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Revisiting Contrastive Learning for Few-Shot Classification
Instance discrimination based contrastive learning has emerged as a lead...
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Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning
We present a plug-in replacement for batch normalization (BN) called exp...
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StarcNet: Machine Learning for Star Cluster Identification
We present a machine learning (ML) pipeline to identify star clusters in...
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Shot in the Dark: Few-Shot Learning with No Base-Class Labels
Few-shot learning aims to learn classifiers for new objects from a small...
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PhraseCut: Language-based Image Segmentation in the Wild
We consider the problem of segmenting image regions given a natural lang...
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Describing Textures using Natural Language
Textures in natural images can be characterized by color, shape, periodi...
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Unsupervised Discovery of Object Landmarks via Contrastive Learning
Given a collection of images, humans are able to discover landmarks of t...
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Exploring and Predicting Transferability across NLP Tasks
Recent advances in NLP demonstrate the effectiveness of training large-s...
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Detecting and Tracking Communal Bird Roosts in Weather Radar Data
The US weather radar archive holds detailed information about biological...
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Deep Manifold Prior
We present a prior for manifold structured data, such as surfaces of 3D ...
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Learning Generative Models of Shape Handles
We present a generative model to synthesize 3D shapes as sets of handles...
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Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
The problems of shape classification and part segmentation from 3D point...
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ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds
We propose a novel, end-to-end trainable, deep network called ParSeNet t...
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Neural Shape Parsers for Constructive Solid Geometry
Constructive Solid Geometry (CSG) is a geometric modeling technique that...
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The Spectral Bias of the Deep Image Prior
The "deep image prior" proposed by Ulyanov et al. is an intriguing prope...
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When Does Self-supervision Improve Few-shot Learning?
We present a technique to improve the generalization of deep representat...
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Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation
User generated 3D shapes in online repositories contain rich information...
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Visualizing and Describing Fine-grained Categories as Textures
We analyze how categories from recent FGVC challenges can be described b...
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Boosting Supervision with Self-Supervision for Few-shot Learning
We present a technique to improve the transferability of deep representa...
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Inferring 3D Shapes from Image Collections using Adversarial Networks
We investigate the problem of learning a probabilistic distribution over...
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Active Adversarial Domain Adaptation
We propose an active learning approach for transferring representations ...
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A Bayesian Perspective on the Deep Image Prior
The deep image prior was recently introduced as a prior for natural imag...
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Meta-Learning with Differentiable Convex Optimization
Many meta-learning approaches for few-shot learning rely on simple base ...
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Task2Vec: Task Embedding for Meta-Learning
We introduce a method to provide vectorial representations of visual cla...
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A Deeper Look at 3D Shape Classifiers
We investigate the role of representations and architectures for classif...
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Second-order Democratic Aggregation
Aggregated second-order features extracted from deep convolutional netwo...
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Multiresolution Tree Networks for 3D Point Cloud Processing
We present multiresolution tree-structured networks to process point clo...
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VisemeNet: Audio-Driven Animator-Centric Speech Animation
We present a novel deep-learning based approach to producing animator-ce...
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SPLATNet: Sparse Lattice Networks for Point Cloud Processing
We present a network architecture for processing point clouds that direc...
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CSGNet: Neural Shape Parser for Constructive Solid Geometry
We present a neural architecture that takes as input a 2D or 3D shape an...
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Improved Bilinear Pooling with CNNs
Bilinear pooling of Convolutional Neural Network (CNN) features [22, 23]...
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3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
We propose a method for reconstructing 3D shapes from 2D sketches in the...
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Shape Generation using Spatially Partitioned Point Clouds
We propose a method to generate 3D shapes using point clouds. Given a po...
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3D Shape Induction from 2D Views of Multiple Objects
In this paper we investigate the problem of inducing a distribution over...
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3D Shape Segmentation with Projective Convolutional Networks
This paper introduces a deep architecture for segmenting 3D objects into...
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Adapting Models to Signal Degradation using Distillation
Model compression and knowledge distillation have been successfully appl...
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High Dimensional Inference with Random Maximum A-Posteriori Perturbations
This paper presents a new approach, called perturb-max, for high-dimensi...
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Deep filter banks for texture recognition, description, and segmentation
Visual textures have played a key role in image understanding because th...
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One-to-many face recognition with bilinear CNNs
The recent explosive growth in convolutional neural network (CNN) resear...
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Multi-view Convolutional Neural Networks for 3D Shape Recognition
A longstanding question in computer vision concerns the representation o...
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Bilinear CNNs for Fine-grained Visual Recognition
We present a simple and effective architecture for fine-grained visual r...
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Jointly Learning Multiple Measures of Similarities from Triplet Comparisons
Similarity between objects is multi-faceted and it can be easier for hum...
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Deep convolutional filter banks for texture recognition and segmentation
Research in texture recognition often concentrates on the problem of mat...
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Describing Textures in the Wild
Patterns and textures are defining characteristics of many natural objec...
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Fine-Grained Visual Classification of Aircraft
This paper introduces FGVC-Aircraft, a new dataset containing 10,000 ima...
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Linearized Additive Classifiers
We revisit the additive model learning literature and adapt a penalized ...
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