Results 211 to 220 of about 368,302 (254)

DeM-FCN: an ultra-lightweight and purely convolutional framework for edge-native human activity recognition in wearable fitness tracking. [PDF]

Front Sports Act Living
Xu Y, Li J, Huang Z, Cheng T, Chen C, Tan B, Tan Z, Chen H, Zhou Y.   +8 more
europepmc   +1 more source

Detection of Pediatric Dental Caries in Panoramic Radiograph Using Deep Learning: A Benchmark Study on MD-OPG. [PDF]

Sensors (Basel)
Rahimi H, Naeimi SM, Darvish S, Nazemi Salman B, Razzaghi P, Luchian I, Budala DG.   +6 more
europepmc   +1 more source

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Incremental focal loss GANs

Information Processing & Management, 2020
Abstract Generative Adversarial Networks (GANs) have achieved inspiring performance in both unsupervised image generation and conditional cross-modal image translation. However, how to generate quality images at an affordable cost is still challenging.
Fei Gao 0006, Jingjie Zhu, Hanliang Jiang, Zhenxing Niu, Weidong Han 0001, Jun Yu 0002   +5 more
openaire   +1 more source

Robust visual tracking with channel attention and focal loss

Neurocomputing, 2020
Abstract Recently, the tracking community leads a fashion of end-to-end feature representation learning for visual tracking. Previous works treat all feature channels and training samples equally during training. This ignores channel interdependencies and foreground–background data imbalance, thus limiting the tracking performance.
Dongdong Li, Yangliu Kuai, Fatih Porikli   +2 more
exaly   +3 more sources

Focal Text: an Accurate Text Detection with Focal Loss

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
Text detection in natural scene images is an important and popular task in the computer vision community. Due to slanted characters and blurred images in natural environments, it is a challenging task under active research. In this paper, we propose a Focal Text Detection Network (FTDN), which could be trained well without abundant data.
Xiaowei Tian, Dao Wu, Rui Wang 0032, Xiaochun Cao   +3 more
openaire   +1 more source

Noise Resistant Focal Loss for Object Detection

2020
Noise robustness and hard example mining are two important aspects in object detection. A common view is that the two techniques are contradictory and they cannot be combined. In this paper, we show that there is a possibility to combine the best of two techniques.
Zibo Hu, Kun Gao 0001, Xiaodian Zhang, Zeyang Dou   +3 more
openaire   +1 more source

Focal Loss for Region Proposal Network

2018
Currently, most state-of-the-art object detection models are based on a two-stage scheme pioneered by R-CNN and integrated with region proposal network (RPN), which is served as proposal generation. During the training of RPN, only a fixed number of samples with a fixed object/not-object ratio are sampled to avoid class imbalance problem.
Chengpeng Chen, Xinhang Song, Shuqiang Jiang   +2 more
openaire   +1 more source

Focal Loss of Pigment in the Belgian Tervuren Dog

Journal of the American Veterinary Medical Association, 1978
SUMMARY Hypopigmentation most commonly affecting the face and mouth of the Belgian Tervuren dog was characterized by an absence of melanocytes in the epidermis. Pigment loss usually occurred during young adulthood, and although there was partial repigmentation in some dogs, complete repigmentation did not occur.
M B, Mahaffey, K M, Yarbrough, J F, Munnell   +2 more
openaire   +2 more sources

Focal hair loss

The Journal of Family Practice, 2022
Reid A. Waldman, Jane M. Grant-Kels
openaire   +2 more sources