456 citations · 1.4k across the 16 of their papers we have counts for
17 papers
Differential Viewpoints for Ground Terrain Material Recognition
Jia Xue, Hang Zhang, Ko Nishino +1
Computational surface modeling that underlies material recognition has transitioned from reflectance modeling using in-lab controlled radiometric measurements to image-based repres…
Character Matters: Video Story Understanding with Character-Aware Relations
Shijie Geng, Ji Zhang, Zuohui Fu +3
Different from short videos and GIFs, video stories contain clear plots and lists of principal characters. Without identifying the connection between appearing people and character…
Improving Semantic Segmentation via Self-Training
Yi Zhu, Zhongyue Zhang, Chongruo Wu +6
Deep learning usually achieves the best results with complete supervision. In the case of semantic segmentation, this means that large amounts of pixelwise annotations are required…
ResNeSt: Split-Attention Networks
Hang Zhang, Chongruo Wu, Zhongyue Zhang +9
It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies t…
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
Nick Erickson, Jonas Mueller, Alexander Shirkov +4
We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabula…
Small-GAN: Speeding Up GAN Training Using Core-sets
Samarth Sinha, Han Zhang, Anirudh Goyal +3
Recent work by Brock et al. (2018) suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch sizes. Unfortunately, using large batches i…
2nd Place Solution to the GQA Challenge 2019
Shijie Geng, Ji Zhang, Hang Zhang +2
We present a simple method that achieves unexpectedly superior performance for Complex Reasoning involved Visual Question Answering. Our solution collects statistical features from…
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
Jian Guo, He He, Tong He +13
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-…
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources
Haibin Lin, Hang Zhang, Yifei Ma +4
With an increasing demand for training powers for deep learning algorithms and the rapid growth of computation resources in data centers, it is desirable to dynamically schedule di…
DenseAttentionSeg: Segment Hands from Interacted Objects Using Depth Input
Zihao Bo, Hao Zhang, Junhai Yong +1
We propose a real-time DNN-based technique to segment hand and object of interacting motions from depth inputs. Our model is called DenseAttentionSeg, which contains a dense attent…
Bag of Freebies for Training Object Detection Neural Networks
Zhi Zhang, Tong He, Hang Zhang +3
Training heuristics greatly improve various image classification model accuracies~\cite{he2018bag}. Object detection models, however, have more complex neural network structures an…
Bag of Tricks for Image Classification with Convolutional Neural Networks
Tong He, Zhi Zhang, Hang Zhang +3
Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods.…
Deep Texture Manifold for Ground Terrain Recognition
Jia Xue, Hang Zhang, Kristin Dana
We present a texture network called Deep Encoding Pooling Network (DEP) for the task of ground terrain recognition. Recognition of ground terrain is an important task in establishi…
Context Encoding for Semantic Segmentation
Hang Zhang, Kristin Dana, Jianping Shi +4
Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convol…
Photo-realistic Facial Texture Transfer
Parneet Kaur, Hang Zhang, Kristin J. Dana
Style transfer methods have achieved significant success in recent years with the use of convolutional neural networks. However, many of these methods concentrate on artistic style…
Friction from Reflectance: Deep Reflectance Codes for Predicting Physical Surface Properties from One-Shot In-Field Reflectance
Hang Zhang, Kristin Dana, Ko Nishino
Images are the standard input for vision algorithms, but one-shot infield reflectance measurements are creating new opportunities for recognition and scene understanding. In this w…
Reflectance Hashing for Material Recognition
Hang Zhang, Kristin Dana, Ko Nishino
We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing…