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Coded ResNeXt: a network for designing disentangled information paths [article]

Apostolos Avranas, Marios Kountouris
2022
Notably, leveraging coding theory to design the information paths enables us to use intermediate network layers for making early predictions without having to evaluate the full network.  ...  We show that using our algorithm we can extract a lighter single-purpose binary classifier for a particular class by removing the parameters that do not participate in the predefined information path of  ...  The work [47] bears resemblance to ours where they identify such information paths; a key difference is that they use a post hoc method so that the paths are identified and not designed.  ... 
doi:10.48550/arxiv.2202.05343 fatcat:36yrz7wrx5cgfovp6atzs2x76m

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

Jason Kuen, Xiangfei Kong, Zhe Lin, Gang Wang, Jianxiong Yin, Simon See, Yap-Peng Tan
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
We propose a novel approach for cost-adjustable inference in CNNs -Stochastic Downsampling Point (SDPoint).  ...  Sharing network parameters across different instances provides significant regularization boost. During inference, one may handpick a SDPoint instance that best fits the inference budget.  ...  The ROSE Lab is supported by the National Research Foundation, Singapore, and the Infocomm Media Development Authority, Singapore. 2 It is a standard practice [10, 11, 40, 14] to resize images to a  ... 
doi:10.1109/cvpr.2018.00827 dblp:conf/cvpr/KuenK00YST18 fatcat:fmjdxcxt2fgjpn55kt3or5oeau

Frequency Disentangled Residual Network [article]

Satya Rajendra Singh, Roshan Reddy Yedla, Shiv Ram Dubey, Rakesh Sanodiya, Wei-Ta Chu
2022 arXiv   pre-print
In this paper, a frequency disentangled residual network (FDResNet) is proposed to tackle these issues.  ...  Moreover, the present residual networks are not able to utilize the high and low frequency information suitably, which also challenges the generalization capability of the network.  ...  We would like to acknowledge the NVIDIA for supporting with NVIDIA GPUs and Google Colab service for providing free computational resources which have been used in this research for the experiments.  ... 
arXiv:2109.12556v2 fatcat:hiu5boclhrgopb3bx4w7wzmv4a

Advancing Tassel Detection and Counting: Annotation and Algorithms

Azam Karami, Karoll Quijano, Melba Crawford
2021 Remote Sensing  
High-resolution RGB imagery acquired by unmanned aerial vehicles (UAVs), coupled with advanced machine learning approaches, including deep learning (DL), provides a new capability for monitoring flowering  ...  Tassel counts provide valuable information related to flowering and yield prediction in maize, but are expensive and time-consuming to acquire via traditional manual approaches.  ...  Changye Yang, Enyu Cai, and Jethro Zhou for their contributions to data annotation.  ... 
doi:10.3390/rs13152881 fatcat:fmp5ghb7zbhi5gjy5jviofeqwy

Variational Nested Dropout [article]

Yufei Cui, Yu Mao, Ziquan Liu, Qiao Li, Antoni B. Chan, Xue Liu, Tei-Wei Kuo, Chun Jason Xue
2022 arXiv   pre-print
Based on this approach, we design a Bayesian nested neural network that learns the order knowledge of the parameter distributions.  ...  For nested nets, when network parameters are removed, the performance decays in a human-specified trajectory rather than in a trajectory learned from data.  ...  a single hyper-parameter β which helps to quantify the degree of learnt disentanglement.  ... 
arXiv:2101.11353v2 fatcat:wlugbqz5srej3jgrx22xthm3xi

Improved Residual Networks for Image and Video Recognition [article]

Ionut Cosmin Duta, Li Liu, Fan Zhu, Ling Shao
2020 arXiv   pre-print
Our proposed improvements address all three main components of a ResNet: the flow of information through the network layers, the residual building block, and the projection shortcut.  ...  In the deep learning era, we establish a new milestone for the depth of a CNN.  ...  Our proposed approach facilitates learning by offering a better path for information to propagate through the network.  ... 
arXiv:2004.04989v1 fatcat:ghvk4fbr6nd7lf7rk345bbq5iq

Crossmodality Person Reidentification Based on Global and Local Alignment

Qiong Lou, Junfeng Li, Yaguan Qian, Anlin Sun, Fang Lu, Hasan Ali Khattak
2022 Wireless Communications and Mobile Computing  
In this paper, a novel AGF network is proposed for RGB-IR re-ID task, which is based on the idea of global and local alignment.  ...  By automatically learning the importance of different channel features, it strengthens the ability of the network to extract more fine-grained structural information of person crossmodalities.  ...  , which consists of two parts, one is a dual-path network for feature extraction, and the other is a bidirectional dual- constrained top-ranking loss for feature learning.  ... 
doi:10.1155/2022/4330804 fatcat:5f3v7wjk4nhd7hlhj4wr4o2rim

Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect [article]

Kaihua Tang, Jianqiang Huang, Hanwang Zhang
2021 arXiv   pre-print
We achieve new state-of-the-arts on three long-tailed visual recognition benchmarks: Long-tailed CIFAR-10/-100, ImageNet-LT for image classification and LVIS for instance segmentation.  ...  Our framework elegantly disentangles the paradoxical effects of the momentum, by pursuing the direct causal effect caused by an input sample.  ...  After equipped with ResNeXt-101-32x4d and ResNeXt-101-64x4d [2] for ImageNet-LT [9] and LVIS [7] V0.5, respectively, the proposed method gains additional improvements.  ... 
arXiv:2009.12991v4 fatcat:iirc3ar72bhelm7kjv2thcssxa

EfficientDet: Scalable and Efficient Object Detection

Mingxing Tan, Ruoming Pang, Quoc V. Le
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency.  ...  resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time.  ...  Acknowledgements Special thanks to Golnaz Ghiasi, Adams Yu, Daiyi Peng for their help on infrastructure and discussion. We also thank Adam Kraft, Barret Zoph, Ekin D.  ... 
doi:10.1109/cvpr42600.2020.01079 dblp:conf/cvpr/TanPL20 fatcat:nohfrx22jvb63n37irx65lroou

Virtual sawing using generative adversarial networks

Daniel Batrakhanov, Fedor Zolotarev, Tuomas Eerola, Lasse Lensu, Heikki Kalviainen
2021 2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ)  
The specific aims were to choose an appropriate generative adversarial network architecture to build a trainable model as an extension to an existing virtual sawing system.  ...  The main objective of this thesis was to study the suitability of state-of-the-art generative adversarial networks for virtual sawing.  ...  The vanilla structure comprises a contracting path (left side) and an expansive path (right side) as shown in Figure 38 .  ... 
doi:10.1109/ivcnz54163.2021.9653436 fatcat:unb3q4fvmjdxzao37yrzyqo2am

EfficientDet: Scalable and Efficient Object Detection [article]

Mingxing Tan, Ruoming Pang, Quoc V. Le
2020 arXiv   pre-print
In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency.  ...  resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time.  ...  Acknowledgements Special thanks to Golnaz Ghiasi, Adams Yu, Daiyi Peng for their help on infrastructure and discussion. We also thank Adam Kraft, Barret Zoph, Ekin D.  ... 
arXiv:1911.09070v7 fatcat:nzgqoegmlvhk3hlkyomzhtsmfu

BlockDrop: Dynamic Inference Paths in Residual Networks

Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, Rogerio Feris
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
In particular, given a pretrained ResNet, we train a policy network in an associative reinforcement learning setting for the dual reward of utilizing a minimal number of blocks while preserving recognition  ...  Exploiting the robustness of Residual Networks (ResNets) to layer dropping, our framework selects on-the-fly which residual blocks to evaluate for a given novel image.  ...  While this happens in standard ResNets as well, all images necessarily utilize all the paths, and disentangling this information is not possible. Instance difficulty.  ... 
doi:10.1109/cvpr.2018.00919 dblp:conf/cvpr/WuNKRDGF18 fatcat:b4vxf6d3mjgjro3kvf6rznsvoq

BlockDrop: Dynamic Inference Paths in Residual Networks [article]

Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, Rogerio Feris
2019 arXiv   pre-print
In particular, given a pretrained ResNet, we train a policy network in an associative reinforcement learning setting for the dual reward of utilizing a minimal number of blocks while preserving recognition  ...  Exploiting the robustness of Residual Networks (ResNets) to layer dropping, our framework selects on-the-fly which residual blocks to evaluate for a given novel image.  ...  While this happens in standard ResNets as well, all images necessarily utilize all the paths, and disentangling this information is not possible. Instance difficulty.  ... 
arXiv:1711.08393v4 fatcat:hrmjqu2p6raqlbhi65bx4z3x2u

CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope

Dulari Bhatt, Chirag Patel, Hardik Talsania, Jigar Patel, Rasmika Vaghela, Sharnil Pandya, Kirit Modi, Hemant Ghayvat
2021 Electronics  
Significant emphasis has been given to leveraging channel and spatial information, with a depth of architecture and information processing via multi-path.  ...  Furthermore, the introduction of large amounts of data and readily available hardware has opened new avenues for CNN study.  ...  Acknowledgments: The authors would like to thank the reviewers for their valuable suggestions which helped in improving the quality of this paper.  ... 
doi:10.3390/electronics10202470 fatcat:aqhrysjtbjagzl6byalgy2du5a

Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach [article]

Zhenyu Wu, Karthik Suresh, Priya Narayanan, Hongyu Xu, Heesung Kwon, Zhangyang Wang
2020 arXiv   pre-print
(NDFT), for the specific challenging problem of object detection in UAV images, achieving a substantial gain in robustness to those nuisances.  ...  The code is available at https://github.com/TAMU-VITA/UAV-NDFT.  ...  a specific time/location with each UAV flight's time-stamp and spatial location (or path).  ... 
arXiv:1908.03856v2 fatcat:d7gx37iz2nbrhiqg27axtl64nu
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