Results 11 to 20 of about 368,302 (254)
Person Re-Identification With Triplet Focal Loss
IEEE Access, 2018 Person re-identification (ReID), which aims at matching individuals across non-overlapping cameras, has attracted much attention in the field of computer vision due to its research significance and potential applications.
Shizhou Zhang +4 more
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Focal Frame Loss: A Simple but Effective Loss for Precipitation Nowcasting
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Precipitation nowcasting is an important but hard problem. Currently, with the landing of deep learning, it has been treated as an image prediction problem based on radar echo maps.
Zhifeng Ma, Hao Zhang, Jie Liu
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Radar Waveform Recognition With ConvNeXt and Focal Loss
IEEE AccessA method of automatic recognition of radar waves based on time-frequency analysis (TFA) and ConvNeXt model is proposed in the paper. The method aims to address the challenges of feature extraction difficulty and low recognition correctness in complex ...
Liping Luo +3 more
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Focal Frequency Loss for Image Reconstruction and Synthesis [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 Image reconstruction and synthesis have witnessed remarkable progress thanks to the development of generative models. Nonetheless, gaps could still exist between the real and generated images, especially in the frequency domain. In this study, we show that narrowing gaps in the frequency domain can ameliorate image reconstruction and synthesis quality ...
Liming Jiang 0001 +3 more
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Dual Focal Loss for Calibration
CoRR, 2023 The use of deep neural networks in real-world applications require well-calibrated networks with confidence scores that accurately reflect the actual probability. However, it has been found that these networks often provide over-confident predictions, which leads to poor calibration.
Linwei Tao, Minjing Dong, Chang Xu 0002
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Focal Loss in 3D Object Detection [PDF]
IEEE Robotics and Automation Letters, 2019 IEEE RA-L 2019 to appear. Codes and trained weights are available on the project page(https://goo.gl/2hFbmL)
Peng Yun +4 more
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Intelligent Crack Detection Method Based on GM-ResNet
Sensors, 2023 Ensuring road safety, structural stability and durability is of paramount importance, and detecting road cracks plays a critical role in achieving these goals.
Xinran Li +4 more
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CoRR, 2022 The cross-entropy softmax loss is the primary loss function used to train deep neural networks. On the other hand, the focal loss function has been demonstrated to provide improved performance when there is an imbalance in the number of training samples in each class, such as in long-tailed datasets.
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Two-Stage Cross-Domain Ocular Disease Recognition With Data Augmentation
IEEE Access, 2023 Ophthalmic diseases afflict many people, and can even lead to irreversible blindness. Therefore, the search for effective early diagnosis methods has attracted the attention of many researchers and clinicians. At present, although there are some ways for
Qiong Wang, Zhilin Guo, Jun Yao, Nan Yan
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Deep learning-based remote sensing image scene classification methods are the current mainstream, and enough labeled samples are very important for their performance. Considering the fact that manual labeling of samples requires high labor and time cost,
Xiaoliang Qian +7 more
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