activity
20152022
most citedDeep Learning for Scene Classification: A Survey

22 citations · 71 across the 9 of their papers we have counts for

collaborators

15 papers

cs.AI202215 cited

Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence

Matti Pietikäinen, Olli Silven

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily inve…

cs.LG20213 cited

Dynamic Binary Neural Network by learning channel-wise thresholds

Jiehua Zhang, Zhuo Su, Yanghe Feng +3

Binary neural networks (BNNs) constrain weights and activations to +1 or -1 with limited storage and computational cost, which is hardware-friendly for portable devices. Recently,…

cs.CV202114 cited

Pixel Difference Networks for Efficient Edge Detection

Zhuo Su, Wenzhe Liu, Zitong Yu +5

Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level performance in edge detection with the rich and abstract edge representation capacities. However, the hi…

cs.CV202122 cited

Deep Learning for Scene Classification: A Survey

Delu Zeng, Minyu Liao, Mohammad Tavakolian +5

Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challengin…

cs.CV2020

FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going Beyond

Zhuo Su, Linpu Fang, Deke Guo +3

Binary neural networks (BNNs), where both weights and activations are binarized into 1 bit, have been widely studied in recent years due to its great benefit of highly accelerated…

cs.CV20204 cited

Dynamic Group Convolution for Accelerating Convolutional Neural Networks

Zhuo Su, Linpu Fang, Wenxiong Kang +3

Replacing normal convolutions with group convolutions can significantly increase the computational efficiency of modern deep convolutional networks, which has been widely adopted i…