69 citations · 268 across the 20 of their papers we have counts for
20 papers
OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization
Peng Hu, Xi Peng, Hongyuan Zhu +2
As Deep Neural Networks (DNNs) usually are overparameterized and have millions of weight parameters, it is challenging to deploy these large DNN models on resource-constrained hard…
On Representation Knowledge Distillation for Graph Neural Networks
Chaitanya K. Joshi, Fayao Liu, Xu Xun +2
Knowledge distillation is a learning paradigm for boosting resource-efficient graph neural networks (GNNs) using more expressive yet cumbersome teacher models. Past work on distill…
PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery
Tianyi Zhang, Jie Lin, Peng Hu +2
Non-maximum Suppression (NMS) is an essential postprocessing step in modern convolutional neural networks for object detection. Unlike convolutions which are inherently parallel, t…
A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition
Govind Narasimman, Kangkang Lu, Arun Raja +4
Despite the vast literature on Human Activity Recognition (HAR) with wearable inertial sensor data, it is perhaps surprising that there are few studies investigating semisupervised…
Deeply Activated Salient Region for Instance Search
Hui-Chu Xiao, Wan-Lei Zhao, Jie Lin +1
The performance of instance search depends heavily on the ability to locate and describe a wide variety of object instances in a video/image collection. Due to the lack of proper m…
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…
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks
Xue Geng, Jie Fu, Bin Zhao +4
This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage.…
TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks
Lile Cai, Anne-Maelle Barneche, Arthur Herbout +4
Embedded deep learning platforms have witnessed two simultaneous improvements. First, the accuracy of convolutional neural networks (CNNs) has been significantly improved through t…
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…
Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing
Jie Lin, Olivier Morère, Julie Petta +2
A typical image retrieval pipeline starts with the comparison of global descriptors from a large database to find a short list of candidate matches. A good image descriptor is key…
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…