activity
20172020
most citedBillion-scale semi-supervised learning for image classification

330 citations · 359 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CV2020

CPARR: Category-based Proposal Analysis for Referring Relationships

Chuanzi He, Haidong Zhu, Jiyang Gao +2

The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of \texttt{<subject, predicat…

cs.CV202029 cited

FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions

Alvin Wan, Xiaoliang Dai, Peizhao Zhang +9

Differentiable Neural Architecture Search (DNAS) has demonstrated great success in designing state-of-the-art, efficient neural networks. However, DARTS-based DNAS's search space i…

cs.CV2020

Video Object Grounding using Semantic Roles in Language Description

Arka Sadhu, Kan Chen, Ram Nevatia

We explore the task of Video Object Grounding (VOG), which grounds objects in videos referred to in natural language descriptions. Previous methods apply image grounding based algo…

cs.CV2019

Zero-Shot Grounding of Objects from Natural Language Queries

Arka Sadhu, Kan Chen, Ram Nevatia

A phrase grounding system localizes a particular object in an image referred to by a natural language query. In previous work, the phrases were restricted to have nouns that were e…

cs.CV2019

Cascaded Parallel Filtering for Memory-Efficient Image-Based Localization

Wentao Cheng, Weisi Lin, Kan Chen +1

Image-based localization (IBL) aims to estimate the 6DOF camera pose for a given query image. The camera pose can be computed from 2D-3D matches between a query image and Structure…

cs.CV2019330 cited

Billion-scale semi-supervised learning for image classification

I. Zeki Yalniz, Hervé Jégou, Kan Chen +2

This paper presents a study of semi-supervised learning with large convolutional networks. We propose a pipeline, based on a teacher/student paradigm, that leverages a large collec…