activity
20162020
most citedSENSE: a Shared Encoder Network for Scene-flow Estimation

7 citations · 9 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2020

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…

cs.CV20197 cited

SENSE: a Shared Encoder Network for Scene-flow Estimation

Huaizu Jiang, Deqing Sun, Varun Jampani +3

We introduce a compact network for holistic scene flow estimation, called SENSE, which shares common encoder features among four closely-related tasks: optical flow estimation, dis…

cs.CV2019

Automatic adaptation of object detectors to new domains using self-training

Aruni RoyChowdhury, Prithvijit Chakrabarty, Ashish Singh +4

This work addresses the unsupervised adaptation of an existing object detector to a new target domain. We assume that a large number of unlabeled videos from this domain are readil…

cs.CV2018

Unsupervised Hard Example Mining from Videos for Improved Object Detection

SouYoung Jin, Aruni RoyChowdhury, Huaizu Jiang +4

Important gains have recently been obtained in object detection by using training objectives that focus on {\em hard negative} examples, i.e., negative examples that are currently…

cs.CV20172 cited

Reasoning about Fine-grained Attribute Phrases using Reference Games

Jong-Chyi Su, Chenyun Wu, Huaizu Jiang +1

We present a framework for learning to describe fine-grained visual differences between instances using attribute phrases. Attribute phrases capture distinguishing aspects of an ob…

cs.CV2016

Face Detection with the Faster R-CNN

Huaizu Jiang, Erik Learned-Miller

The Faster R-CNN has recently demonstrated impressive results on various object detection benchmarks. By training a Faster R-CNN model on the large scale WIDER face dataset, we rep…