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Found 34 papers, 18 papers with code
Evaluating Text-to-Image Synthesis: Survey and Taxonomy of Image Quality Metrics
Recent advances in text-to-image synthesis enabled through a combination of language and vision foundation models have led to a proliferation of the tools available and an increased attention to the field.
Attention-Guided Masked Autoencoders For Learning Image Representations
Masked autoencoders (MAEs) have established themselves as a powerful method for unsupervised pre-training for computer vision tasks.
Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships
We co-embed the features from a 3D scene graph prediction backbone with the feature space of powerful open world 2D vision language foundation models.
Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph prediction
While it is widely accepted that pre-training is an effective approach to improve model performance in low data regimes, in this paper, we find that existing pre-training methods are ill-suited for 3D scene graphs.
SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction
In the field of 3D scene understanding, 3D scene graphs have emerged as a new scene representation that combines geometric and semantic information about objects and their relationships.
Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling
We achieve this by (1) learning depth-feature correlation by spatially correlate the feature maps with the depth maps to induce knowledge about the structure of the scene and (2) implementing farthest-point sampling to more effectively select relevant features by utilizing 3D sampling techniques on depth information of the scene.
Leveraging Self-Supervised Vision Transformers for Neural Transfer Function Design
In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity.
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging
Based on our results, we propose the SparK pre-training for medical imaging tasks with only small annotated datasets.
ClusterNet: A Perception-Based Clustering Model for Scattered Data
Finally, we compare our approach against existing state-of-the-art clustering techniques and can show, that ClusterNet is able to generalize to unseen and out of scope data.
LLMMaps -- A Visual Metaphor for Stratified Evaluation of Large Language Models
To support such stratified evaluations, we propose LLMMaps as a novel visualization technique that enables users to evaluate LLMs' performance with respect to Q&A datasets.
Weakly-Supervised Optical Flow Estimation for Time-of-Flight
Indirect Time-of-Flight (iToF) cameras are a widespread type of 3D sensor, which perform multiple captures to obtain depth values of the captured scene.
Monocular Depth Decomposition of Semi-Transparent Volume Renderings
Additionally, we investigate how these networks can be extended to further obtain color and opacity information, in order to create a layered representation of the scene based on a single color image.
Neural Activation Patterns (NAPs): Visual Explainability of Learned Concepts
Promising methods for discovering learned features are based on analyzing activation values, whereby current techniques focus on analyzing high activation values to reveal interesting features on a neuron level.
Contrastive Representation Learning for 3D Protein Structures
Learning from 3D protein structures has gained wide interest in protein modeling and structural bioinformatics.
Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization
We propose a new microscopy simulation system that can depict atomistic models in a micrograph visual style, similar to results of physical electron microscopy imaging.
Clean Implicit 3D Structure from Noisy 2D STEM Images
Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of a 3D sample on the scale of individual cell components.
Gaussian Mixture Convolution Networks
This paper proposes a novel method for deep learning based on the analytical convolution of multidimensional Gaussian mixtures.
Visual Identification of Problematic Bias in Large Label Spaces
With our approach, different models and datasets for large label spaces can be systematically and visually analyzed and compared to make informed fairness assessments tackling problematic bias.
Variance-Aware Weight Initialization for Point Convolutional Neural Networks
Appropriate weight initialization has been of key importance to successfully train neural networks.
RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising
Time-of-Flight (ToF) cameras are subject to high levels of noise and distortions due to Multi-Path-Interference (MPI).
Data-driven deep density estimation
Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples.
A3GC-IP: Attention-Oriented Adjacency Adaptive Recurrent Graph Convolutions for Human Pose Estimation from Sparse Inertial Measurements
Conventional methods for human pose estimation either require a high degree of instrumentation, by relying on many inertial measurement units (IMUs), or constraint the recording space, by relying on extrinsic cameras.
Finding Nano-Ötzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography
Our technique shows the high impact in target sciences for visual data analysis of very noisy volumes that cannot be visualized with existing techniques.
exploRNN: Understanding Recurrent Neural Networks through Visual Exploration
In an empirical study, we assessed 37 subjects in a between-subjects design to investigate the learning outcomes and cognitive load of exploRNN compared to a classic text-based learning environment.
Deep Volumetric Ambient Occlusion
We present a novel deep learning based technique for volumetric ambient occlusion in the context of direct volume rendering.
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
Proteins perform a large variety of functions in living organisms, thus playing a key role in biology.
Enabling Viewpoint Learning through Dynamic Label Generation
Optimal viewpoint prediction is an essential task in many computer graphics applications.
Classifying the classifier: dissecting the weight space of neural networks
of neural network classifiers, and train a large number of models to represent the weight space.
Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning
We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only.
Training Object Detectors on Synthetic Images Containing Reflecting Materials
While synthesized data sets can be used to overcome this challenge, it is important that these data sets close the reality gap, i. e., a model trained on synthetic image data is able to generalize to real images.
Net2Vis -- A Visual Grammar for Automatically Generating Publication-Tailored CNN Architecture Visualizations
To convey neural network architectures in publications, appropriate visualizations are of great importance.
Deep-learning the Latent Space of Light Transport
Thus, we suggest a two-stage operator comprising of a 3D network that first transforms the point cloud into a latent representation, which is later on projected to the 2D output image using a dedicated 3D-2D network in a second step.
Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks
We thus propose a novel approach that uses the power of pretrained classifiers to visually guide users to noisy labels, and let them interactively check error candidates, to iteratively improve the training data set.
Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds
We propose an efficient and effective method to learn convolutions for non-uniformly sampled point clouds, as they are obtained with modern acquisition techniques.
