482 citations · 1.4k across the 36 of their papers we have counts for
16 papers · 1 filter
FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision
Ramanpreet Singh Pahwa, Jin Chao, Jestine Paul +7
Regular inspection of rail valves and engines is an important task to ensure the safety and efficiency of railway networks around the globe. Over the past decade, computer vision a…
A*3D Dataset: Towards Autonomous Driving in Challenging Environments
Quang-Hieu Pham, Pierre Sevestre, Ramanpreet Singh Pahwa +6
With the increasing global popularity of self-driving cars, there is an immediate need for challenging real-world datasets for benchmarking and training various computer vision tas…
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Bruno Lecouat, Ken Chang, Chuan-Sheng Foo +7
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require…
Holistic Multi-modal Memory Network for Movie Question Answering
Anran Wang, Anh Tuan Luu, Chuan-Sheng Foo +3
Answering questions according to multi-modal context is a challenging problem as it requires a deep integration of different data sources. Existing approaches only employ partial i…
Deep Adaptive Temporal Pooling for Activity Recognition
Sibo Song, Ngai-Man Cheung, Vijay Chandrasekhar +1
Deep neural networks have recently achieved competitive accuracy for human activity recognition. However, there is room for improvement, especially in modeling long-term temporal i…
End-to-End Video Classification with Knowledge Graphs
Fang Yuan, Zhe Wang, Jie Lin +4
Video understanding has attracted much research attention especially since the recent availability of large-scale video benchmarks. In this paper, we address the problem of multi-l…
Pruning Convolutional Neural Networks for Image Instance Retrieval
Gaurav Manek, Jie Lin, Vijay Chandrasekhar +4
In this work, we focus on the problem of image instance retrieval with deep descriptors extracted from pruned Convolutional Neural Networks (CNN). The objective is to heavily prune…
Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text
Zhe Wang, Kingsley Kuan, Mathieu Ravaut +13
The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% o…
Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge
Kingsley Kuan, Mathieu Ravaut, Gaurav Manek +7
We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignan…
Compact Descriptors for Video Analysis: the Emerging MPEG Standard
Ling-Yu Duan, Vijay Chandrasekhar, Shiqi Wang +6
This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG). MPEG-CDVA targets at d…
Compression of Deep Neural Networks for Image Instance Retrieval
Vijay Chandrasekhar, Jie Lin, Qianli Liao +4
Image instance retrieval is the problem of retrieving images from a database which contain the same object. Convolutional Neural Network (CNN) based descriptors are becoming the do…
Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval
Olivier Morère, Jie Lin, Antoine Veillard +2
The goal of this work is the computation of very compact binary hashes for image instance retrieval. Our approach has two novel contributions. The first one is Nested Invariance Po…
Group Invariant Deep Representations for Image Instance Retrieval
Olivier Morère, Antoine Veillard, Jie Lin +3
Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in…
A Practical Guide to CNNs and Fisher Vectors for Image Instance Retrieval
Vijay Chandrasekhar, Jie Lin, Olivier Morère +2
With deep learning becoming the dominant approach in computer vision, the use of representations extracted from Convolutional Neural Nets (CNNs) is quickly gaining ground on Fisher…
Co-Regularized Deep Representations for Video Summarization
Olivier Morère, Hanlin Goh, Antoine Veillard +2
Compact keyframe-based video summaries are a popular way of generating viewership on video sharing platforms. Yet, creating relevant and compelling summaries for arbitrarily long v…
DeepHash: Getting Regularization, Depth and Fine-Tuning Right
Jie Lin, Olivier Morere, Vijay Chandrasekhar +2
This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval. We propose DeepHash: a hashing…