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Background/aims To improve the accuracy of pterygium screening and detection through smartphones, we established a fusion training model by blending a large number of slit-lamp image data with a small proportion of smartphone data. Method Two datasets were used, a slit-lamp image dataset containing 20 987 images and a smartphone-based image dataset containing 1094 images. The RFRC (Faster RCNN based on ResNet101) model for the detection model. The SRU-Net (U-Net based on SE-ResNeXt50) for the segmentation models. The open-cv algorithm measured the width, length and area of pterygium in the cornea. Results The detection model (trained by slit-lamp images) obtained the mean accuracy of 95.24%. The fusion segmentation model (trained by smartphone and slit-lamp images) achieved a microaverage F1 score of 0.8981, sensitivity of 0.8709, specificity of 0.9668 and area under the curve (AUC) of 0.9295. Compared with the same group of patients' smartphone and slit-lamp images, the fusion model performance in smartphone-based images (F1 score of 0.9313, sensitivity of 0.9360, specificity of 0.9613, AUC of 0.9426, accuracy of 92.38%) is close to the model (trained by slit-lamp images) in slit-lamp images (F1 score of 0.9448, sensitivity of 0.9165, specificity of 0.9689, AUC of 0.9569 and accuracy of 94.29%). Conclusion Our fusion model method got high pterygium detection and grading accuracy in insufficient smartphone data, and its performance is comparable to experienced ophthalmologists and works well in different smartphone brands. [ABSTRACT FROM AUTHOR]

To reduce computational redundancies, a common approach is to integrate detection and re-identification (Re-ID) into a single network in multi-object tracking (MOT), referred to as "tracking by detection." Most of the previous research has focused on resolving the conflict between the detection and Re-ID branches, considering it a simple coupling. In our work, we uncover that the entangled state between the detection and Re-ID tasks is much more complex than previous idea, resulting in a form of competition that degrades performance. To address the preceding issue, we propose a feature disentanglement network that deeply disentangles the intricately interwoven latent space of features and provides differentiated feature maps for each individual task. Furthermore, considering the demand for shallow semantic features in the feature re-ID branch, we also introduce a feature re-globalization module to enrich the shallow semantics. By integrating two distinct networks into a one-shot online MOT method, we develop a robust MOT tracker (named HDGTrack). We conduct extensive experiments on a number of benchmarks, and our experimental results demonstrate that our method significantly outperforms state-of-the-art MOT methods. Besides, HDGTrack is efficient and can run at 13.9 (MOT17) and 8.7 (MOT20) frames per second. [ABSTRACT FROM AUTHOR]

Adversarial examples provide an opportunity as well as impose a challenge for understanding image classification systems. Based on the analysis of the adversarial training solution--Adversarial Logits Pairing (ALP), we observed in this work that: (1) The inference of adversarially robust model tends to rely on fewer highcontribution features compared with vulnerable ones. (2) The training target of ALP does not fit well to a noticeable part of samples, where the logits pairing loss is overemphasized and obstructs minimizing the classification loss. Motivated by these observations, we design an Adaptive Adversarial Logits Pairing (AALP) solution by modifying the training process and training target of ALP. Specifically, AALP consists of an adaptive feature optimization module with Guided Dropout to systematically pursue fewer high-contribution features, and an adaptive sample weighting module by setting sample-specific training weights to balance between logits pairing loss and classification loss. The proposed AALP solution demonstrates superior defense performance on multiple datasets with extensive experiments. [ABSTRACT FROM AUTHOR]

Earthquakes have a profound impact on social harmony and property, resulting in damage to buildings and infrastructure. Effective earthquake rescue efforts require rapid and accurate determination of whether any survivors are trapped in the rubble of collapsed buildings. While deep learning algorithms can enhance the speed of rescue operations using single-modal data (either visual or audio), they are confronted with two primary challenges: insufficient information provided by single-modal data and catastrophic forgetting. In particular, the complexity of earthquake scenes means that single-modal features may not provide adequate information. Additionally, catastrophic forgetting occurs when the model loses the information learned in a previous task after training on subsequent tasks, due to non-stationary data distributions in changing earthquake scenes. To address these challenges, we propose an innovative approach that utilizes an incremental audio-visual fusion model for person recognition in earthquake rescue scenarios. Firstly, we leverage a cross-modal hybrid attention network to capture discriminative temporal context embedding, which uses self-attention and cross-modal attention mechanisms to combine multi-modality information, enhancing the accuracy and reliability of person recognition. Secondly, an incremental learning model is proposed to overcome catastrophic forgetting, which includes elastic weight consolidation and feature replay modules. Specifically, the elastic weight consolidation module slows down learning on certain weights based on their importance to previously learned tasks. The feature replay module reviews the learned knowledge by reusing the features conserved from the previous task, thus preventing catastrophic forgetting in dynamic environments. To validate the proposed algorithm, we collected the Audio-Visual Earthquake Person Recognition (AVEPR) dataset from earthquake films and real scenes. Furthermore, the proposed method gets 85.41% accuracy while learning the 10th new task, which demonstrates the effectiveness of the proposed method and highlights its potential to significantly improve earthquake rescue efforts. [ABSTRACT FROM AUTHOR]