2.1k citations · 5.6k across the 30 of their papers we have counts for
6 papers · 1 filter
Exploring Simple Siamese Representation Learning
Xinlei Chen, Kaiming He
Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentat…
Understanding Self-supervised Learning with Dual Deep Networks
Yuandong Tian, Lantao Yu, Xinlei Chen +1
We propose a novel theoretical framework to understand contrastive self-supervised learning (SSL) methods that employ dual pairs of deep ReLU networks (e.g., SimCLR). First, we pro…
Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation
Medhini Narasimhan, Erik Wijmans, Xinlei Chen +4
We introduce a learning-based approach for room navigation using semantic maps. Our proposed architecture learns to predict top-down belief maps of regions that lie beyond the agen…
MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond
Duy-Kien Nguyen, Vedanuj Goswami, Xinlei Chen
This paper focuses on visual counting, which aims to predict the number of occurrences given a natural image and a query (e.g. a question or a category). Unlike most prior works th…
Improved Baselines with Momentum Contrastive Learning
Xinlei Chen, Haoqi Fan, Ross Girshick +1
Contrastive unsupervised learning has recently shown encouraging progress, e.g., in Momentum Contrast (MoCo) and SimCLR. In this note, we verify the effectiveness of two of SimCLR'…
In Defense of Grid Features for Visual Question Answering
Huaizu Jiang, Ishan Misra, Marcus Rohrbach +2
Popularized as 'bottom-up' attention, bounding box (or region) based visual features have recently surpassed vanilla grid-based convolutional features as the de facto standard for…