Browse SoTA > Computer Vision > Contrastive Learning

Contrastive Learning

26 papers with code ยท Computer Vision

Leaderboards

No evaluation results yet. Help compare methods by submit evaluation metrics.

Latest papers without code

Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation

3 Jul 2020

Weakly supervised phrase grounding aims at learning region-phrase correspondences using only image-sentence pairs.

CONTRASTIVE LEARNING OBJECT DETECTION PHRASE GROUNDING

Data Augmenting Contrastive Learning of Speech Representations in the Time Domain

2 Jul 2020

Contrastive Predictive Coding (CPC), based on predicting future segments of speech based on past segments is emerging as a powerful algorithm for representation learning of speech signal.

CONTRASTIVE LEARNING DATA AUGMENTATION REPRESENTATION LEARNING

Words Aren't Enough, Their Order Matters: On the Robustness of Grounding Visual Referring Expressions

ACL 2020

Visual referring expression recognition is a challenging task that requires natural language understanding in the context of an image.

CONTRASTIVE LEARNING MULTI-TASK LEARNING NATURAL LANGUAGE UNDERSTANDING

SCE: Scalable Network Embedding from Sparsest Cut

30 Jun 2020

A key of success to such contrastive learning methods is how to draw positive and negative samples.

CONTRASTIVE LEARNING NETWORK EMBEDDING

PCLNet: A Practical Way for Unsupervised Deep PolSAR Representations and Few-Shot Classification

27 Jun 2020

To handle this problem, in this paper, learning transferrable representations from unlabeled PolSAR data through convolutional architectures is explored for the first time.

CONTRASTIVE LEARNING IMAGE CLASSIFICATION REPRESENTATION LEARNING

Domain Contrast for Domain Adaptive Object Detection

26 Jun 2020

We present Domain Contrast (DC), a simple yet effective approach inspired by contrastive learning for training domain adaptive detectors.

CONTRASTIVE LEARNING OBJECT DETECTION

Disentangle Perceptual Learning through Online Contrastive Learning

24 Jun 2020

Perceptual learning approaches like perceptual loss are empirically powerful for such tasks but they usually rely on the pre-trained classification network to provide features, which are not necessarily optimal in terms of visual perception of image transformation.

CONTRASTIVE LEARNING FEATURE SELECTION

Contrastive learning of global and local features for medical image segmentation with limited annotations

18 Jun 2020

In this work, we propose strategies for extending the contrastive learning framework for segmentation of volumetric medical images in the semi-supervised setting with limited annotations, by leveraging domain-specific and problem-specific cues.

CONTRASTIVE LEARNING DATA AUGMENTATION MEDICAL IMAGE SEGMENTATION SELF-SUPERVISED LEARNING SEMANTIC SEGMENTATION

Joint Contrastive Learning for Unsupervised Domain Adaptation

18 Jun 2020

Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination.

CONTRASTIVE LEARNING UNSUPERVISED DOMAIN ADAPTATION