2.4k citations · 2.7k across the 10 of their papers we have counts for
11 papers · 1 filter
Give me a hint! Navigating Image Databases using Human-in-the-loop Feedback
Bryan A. Plummer, M. Hadi Kiapour, Shuai Zheng +1
In this paper, we introduce an attribute-based interactive image search which can leverage human-in-the-loop feedback to iteratively refine image search results. We study active im…
ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations
Shuai Zheng, Fan Yang, M. Hadi Kiapour +1
Understanding clothes from a single image has strong commercial and cultural impacts on modern societies. However, this task remains a challenging computer vision problem due to wi…
Fast End-to-End Trainable Guided Filter
Huikai Wu, Shuai Zheng, Junge Zhang +1
Dense pixel-wise image prediction has been advanced by harnessing the capabilities of Fully Convolutional Networks (FCNs). One central issue of FCNs is the limited capacity to hand…
Conditional Image-Text Embedding Networks
Bryan A. Plummer, Paige Kordas, M. Hadi Kiapour +3
This paper presents an approach for grounding phrases in images which jointly learns multiple text-conditioned embeddings in a single end-to-end model. In order to differentiate te…
GP-GAN: Towards Realistic High-Resolution Image Blending
Huikai Wu, Shuai Zheng, Junge Zhang +1
It is common but challenging to address high-resolution image blending in the automatic photo editing application. In this paper, we would like to focus on solving the problem of h…
A Projected Gradient Descent Method for CRF Inference allowing End-To-End Training of Arbitrary Pairwise Potentials
Måns Larsson, Anurag Arnab, Fredrik Kahl +2
Are we using the right potential functions in the Conditional Random Field models that are popular in the Vision community? Semantic segmentation and other pixel-level labelling ta…
Fully-Trainable Deep Matching
James Thewlis, Shuai Zheng, Philip H. S. Torr +1
Deep Matching (DM) is a popular high-quality method for quasi-dense image matching. Despite its name, however, the original DM formulation does not yield a deep neural network that…
Higher Order Conditional Random Fields in Deep Neural Networks
Anurag Arnab, Sadeep Jayasumana, Shuai Zheng +1
We address the problem of semantic segmentation using deep learning. Most segmentation systems include a Conditional Random Field (CRF) to produce a structured output that is consi…
Joint Training of Generic CNN-CRF Models with Stochastic Optimization
Alexander Kirillov, Dmitrij Schlesinger, Shuai Zheng +3
We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Rand…
An equalised global graphical model-based approach for multi-camera object tracking
Weihua Chen, Lijun Cao, Xiaotang Chen +1
Non-overlapping multi-camera visual object tracking typically consists of two steps: single camera object tracking and inter-camera object tracking. Most of tracking methods focus…
Conditional Random Fields as Recurrent Neural Networks
Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes +5
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learnin…