69 citations · 110 across the 8 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…