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Found 21 papers, 10 papers with code
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
We present baseline results for generating natural language explanations in the context of VQA using two state-of-the-art frameworks on the CLEVR-X dataset.
Ranked #1 on
Explanation Generation
on CLEVR-X
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition
Decomposing a scene into its shape, reflectance and illumination is a fundamental problem in computer vision and graphics.
Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task Training
The general approach is to embed both textual and visual information into a common space -the grounded space-confined by an explicit relationship between both modalities.
NeRD: Neural Reflectance Decomposition from Image Collections
This problem is inherently more challenging when the illumination is not a single light source under laboratory conditions but is instead an unconstrained environmental illumination.
Latent State Inference in a Spatiotemporal Generative Model
Knowledge about the hidden factors that determine particular system dynamics is crucial for both explaining them and pursuing goal-directed interventions.
Inferring, Predicting, and Denoising Causal Wave Dynamics
The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network.
Two-shot Spatially-varying BRDF and Shape Estimation
Extensive experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images.
A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction
We introduce a distributed spatio-temporal artificial neural network architecture (DISTANA).
GGNN: Graph-based GPU Nearest Neighbor Search
Approximate nearest neighbor (ANN) search in high dimensions is an integral part of several computer vision systems and gains importance in deep learning with explicit memory representations.
Single Image BRDF Parameter Estimation with a Conditional Adversarial Network
Creating plausible surfaces is an essential component in achieving a high degree of realism in rendering.
Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)
Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid.
Learning Blind Motion Deblurring
As handheld video cameras are now commonplace and available in every smartphone, images and videos can be recorded almost everywhere at anytime.
Efficient Large-scale Approximate Nearest Neighbor Search on the GPU
We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization.
Backpropagation Training for Fisher Vectors within Neural Networks
Fisher-Vectors (FV) encode higher-order statistics of a set of multiple local descriptors like SIFT features.
Will People Like Your Image? Learning the Aesthetic Space
Rating how aesthetically pleasing an image appears is a highly complex matter and depends on a large number of different visual factors.
Learning Robust Video Synchronization without Annotations
Aligning video sequences is a fundamental yet still unsolved component for a broad range of applications in computer graphics and vision.
Transfer Learning for Material Classification using Convolutional Networks
Specifically, transfer learning from the task of object recognition is exploited to more effectively train good features for material classification.
End-to-End Learning for Image Burst Deblurring
We present a neural network model approach for multi-frame blind deconvolution.
Robust Deep-Learning-Based Road-Prediction for Augmented Reality Navigation Systems
The proposed approach reliably detects roads with and without lane markings and thus increases the robustness and availability of road course estimations and augmented reality navigation.
Infrared Colorization Using Deep Convolutional Neural Networks
This paper proposes a method for transferring the RGB color spectrum to near-infrared (NIR) images using deep multi-scale convolutional neural networks.
Scalable Structure From Motion for Densely Sampled Videos
Videos consisting of thousands of high resolution frames are challenging for existing structure from motion (SfM) and simultaneous-localization and mapping (SLAM) techniques.
