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Found 21 papers, 10 papers with code
Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation
To tackle the threat of fake news, the task of detecting and grounding multi-modal media manipulation DGM4 has received increasing attention.
Comparative Analysis of Machine Learning Models for Lung Cancer Mutation Detection and Staging Using 3D CT Scans
Lung cancer is the leading cause of cancer mortality worldwide, and non-invasive methods for detecting key mutations and staging are essential for improving patient outcomes.
Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility
The substantial training cost of diffusion models hinders their deployment.
Benchmarking Chest X-ray Diagnosis Models Across Multinational Datasets
MAVL, a model incorporating knowledge-enhanced prompts and structured supervision, achieved the highest performance on public (mean AUROC: 0. 82; AUPRC: 0. 32) and private (mean AUROC: 0. 95; AUPRC: 0. 89) datasets, ranking first in 14 of 37 public and 3 of 4 private tasks.
CoreNet: Conflict Resolution Network for Point-Pixel Misalignment and Sub-Task Suppression of 3D LiDAR-Camera Object Detection
It uses the dual-branch structure which adopts class-specific query and Bbox-specific query to corresponding sub-tasks.
RCTrans: Radar-Camera Transformer via Radar Densifier and Sequential Decoder for 3D Object Detection
In radar-camera 3D object detection, the radar point clouds are sparse and noisy, which causes difficulties in fusing camera and radar modalities.
UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing
Human pose plays a crucial role in the digital age.
Mitigating Object Hallucination via Concentric Causal Attention
Due to the long-term decay in RoPE, LVLMs tend to hallucinate more when relevant visual cues are distant from instruction tokens in the multimodal input sequence.
Learning Content-Aware Multi-Modal Joint Input Pruning via Bird's-Eye-View Representation
In the landscape of autonomous driving, Bird's-Eye-View (BEV) representation has recently garnered substantial academic attention, serving as a transformative framework for the fusion of multi-modal sensor inputs.
QuadBEV: An Efficient Quadruple-Task Perception Framework via Bird's-Eye-View Representation
Bird's-Eye-View (BEV) perception has become a vital component of autonomous driving systems due to its ability to integrate multiple sensor inputs into a unified representation, enhancing performance in various downstream tasks.
Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment
Drawing inspiration from the immiscibility phenomenon in physics, we propose Immiscible Diffusion, a simple and effective method to improve the random mixture of noise-data mapping.
SparseFusion: Efficient Sparse Multi-Modal Fusion Framework for Long-Range 3D Perception
The versatility of SparseFusion is also validated in the temporal object detection task and 3D lane detection task.
Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach
3D object detection in Bird's-Eye-View (BEV) space has recently emerged as a prevalent approach in the field of autonomous driving.
Pre-training on Synthetic Driving Data for Trajectory Prediction
Accumulating substantial volumes of real-world driving data proves pivotal in the realm of trajectory forecasting for autonomous driving.
HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration
Feature Descriptors and Detectors are two main components of feature-based point cloud registration.
HybridPoint: Point Cloud Registration Based on Hybrid Point Sampling and Matching
Firstly, we propose to use salient points with prominent local features as nodes to increase patch repeatability, and introduce some uniformly distributed points to complete the point cloud, thus constituting hybrid points.
Deep 360$^\circ$ Optical Flow Estimation Based on Multi-Projection Fusion
Optical flow computation is essential in the early stages of the video processing pipeline.
Kinematics clustering enables head impact subtyping for better traumatic brain injury prediction
However, due to different kinematic characteristics, many brain injury risk estimation models are not generalizable across the variety of impacts that humans may sustain.
Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics
A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e. g., football, car crash, mixed martial arts).
Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation
To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order (angular velocity, angular acceleration, angular jerk), 2) the direction and 3) the power (e. g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events.
Relationship between brain injury criteria and brain strain across different types of head impacts can be different
The results show a significant difference in the relationship between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain in different head impact types.
