Investigation of Solar Flare Classification to Identify Optimal Performance

When an intense brightness for a small amount of time is seen in the sun, then we can say that a solar flare emerged. As solar flares are made up of high energy photons and particles, thus causing the production of high electric fields and currents and therefore results in the disruption in space-borne or ground-based technological system. It also becomes a challenging task to extract its important features for prediction. Convolutional Neural Networks have gain a significant amount of popularity in the classification and localization tasks. This paper has given stress on the classification of the solar flares emerged on different years by stacking different convolutional layers followed by max pooling layers. From the reference of Alexnet, the pooling layer employed in this paper is the overlapping pooling. Also two different activation functions that are ELU and CReLU have been used to investigate how many number of convolutional layers with a particular activation function provides with the best results on this dataset as the size of the dataset in this domain is always small. The proposed investigation can be further used in a novel solar prediction systems

Towards lightweight convolutional neural networks for object detection

We propose model with larger spatial size of feature maps and evaluate it on object detection task. With the goal to choose the best feature extraction network for our model we compare several popular lightweight networks. After that we conduct a set of experiments with channels reduction algorithms in order to accelerate execution. Our vehicle detection models are accurate, fast and therefore suit for embedded visual applications. With only 1.5 GFLOPs our best model gives 93.39 AP on validation subset of challenging DETRAC dataset. The smallest of our models is the first to achieve real-time inference speed on CPU with reasonable accuracy drop to 91.43 AP.Comment: Submitted to the International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S) in conjunction with the 14th IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS 2017

Meshed Up: Learnt Error Correction in 3D Reconstructions

Dense reconstructions often contain errors that prior work has so far minimised using high quality sensors and regularising the output. Nevertheless, errors still persist. This paper proposes a machine learning technique to identify errors in three dimensional (3D) meshes. Beyond simply identifying errors, our method quantifies both the magnitude and the direction of depth estimate errors when viewing the scene. This enables us to improve the reconstruction accuracy. We train a suitably deep network architecture with two 3D meshes: a high-quality laser reconstruction, and a lower quality stereo image reconstruction. The network predicts the amount of error in the lower quality reconstruction with respect to the high-quality one, having only view the former through its input. We evaluate our approach by correcting two-dimensional (2D) inverse-depth images extracted from the 3D model, and show that our method improves the quality of these depth reconstructions by up to a relative 10% RMSE.Comment: Accepted for the International Conference on Robotics and Automation (ICRA) 201

Probabilistic Image Colorization

We develop a probabilistic technique for colorizing grayscale natural images. In light of the intrinsic uncertainty of this task, the proposed probabilistic framework has numerous desirable properties. In particular, our model is able to produce multiple plausible and vivid colorizations for a given grayscale image and is one of the first colorization models to provide a proper stochastic sampling scheme. Moreover, our training procedure is supported by a rigorous theoretical framework that does not require any ad hoc heuristics and allows for efficient modeling and learning of the joint pixel color distribution. We demonstrate strong quantitative and qualitative experimental results on the CIFAR-10 dataset and the challenging ILSVRC 2012 dataset

Pengenalan Ekspresi Wajah dengan CNN dan Wavelet

Dengan berkembangnya teknologi di jaman modern ini diharapkan komputer juga mampu mengenali ekspresi wajah manusia. Hal itu dapat terwujud dengan kemajuan machine learning. Machine learning telah menjadi bagian dari kehidupan sehari-hari bagi banyak orang di seluruh dunia. Penemuan dan implementasi machine learning memungkinkan komputer untuk belajar dan memprediksi pola yang mungkin terjadi dan dapat digunakan untuk membantu manusia melakukan kegiatan sehari-hari. Salah satunya yaitu Convolutional Neural Network. Pada penelitian ini akan digunakan wavelet transform untuk membantu meningkatkan akurasi dari convolutional neural network dan mempercepat peningkatan akurasi. Wavelet berguna untuk melakukan compressing pada gambar sehingga lebih mudah untuk diolah. Gambar yang dihasilkan oleh wavelet terbagi menjadi 4 frekuensi yang berbeda-beda. Setiap gambar yang dihasilkan oleh wavelet diuji cobakan kedalam convolutional neural network. Berdasarkan hasil uji coba yang dilakukan, akurasi terbaik didapatkan dari dataset KDEF dengan menggunakan gambar wavelet berfrekuensi Low-Low (LL) dengan akurasi yang didapatkan sebesar 79%. Sedangkan hasil uji coba menggunakan dataset buatan sendiri didapatkan akurasi terbaik dengan menggunakan wavelet berfrekuensi Low-Low (LL) dengan akurasi yang didapatkan sebesar 36,925%

Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition

The audio-video based emotion recognition aims to classify a given video into basic emotions. In this paper, we describe our approaches in EmotiW 2019, which mainly explores emotion features and feature fusion strategies for audio and visual modality. For emotion features, we explore audio feature with both speech-spectrogram and Log Mel-spectrogram and evaluate several facial features with different CNN models and different emotion pretrained strategies. For fusion strategies, we explore intra-modal and cross-modal fusion methods, such as designing attention mechanisms to highlights important emotion feature, exploring feature concatenation and factorized bilinear pooling (FBP) for cross-modal feature fusion. With careful evaluation, we obtain 65.5% on the AFEW validation set and 62.48% on the test set and rank third in the challenge.Comment: Accepted by ACM ICMI'19 (2019 International Conference on Multimodal Interaction