Search Results for author:
Found 22 papers, 14 papers with code
Self-Supervised Learning for Endoscopic Video Analysis
To fully exploit the power of SSL, we create sizable unlabeled endoscopic video datasets for training MSNs.
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution before processing them with computer vision models has not yet been successfully challenged.
Retrieval-Enhanced Contrastive Vision-Text Models
Remarkably, we show that this can be done with a light-weight, single-layer, fusion transformer on top of a frozen CLIP.
Ranked #3 on
Fine-Grained Image Recognition
on OVEN
Verbs in Action: Improving verb understanding in video-language models
Understanding verbs is crucial to modelling how people and objects interact with each other and the environment through space and time.
Ranked #8 on
Video Question Answering
on NExT-QA
(using extra training data)
Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on
Linear-Probe Classification
on ImageNet
(using extra training data)
FlexiViT: One Model for All Patch Sizes
Vision Transformers convert images to sequences by slicing them into patches.
Location-Aware Self-Supervised Transformers for Semantic Segmentation
Pixel-level labels are particularly expensive to acquire.
A Memory Transformer Network for Incremental Learning
We study class-incremental learning, a training setup in which new classes of data are observed over time for the model to learn from.
Masked Siamese Networks for Label-Efficient Learning
We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations.
Self-Supervised Image Classification
Self-Supervised Learning
+1
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
(using extra training data)
Unsupervised Dense Information Retrieval with Contrastive Learning
In this work, we explore the limits of contrastive learning as a way to train unsupervised dense retrievers and show that it leads to strong performance in various retrieval settings.
Contrastive Pre-training for Zero-Shot Information Retrieval
By contrast, in many other NLP tasks, conventional self-supervised pre-training based on masking leads to strong generalization with small number of training examples.
XCiT: Cross-Covariance Image Transformers
We propose a "transposed" version of self-attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariance matrix between keys and queries.
Ranked #54 on
Instance Segmentation
on COCO minival
ResMLP: Feedforward networks for image classification with data-efficient training
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.
Ranked #5 on
Image Classification
on ImageNet ReaL
(Top 1 Accuracy metric)
Emerging Properties in Self-Supervised Vision Transformers
In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).
Ranked #2 on
Visual Place Recognition
on Laurel Caverns
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS).
Self-supervised Pretraining of Visual Features in the Wild
Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods.
Ranked #6 on
Image Classification
on Places205
Self-Supervised Image Classification
Self-Supervised Learning
+1
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much.
Ranked #1 on
Contrastive Learning
on imagenet-1k
Pruning Convolutional Neural Networks with Self-Supervision
In this work, we investigate the use of standard pruning methods, developed primarily for supervised learning, for networks trained without labels (i. e. on self-supervised tasks).
Finding Winning Tickets with Limited (or No) Supervision
The lottery ticket hypothesis argues that neural networks contain sparse subnetworks, which, if appropriately initialized (the winning tickets), are capable of matching the accuracy of the full network when trained in isolation.
Unsupervised Pre-Training of Image Features on Non-Curated Data
Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available.
Ranked #63 on
Self-Supervised Image Classification
on ImageNet (finetuned)
(using extra training data)
Deep Clustering for Unsupervised Learning of Visual Features
In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features.
Ranked #1 on
Image Clustering
on CIFAR-100
(Train Set metric, using extra
training data)
