Search Results for author:
Found 10 papers, 6 papers with code
Goal-driven text descriptions for images
In Chapter 3, we focus on generating the referring expression, a text description for an object in the image so that a receiver can infer which object is being described.
Controlling Length in Image Captioning
We develop and evaluate captioning models that allow control of caption length.
Pixel Consensus Voting for Panoptic Segmentation
The core of our approach, Pixel Consensus Voting, is a framework for instance segmentation based on the Generalized Hough transform.
Ranked #36 on
Panoptic Segmentation
on COCO test-dev
Detection and Description of Change in Visual Streams
This paper presents a framework for the analysis of changes in visual streams: ordered sequences of images, possibly separated by significant time gaps.
A Better Variant of Self-Critical Sequence Training
In this work, we present a simple yet better variant of Self-Critical Sequence Training.
Ranked #24 on
Image Captioning
on COCO Captions
Analysis of diversity-accuracy tradeoff in image captioning
We investigate the effect of different model architectures, training objectives, hyperparameter settings and decoding procedures on the diversity of automatically generated image captions.
DIODE: A Dense Indoor and Outdoor DEpth Dataset
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements.
Context-Aware Zero-Shot Recognition
We present a novel problem setting in zero-shot learning, zero-shot object recognition and detection in the context.
Discriminability objective for training descriptive captions
One property that remains lacking in image captions generated by contemporary methods is discriminability: being able to tell two images apart given the caption for one of them.
Comprehension-guided referring expressions
Second, we use the comprehension module in a generate-and-rerank pipeline, which chooses from candidate expressions generated by a model according to their performance on the comprehension task.
