Momentum Contrast for Unsupervised Visual Representation Learning
We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic diction...
Publications
View AllNon-local Neural Networks
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family...
Enriching Word Vectors with Subword Information
Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore ...
Learning Spatiotemporal Features with 3D Convolutional Networks
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised...
Focal Loss for Dense Object Detection
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations...
A ConvNet for the 2020s
The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification...
SlowFast Networks for Video Recognition
We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway, operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, op...
A Closer Look at Spatiotemporal Convolutions for Action Recognition
In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition. Our motivation stems from the observation...
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture
In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling. We use a mu...
Supervised Learning of Universal Sentence Representations from Natural\n Language Inference Data
Many modern NLP systems rely on word embeddings, previously trained in an\nunsupervised manner on large corpora, as base features. Efforts to obtain\nembeddings for larger chunk...
Affiliated Researchers
Institution Info
- Type
- company
- Country
- IL
- Publications
- 12
- Citations
- 68,304
External Links
Identifiers
- ROR
- https://ror.org/02388em19