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Found 33 papers, 18 papers with code
Iterative Cluster Harvesting for Wafer Map Defect Patterns
Unsupervised clustering of wafer map defect patterns is challenging because the appearance of certain defect patterns varies significantly.
Disentangling representations of retinal images with generative models
Retinal fundus images play a crucial role in the early detection of eye diseases and, using deep learning approaches, recent studies have even demonstrated their potential for detecting cardiovascular risk factors and neurological disorders.
SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild
We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background.
Zero-shot Translation of Attention Patterns in VQA Models to Natural Language
Converting a model's internals to text can yield human-understandable insights about the model.
ViPE: Visualise Pretty-much Everything
Figurative and non-literal expressions are profoundly integrated in human communication.
Dual-Query Multiple Instance Learning for Dynamic Meta-Embedding based Tumor Classification
With our work, we explore the potential of dynamic meta-embedding based on cutting-edge self-supervised pre-trained models in the context of MIL.
S.T.A.R.-Track: Latent Motion Models for End-to-End 3D Object Tracking with Adaptive Spatio-Temporal Appearance Representations
Following the tracking-by-attention paradigm, this paper introduces an object-centric, transformer-based framework for tracking in 3D.
Visual Grounding of Inter-lingual Word-Embeddings
We obtained visually grounded vector representations for these languages and studied whether visual grounding on one or multiple languages improved the performance of embeddings on word similarity and categorization benchmarks.
Diverse Video Captioning by Adaptive Spatio-temporal Attention
To generate proper captions for videos, the inference needs to identify relevant concepts and pay attention to the spatial relationships between them as well as to the temporal development in the clip.
Ranked #7 on
Video Captioning
on VATEX
How direct is the link between words and images?
To what extent does this setup rely on visual information from images?
Language with Vision: a Study on Grounded Word and Sentence Embeddings
Our model effectively balances the interplay between language and vision by aligning textual embeddings with visual information while simultaneously preserving the distributional statistics that characterize word usage in text corpora.
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
Our method works on in-the-wild online image collections of an object and produces relightable 3D assets for several use-cases such as AR/VR.
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.
Ranked #5 on
Image Relighting
on Stanford-ORB
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.
