activity
20182021
most citedAnalysis of diversity-accuracy tradeoff in image captioning

11 citations · 21 across the 6 of their papers we have counts for

collaborators

9 papers

cs.CV20211 cited

Goal-driven text descriptions for images

Ruotian Luo

A big part of achieving Artificial General Intelligence(AGI) is to build a machine that can see and listen like humans. Much work has focused on designing models for image classifi…

cs.CV20202 cited

Controlling Length in Image Captioning

Ruotian Luo, Greg Shakhnarovich

We develop and evaluate captioning models that allow control of caption length. Our models can leverage this control to generate captions of different style and descriptiveness.

cs.CV20204 cited

Detection and Description of Change in Visual Streams

Davis Gilton, Ruotian Luo, Rebecca Willett +1

This paper presents a framework for the analysis of changes in visual streams: ordered sequences of images, possibly separated by significant time gaps. We propose a new approach t…

cs.CV2020

Pixel Consensus Voting for Panoptic Segmentation

Haochen Wang, Ruotian Luo, Michael Maire +1

The core of our approach, Pixel Consensus Voting, is a framework for instance segmentation based on the Generalized Hough transform. Pixels cast discretized, probabilistic votes fo…

cs.CV2020

A Better Variant of Self-Critical Sequence Training

Ruotian Luo

In this work, we present a simple yet better variant of Self-Critical Sequence Training. We make a simple change in the choice of baseline function in REINFORCE algorithm. The new…

cs.CL202011 cited

Analysis of diversity-accuracy tradeoff in image captioning

Ruotian Luo, Gregory Shakhnarovich

We investigate the effect of different model architectures, training objectives, hyperparameter settings and decoding procedures on the diversity of automatically generated image c…