Learning visual question answering on controlled semantic noisy labels

Zhang, Haonan; Zeng, Pengpeng; Hu, Yuxuan; Qian, Jin; Song, Jingkuan; Gao, Lianli

doi:10.1016/j.patcog.2023.109339

Cited by 15 publications

(2 citation statements)

References 10 publications

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“…In medical imaging, continual learning has been applied to pathological image analysis [189] and segmentation [190]. Research is also burgeoning in applying continual learning to embodied AI [191], [192], [193], text recognition [194], [195], visual question answering [196], [197], [198], and sign language recognition [199], [200] is also emerging. The expansion from basic tasks to a wider spectrum of applications underscores continual learning's increasing potential for realworld impact.…”

Section: Applicationmentioning

confidence: 99%

Continual Learning with Knowledge Distillation: A Survey

Li,

Su,

Zhang

et al. 2024

Preprint

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The foremost challenge in continual learning is devising strategies to alleviate catastrophic forgetting, thereby preserving a model’s memory of prior knowledge while learning new tasks. Knowledge distillation, a form of data regularization, is garnering increasing attention in the field of continual learning for its ability to constrain a model’s discriminative power for previous tasks by emulating the outputs of old task models while learning new tasks, thus mitigating forgetting. This paper offers a comprehensive survey of continual learning methods employing knowledge distillation within the realm of image classification. We inductively categorize these methods according to the source of knowledge utilized and provide a detailed analysis of the distillation solutions they employ. Furthermore, given the characteristic inability of continual learning to access historical data, we introduce a novel taxonomy for continual learning approaches from the perspective of auxiliary data usage. In addition, we have conducted extensive experiments on CIFAR-100, TinyImageNet, and ImageNet-100 across nine knowledge distillation-integrated continual learning methods, deeply analyzing the role of knowledge distillation in different continual learning scenarios to alleviate model forgetting. Our substantial experimental evidence demonstrates that knowledge distillation can indeed reduce forgetting across most scenarios.

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Section: Applicationmentioning

confidence: 99%

Continual Learning with Knowledge Distillation: A Survey

Li,

Su,

Zhang

et al. 2024

Preprint

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“…(4) Modality-incremental CSS: Modality-incremental learning deals with incorporating new data modalities into a model's scope. A modality can be a different type of input data, such as adding text data to an existing visual model [77], [78] or introducing data from different sensors [79], [80]. Continual learning in this context refers to the model's ability to incorporate and learn from the new modality.…”

Section: Css Tasksmentioning

confidence: 99%

Ecosystem Health of the Beiyun River Basin (Beijing, China) as Evaluated by the Analytic Hierarchy Process Method

Yuan

Zhao

et al. 2021

Preprint

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Ecosystem services provided by river ecosystems rely on healthy ecosystem structure and ecological processes. The Beijing-Tianjin-Hebei urban is a typical water-deficient area. As an important part of the urban-rural integration construction, evaluating the health status of the Beiyun River Basin and discovering the weak links in the water environment is the basis for improving the health of the basin. In this study, Analytic Hierarchy Process was used to establish an evaluation index system for the Beiyun River Basin from 5 aspects including water quality, biology, habitat, hydrology, and social functions, and to assign weights to the index layer. The evaluation results showed that the health evaluation results of the Beiyun River Basin in 2019 are “sub-healthy”, and the overall health status is gradually worsening from northwest to southeast. In the middle reaches of the region, the evaluation result is "healthy", followed by the upstream, and the downstream is the worst. The results showed that areas with less human interference are in better health. The factors that affect the overall health evaluation status in the basin are the level of nutrition, biodiversity, and vegetation coverage. For the comprehensive management of the Beiyun River, the improvement of water quality and habitat ecological restoration is the key to the health of the upstream ecosystem health status. The improvement of the health status of the downstream should focus on equal emphasis on water quality and quantity, restoration of biodiversity, and improvement of the quality of the riparian ecological environment.

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