs: A Multifaceted Statistical Approach
Evaluations have revealed that factors such as scaling, training types, architectures and other factors profoundly impact the performance of LLMs.
Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation
S2ADet comprises a hyperspectral information decoupling (HID) module, a two-stream feature extraction network, and a one-stage detection head.
Towards Accurate and Reliable Change Detection of Remote Sensing Images via Knowledge Review and Online Uncertainty Estimation
Specifically, an online uncertainty estimation branch is constructed to model the pixel-wise uncertainty, which is supervised by the difference between predicted change maps and corresponding ground truth during the training process.
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.
SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration
In this paper, we present a second order spatial compatibility (SC^2) measure based method for efficient and robust point cloud registration (PCR), called SC^2-PCR.
SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception
In this paper, we propose a novel underwater image enhancement network, called SGUIE-Net, in which we introduce semantic information as high-level guidance across different images that share common semantic regions.
SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration
In this paper, we present a second order spatial compatibility (SC^2) measure based method for efficient and robust point cloud registration (PCR), called SC^2-PCR.
Ranked #1 on
Point Cloud Registration
on FP-O-H
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks
We also evaluate the effectiveness of our attack under two defenses: one is well-designed adversarial graph detector and the other is that the target GNN model itself is equipped with a defense to prevent adversarial graph generation.
Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis
Particularly, the localized adversarial examples only perturb a small and contiguous region of the target object, so that they are robust and effective in both digital and physical worlds.
GPU Accelerated Cascade Hashing Image Matching for Large Scale 3D Reconstruction
In this paper, we proposed a GPU accelerated image matching method with improved Cascade Hashing.
Trilaminar Multiway Reconstruction Tree for Efficient Large Scale Structure from Motion
Accuracy and efficiency are two key problems in large scale incremental Structure from Motion (SfM).
4D Cardiac Ultrasound Standard Plane Location by Spatial-Temporal Correlation
The proposed method is comprised of three stages, the frame smoothing, spatial-temporal embedding and final classification.
Asymmetrical Gauss Mixture Models for Point Sets Matching
The probabilistic methods based on Symmetrical Gauss Mixture Model (SGMM) have achieved great success in point sets registration, but are seldom used to find the correspondences between two images due to the complexity of the non-rigid transformation and too many outliers.
