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- Carnegie Mellon UniversityUS49 papers
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8 papers · 2 filters
Large Scale Open-Set Deep Logo Detection
Muhammet Bastan, Hao-Yu Wu, Tian Cao +2
We present an open-set logo detection (OSLD) system, which can detect (localize and recognize) any number of unseen logo classes without re-training; it only requires a small set o…
Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles
Dror Simon, Miriam Farber, Roman Goldenberg
Auto-annotation by ensemble of models is an efficient method of learning on unlabeled data. Wrong or inaccurate annotations generated by the ensemble may lead to performance degrad…
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations
Luming Liang, Sen Deng, Lionel Gueguen +3
We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise. A median layer…
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…
Learning to Generate Synthetic Data via Compositing
Shashank Tripathi, Siddhartha Chandra, Amit Agrawal +3
We present a task-aware approach to synthetic data generation. Our framework employs a trainable synthesizer network that is optimized to produce meaningful training samples by ass…
ANTNets: Mobile Convolutional Neural Networks for Resource Efficient Image Classification
Yunyang Xiong, Hyunwoo J. Kim, Varsha Hedau
Deep convolutional neural networks have achieved remarkable success in computer vision. However, deep neural networks require large computing resources to achieve high performance.…