Search Results for author:
Found 54 papers, 38 papers with code
Cluster and Predict Latents Patches for Improved Masked Image Modeling
Masked Image Modeling (MIM) offers a promising approach to self-supervised representation learning, however existing MIM models still lag behind the state-of-the-art.
DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment
Self-supervised visual foundation models produce powerful embeddings that achieve remarkable performance on a wide range of downstream tasks.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+2
You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning
Self-Supervised learning (SSL) with Joint-Embedding Architectures (JEA) has led to outstanding performances.
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
This manual process has some limitations similar to those encountered in supervised learning, e. g., the crowd-sourced selection of data is costly and time-consuming, preventing scaling the dataset size.
Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning
AI Foundation models are gaining traction in various applications, including medical fields like radiology.
Vision Transformers Need Registers
Transformers have recently emerged as a powerful tool for learning visual representations.
Ranked #1 on
Self-Supervised Image Classification
on ImageNet
(using extra training data)
Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions
The success of transformer models trained with a language modeling objective brings a promising opportunity to the reinforcement learning framework.
Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar
The maps are generated by the extraction of features from a self-supervised model trained on Maxar imagery from 2017 to 2020, and the training of a dense prediction decoder against aerial lidar maps.
DINOv2: Learning Robust Visual Features without Supervision
The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.
Ranked #1 on
Image Retrieval
on AmsterTime
(using extra training data)
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations.
Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping
Developing agents that can execute multiple skills by learning from pre-collected datasets is an important problem in robotics, where online interaction with the environment is extremely time-consuming.
Co-Training 2L Submodels for Visual Recognition
Given a neural network to be trained, for each sample we implicitly instantiate two altered networks, "submodels", with stochastic depth: i. e. activating only a subset of the layers and skipping others.
Co-training $2^L$ Submodels for Visual Recognition
We introduce submodel co-training, a regularization method related to co-training, self-distillation and stochastic depth.
Ranked #63 on
Image Classification
on ImageNet
The Hidden Uniform Cluster Prior in Self-Supervised Learning
A successful paradigm in representation learning is to perform self-supervised pretraining using tasks based on mini-batch statistics (e. g., SimCLR, VICReg, SwAV, MSN).
Walk the Random Walk: Learning to Discover and Reach Goals Without Supervision
Finally, we train a goal-conditioned policy network with goals sampled from the goal memory and reward it by the reachability network and the goal memory.
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
Copy Detection
on Copydays strong subset
(using extra training data)
Augmenting Convolutional networks with attention-based aggregation
We show how to augment any convolutional network with an attention-based global map to achieve non-local reasoning.
Ranked #41 on
Semantic Segmentation
on ADE20K val
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.
Ranked #5 on
Sentence Retrieval
on PeerQA
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 #58 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 #1 on
Image Classification
on Certificate Verification
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
Copy Detection
on Copydays strong subset
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
Memory-Augmented Reinforcement Learning for Image-Goal Navigation
In this work, we present a memory-augmented approach for image-goal navigation.
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 #9 on
Image Classification
on OmniBenchmark
Learning to Visually Navigate in Photorealistic Environments Without any Supervision
Learning to navigate in a realistic setting where an agent must rely solely on visual inputs is a challenging task, in part because the lack of position information makes it difficult to provide supervision during training.
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).
Updating Pre-trained Word Vectors and Text Classifiers using Monolingual Alignment
In this paper, we focus on the problem of adapting word vector-based models to new textual data.
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.
Training Hybrid Language Models by Marginalizing over Segmentations
In this paper, we study the problem of hybrid language modeling, that is using models which can predict both characters and larger units such as character ngrams or words.
Misspelling Oblivious Word Embeddings
In this paper we present a method to learn word embeddings that are resilient to misspellings.
Adaptive Attention Span in Transformers
We propose a novel self-attention mechanism that can learn its optimal attention span.
Ranked #4 on
Language Modelling
on Text8
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 #65 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 #5 on
Unsupervised Semantic Segmentation
on ImageNet-S-50
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion
Continuous word representations learned separately on distinct languages can be aligned so that their words become comparable in a common space.
Colorless green recurrent networks dream hierarchically
Recurrent neural networks (RNNs) have achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language.
Learning Word Vectors for 157 Languages
Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance.
Ranked #12 on
Only Connect Walls Dataset Task 1 (Grouping)
on OCW
(using extra training data)
Advances in Pre-Training Distributed Word Representations
Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl.
Fast Linear Model for Knowledge Graph Embeddings
This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings.
Learning from Video and Text via Large-Scale Discriminative Clustering
Discriminative clustering has been successfully applied to a number of weakly-supervised learning tasks.
Ranked #35 on
Video Retrieval
on LSMDC
Optimizing the Latent Space of Generative Networks
Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images.
Parseval Networks: Improving Robustness to Adversarial Examples
We introduce Parseval networks, a form of deep neural networks in which the Lipschitz constant of linear, convolutional and aggregation layers is constrained to be smaller than 1.
Unsupervised Learning by Predicting Noise
We propose to fix a set of target representations, called Noise As Targets (NAT), and to constrain the deep features to align to them.
FastText.zip: Compressing text classification models
We consider the problem of producing compact architectures for text classification, such that the full model fits in a limited amount of memory.
Enriching Word Vectors with Subword Information
A vector representation is associated to each character $n$-gram; words being represented as the sum of these representations.
Ranked #3 on
Word Similarity
on WS353
Bag of Tricks for Efficient Text Classification
This paper explores a simple and efficient baseline for text classification.
Ranked #1 on
Sentiment Analysis
on Sogou News
Emotion Recognition in Conversation
General Classification
+2
Instance-Level Video Segmentation From Object Tracks
We address the problem of segmenting multiple object instances in complex videos.
Alternative structures for character-level RNNs
The first one consists on conditioning the character level representation on the previous word representation.
Unsupervised Learning from Narrated Instruction Videos
Third, we experimentally demonstrate that the proposed method can automatically discover, in an unsupervised manner, the main steps to achieve the task and locate the steps in the input videos.
Ranked #7 on
Temporal Action Localization
on CrossTask
Semidefinite and Spectral Relaxations for Multi-Label Classification
In this paper, we address the problem of multi-label classification.
Weakly-Supervised Alignment of Video With Text
Given vectorial features for both video and text, we propose to cast this task as a temporal assignment problem, with an implicit linear mapping between the two feature modalities.
Weakly Supervised Action Labeling in Videos Under Ordering Constraints
We are given a set of video clips, each one annotated with an {\em ordered} list of actions, such as "walk" then "sit" then "answer phone" extracted from, for example, the associated text script.
