Convolution-based neural attention with applications to sentiment classification

Neural attention mechanism has achieved many successes in various tasks in natural language processing. However, existing neural attention models based on a densely connected network are loosely related to the attention mechanism found in psychology and neuroscience. Motivated by the finding in neuroscience that human possesses the template-searching attention mechanism, we propose to use convolution operation to simulate attentions and give a mathematical explanation of our neural attention model. We then introduce a new network architecture, which combines a recurrent neural network with our convolution-based attention model and further stacks an attention-based neural model to build a hierarchical sentiment classification model. The experimental results show that our proposed models can capture salient parts of the text to improve the performance of sentiment classification at both the sentence level and the document level

REGION CONVOLUTIONAL NEURAL NETWORK SIAMESE UNTUK DETEKSI OBJEK REFERENSI PADA VIDEO REKAMAN CCTV

Dalam mendeteksi sebuah objek terdapat kasus di mana akan sulit untuk melakukan pendeteksian pada video rekaman CCTV, terlebih perangkat yang dipasang pada tempat yang akan merekam banyak objek yang berbeda dari waktu ke waktu. Jika pendeteksian objek pada CCTV dilakukan secara manual menggunakan tenaga manusia, akan memerlukan waktu yang relatif panjang mengingat memantau setiap layar kamera bukanlah pekerjaan yang mudah. Sehingga pendekatan yang dapat digunakan ialah pendekatan objek referensi. Pada penelitian ini akan digunakan penggabungan dari metode RCNN dengan Siamese, di mana pendeteksian wilayah proposal akan diganti dengan metode RPN dan klasifikasi dengan metode Siamese untuk mencari nilai kesamaan antar dua objek. Terdapat 4 objek yang akan dideteksi. Selain itu, akan dibuat dua buah model dengan arsitektur yang berbeda, di mana salah satu dari model tersebut ditambahkan metode perhitungan euclidean distance dan diuji untuk dilihat hasil mAP serta akurasi dari masing-masing model. Hal ini bertujuan untuk mengetahui kinerja model dari penggabungan metode tersebut. Dari penelitian yang telah dilakukan, model yang menggunakan euclidean distance mendapatkan hasil lebih tinggi dengan mAP sekitar 2% hingga 56% dan akurasi sekitar 4% hingga 27% bergantung pada proses awal pendeteksian, perubahan yang signifikan terhadap bentuk objek yang ditangkap oleh kamera dibandingkan dengan gambar objek target, dan pencahayaan pada video rekaman CCTV. In object detection, there are cases where will be difficult to detect from CCTV video footage, especially devices installed in places that will record many different objects from time to time. If object detection on CCTV is done manually using human operator, it will take a relatively long time considering that monitoring each camera screen is not an easy job. The approach that can be used is the object reference. In this study, the combination of RCNN method with Siamese will be used, where the detection of the proposal region will be replaced by the RPN method and classification with the Siamese method to find the similarity value between two objects. There are four objects to be detected. In addition, which two models with different architectures will be made, where one of the models is added with the Euclidean distance calculation method and tested to see the mAP results and the accuracy of each model. It aims to determine the performance of the model from the combination of these methods. The results shown the model that uses the Euclidean distance gets a higher with mAP of around 2% to 56% and accuracy of around 4% to 27%, depend on the initial detection process, a significant change in the shape of the object captured by the camera compared to the image of the target object, and illumination on CCTV video footage