Product Alert

How good are deep learning methods for automated road safety analysis using video data? An experimental study

Image-based multi-object detection (MOD) and multi-object tracking (MOT) are advancing at a fast pace. A variety of 2D and 3D MOD and MOT methods have been developed for monocular and stereo cameras. Road safety analysis can benefit from those advancements. As crashes are rare events, surrogate measures of safety (SMoS) have been developed for safety analyses. (Semi-)Automated safety analysis me...

Deformable Registration Framework for Augmented Reality-based Surgical Guidance in Head and Neck Tumor Resection

Head and neck squamous cell carcinoma (HNSCC) has one of the highest rates of recurrence cases among solid malignancies. Recurrence rates can be reduced by improving positive margins localization. Frozen section analysis (FSA) of resected specimens is the gold standard for intraoperative margin assessment. However, because of the complex 3D anatomy and the significant shrinkage of resected speci...

i-WiViG: Interpretable Window Vision GNN

Deep learning models based on graph neural networks have emerged as a popular approach for solving computer vision problems. They encode the image i...

Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping

Diffusion Transformer (DiT) has now become the preferred choice for building image generation models due to its great generation capability. Unlike ...

Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing

Calculating percentage bone loss is a critical test for periodontal disease staging but is sometimes imprecise and time consuming when manually calc...

MlyPredCSED: based on extreme point deviation compensated clustering combined with cross-scale convolutional neural networks to predict multiple lysine sites in human.

In post-translational modification, covalent bonds on lysine and attached chemical groups significantly change proteins' physical and chemical propert...

Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation

Depression is a widespread mental health disorder, and clinical interviews are the gold standard for assessment. However, their reliance on scarce p...

PEO: Improving Bi-Factorial Preference Alignment with Post-Training Policy Extrapolation

The alignment of large language models with human values presents a critical challenge, particularly when balancing conflicting objectives like help...

Detecting Heel Strike and toe off Events Using Kinematic Methods and LSTM Models

Accurate gait event detection is crucial for gait analysis, rehabilitation, and assistive technology, particularly in exoskeleton control, where pre...

SEE: See Everything Every Time -- Adaptive Brightness Adjustment for Broad Light Range Images via Events

Event cameras, with a high dynamic range exceeding $120dB$, significantly outperform traditional embedded cameras, robustly recording detailed chang...

Automatic Temporal Segmentation for Post-Stroke Rehabilitation: A Keypoint Detection and Temporal Segmentation Approach for Small Datasets

Rehabilitation is essential and critical for post-stroke patients, addressing both physical and cognitive aspects. Stroke predominantly affects olde...

BarkXAI: A Lightweight Post-Hoc Explainable Method for Tree Species Classification with Quantifiable Concepts

The precise identification of tree species is fundamental to forestry, conservation, and environmental monitoring. Though many studies have demonstr...

Clinical Inspired MRI Lesion Segmentation

Magnetic resonance imaging (MRI) is a potent diagnostic tool for detecting pathological tissues in various diseases. Different MRI sequences have di...

CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-Resolution

Low-bit model quantization for image super-resolution (SR) is a longstanding task that is renowned for its surprising compression and acceleration a...

PQBFL: A Post-Quantum Blockchain-based Protocol for Federated Learning

One of the goals of Federated Learning (FL) is to collaboratively train a global model using local models from remote participants. However, the FL ...

RAPTOR: Refined Approach for Product Table Object Recognition

Extracting tables from documents is a critical task across various industries, especially on business documents like invoices and reports. Existing ...

Freezing of Gait as a Complication of Pallidal Deep Brain Stimulation in DYT- KMT2B Patients with Evidence of Striatonigral Degeneration

Background: Mutations in KMT2B are a recognized cause of early-onset complex dystonia, with deep brain stimulation (DBS) of the internal globus pall...

PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models

Large Language Models (LLMs) suffer severe performance degradation when facing extremely low-bit (sub 2-bit) quantization. Several existing sub 2-bi...

Can LLMs Simulate Social Media Engagement? A Study on Action-Guided Response Generation

Social media enables dynamic user engagement with trending topics, and recent research has explored the potential of large language models (LLMs) fo...

Enhancing Out-of-Distribution Detection in Medical Imaging with Normalizing Flows

Out-of-distribution (OOD) detection is crucial in AI-driven medical imaging to ensure reliability and safety by identifying inputs outside a model's...