The seed word-driven approach based on weakly supervised text classification (WTC) is the dominant approach. In existing seed word-driven methods,using metrics such as Term Frequency (TF), Inverse Document Frequency (IDF) and its combinations to update the seed words. the method assigns the same weight to all metrics, leading to the selection of common or poorly differentiated words as seed words; In addition most of the text classifiers used in the study have difficulty in capturing the correlation and global information between text information. In order to solve the above problems, Using Transformer as a text classifier first, The multi-headed self-attention mechanism allows capturing longrange dependencies while computing in parallel and fully learning the global semantic information of the input text. Then an improved TF-IDF method is proposed to increase the weight of IDF so that some common words that affect the classification can be filtered out. Its experimental results are improved on 20News and NYT datasets.
In the complex tracking environment, most of existing correlation filter-based tracking algorithms are often unable to track the target stably for a long time. To solve this challenging problem, in this paper, we propose a long-term correlation filter tracking algorithm based on adaptive feature fusion. Firstly, we normalize the response peaks of different features to dynamically assign feature weights for the purpose of combining HOG features and color histogram features adaptively. Secondly, a detection filter is learned to detect the tracking results, and we judge the confidence of detection results by the detection response peak and the normalized value of average peak correlation energy. If the target is in a low confidence state, the re-detection module is employed to relocate the target to achieve long-term tracking. Our experimental results on the OTB-2013 and OTB-2015 benchmark datasets demonstrate that the proposed method performs favorably against some state-of-the-art methods in terms of accuracy and robustness. Furthermore, the proposed method satisfies the accuracy and real-time requirements of long-term tracking in complex environments.
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