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Found 27 papers, 9 papers with code
Enhancing Features in Long-tailed Data Using Large Vision Mode
Language-based foundation models, such as large language models (LLMs) or large vision-language models (LVLMs), have been widely studied in long-tailed recognition.
Prompt-Guided Dual-Path UNet with Mamba for Medical Image Segmentation
Convolutional neural networks (CNNs) and transformers are widely employed in constructing UNet architectures for medical image segmentation tasks.
Neural Radiance Fields for the Real World: A Survey
Neural Radiance Fields (NeRFs) have remodeled 3D scene representation since release.
Maintain Plasticity in Long-timescale Continual Test-time Adaptation
Based on this correlation, we propose a simple yet effective policy, Adaptive Shrink-Restore (ASR), towards preserving the model's plasticity.
FlameGS: Reconstruct flame light field via Gaussian Splatting
To address the time-consuming and computationally intensive issues of traditional ART algorithms for flame combustion diagnosis, inspired by flame simulation technology, we propose a novel representation method for flames.
DGNS: Deformable Gaussian Splatting and Dynamic Neural Surface for Monocular Dynamic 3D Reconstruction
During training, depth maps generated by the deformable Gaussian splatting module guide the ray sampling for faster processing and provide depth supervision within the dynamic neural surface module to improve geometry reconstruction.
Viewpoint Consistency in 3D Generation via Attention and CLIP Guidance
Despite recent advances in text-to-3D generation techniques, current methods often suffer from geometric inconsistencies, commonly referred to as the Janus Problem.
NeFF-BioNet: Crop Biomass Prediction from Point Cloud to Drone Imagery
To address this limitation, we present a biomass prediction network (BioNet), designed for adaptation across different data modalities, including point clouds and drone imagery.
Registration between Point Cloud Streams and Sequential Bounding Boxes via Gradient Descent
In this paper, we propose an algorithm for registering sequential bounding boxes with point cloud streams.
Collaborative Static-Dynamic Teaching: A Semi-Supervised Framework for Stripe-Like Space Target Detection
To address this, we introduce an innovative Collaborative Static-Dynamic Teacher (CSDT) SSL framework, which includes static and dynamic teacher models as well as a student model.
RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation
Therefore, investigating how to better comprehend the negative labeling of unobserved interactions in academic reviewer recommendations is a significant challenge.
SSTD: Stripe-Like Space Target Detection Using Single-Point Weak Supervision
This domain faces three challenges: the lack of publicly available datasets, interference from stray light and stars, and the variability of stripe-like targets, which makes manual labeling both inaccurate and labor-intensive.
Class-Aware Cartilage Segmentation for Autonomous US-CT Registration in Robotic Intercostal Ultrasound Imaging
Then, a dense skeleton graph-based non-rigid registration is presented to map the intercostal scanning path from a generic template to individual patients.
MMCBE: Multi-modality Dataset for Crop Biomass Prediction and Beyond
Addressing this gap, we introduce a new dataset in this domain, i. e. Multi-modality dataset for crop biomass estimation (MMCBE).
Latent-based Diffusion Model for Long-tailed Recognition
We propose a new approach, the Latent-based Diffusion Model for Long-tailed Recognition (LDMLR), as a feature augmentation method to tackle the issue.
Backpropagation-free Network for 3D Test-time Adaptation
Here, we propose a novel method that uses a backpropagation-free approach for TTA for the specific case of 3D data.
Spatial Transcriptomics Analysis of Zero-shot Gene Expression Prediction
Spatial transcriptomics (ST) captures gene expression within distinct regions (i. e., windows) of a tissue slide.
Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation
Current state-of-the-art point cloud-based perception methods usually rely on large-scale labeled data, which requires expensive manual annotations.
Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph
To address this challenge, a graph-based non-rigid registration is proposed to enable transferring planned paths from the atlas to the current setup by explicitly considering subcutaneous bone surface features instead of the skin surface.
Skeleton Graph-based Ultrasound-CT Non-rigid Registration
Autonomous ultrasound (US) scanning has attracted increased attention, and it has been seen as a potential solution to overcome the limitations of conventional US examinations, such as inter-operator variations.
Heterogeneous Graph-Based Multimodal Brain Network Learning
Other experiments also indicate that our proposed model with a pretraining strategy alleviates the problem of limited labelled data and yields a significant improvement in accuracy.
Weight Encode Reconstruction Network for Computed Tomography in a Semi-Case-Wise and Learning-Based Way
In the generalization test of the model, the encoder is transferable from a voxel set with complex structure to the unseen cases without the deduction of the accuracy.
Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion
Then, by means of the multibranch neural network, three kinds of features from shallow to deep are extracted and fused in the spectral dimension.
Efficient and accurate object detection with simultaneous classification and tracking
To satisfy both requirements, we propose a detection framework based on simultaneous classification and tracking in the point stream.
Real-time 3D object proposal generation and classification under limited processing resources
The task of detecting 3D objects is important to various robotic applications.
Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds
Varying density of point clouds increases the difficulty of 3D detection.
Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes
The task of detecting 3D objects in traffic scenes has a pivotal role in many real-world applications.
