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Found 14 papers, 7 papers with code
Learning to Taste: A Multimodal Wine Dataset
We demonstrate that this shared concept embedding space improves upon separate embedding spaces for coarse flavor classification (alcohol percentage, country, grape, price, rating) and aligns with the intricate human perception of flavor.
DAC: Detector-Agnostic Spatial Covariances for Deep Local Features
Current deep visual local feature detectors do not model the spatial uncertainty of detected features, producing suboptimal results in downstream applications.
Nerfbusters: Removing Ghostly Artifacts from Casually Captured NeRFs
Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the camera trajectory.
Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty
To ensure robustness to such incorrect segmentations, we propose Laplacian Segmentation Networks (LSN) that jointly model epistemic (model) and aleatoric (data) uncertainty in image segmentation.
K-Planes: Explicit Radiance Fields in Space, Time, and Appearance
We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions.
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Searching for Structure in Unfalsifiable Claims
In this work, we aim to distill such posts into a small set of narratives that capture the essential claims related to a given topic.
Laplacian Autoencoders for Learning Stochastic Representations
In this work, we present a Bayesian autoencoder for unsupervised representation learning, which is trained using a novel variational lower-bound of the autoencoder evidence.
SparseFormer: Attention-based Depth Completion Network
This remains a challenging problem due to the low density, non-uniform and outlier-prone 3D landmarks produced by SfM and SLAM pipelines.
Volumetric Disentanglement for 3D Scene Manipulation
To this end, we propose a volumetric framework for (i) disentangling or separating, the volumetric representation of a given foreground object from the background, and (ii) semantically manipulating the foreground object, as well as the background.
Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization
Place recognition and visual localization are particularly challenging in wide baseline configurations.
Self-Supervised Depth Completion for Active Stereo
Active stereo systems are used in many robotic applications that require 3D information.
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval
Uncertainty quantification in image retrieval is crucial for downstream decisions, yet it remains a challenging and largely unexplored problem.
Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition
Lifelong place recognition is an essential and challenging task in computer vision with vast applications in robust localization and efficient large-scale 3D reconstruction.
Probabilistic Spatial Transformer Networks
Spatial Transformer Networks (STNs) estimate image transformations that can improve downstream tasks by `zooming in' on relevant regions in an image.
