2.4k citations · 2.7k across the 10 of their papers we have counts for
13 papers
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…
ImageSpirit: Verbal Guided Image Parsing
Ming-Ming Cheng, Shuai Zheng, Wen-Yan Lin +6
Humans describe images in terms of nouns and adjectives while algorithms operate on images represented as sets of pixels. Bridging this gap between how humans would like to access…
Translation-Invariant Representation for Cumulative Foot Pressure Images
Shuai Zheng, Kaiqi Huang, Tieniu Tan
Human can be distinguished by different limb movements and unique ground reaction force. Cumulative foot pressure image is a 2-D cumulative ground reaction force during one gait cy…