Odloˇ cili smo se, da bo konˇ cni model prejemal predhodno obdelane podatke iz VGG16 modela in bo za vhodne vrednosti prejel vektor znaˇ cilk dolˇ zine 4096.. Slike iz podatkovne

recurrent neural networks, RNN, convolutional neural networks, CNN, image captioning, LSTM, GRU, MS COCO, Torch, deep learning..

Table 4 shows that our results on MSVD are competitive among other methods although we do not use other strong features like C3D (p-RNN) and use fewer frames (80 vs.. We

The original objective of this thesis was to study how well images can be enhanced using automated neural network based enhancement methods. The goal was to pro- duce a similar

The image encoder transforms an image into an image feature map (in blue) that is accepted by the salient region detector that localizes regions in the image using a

input at each time step, the LSTM layer in it facilitates producing a different image feature. descriptor at each time

Our chosen flat model is pre-trained on MS COCO Captions using max-pooled DenseCap features, and embedding size of E = 1024; the hierarchical model is using the same input features

This is completely a Deep Learning project, which makes use of multiple Neural Networks like Convolutional Neural Network and Long Short Term Memory to detect objects

The proposed ap- proach is compared to state-of-the-art i-vector based acoustic systems on two different datasets: Google 5M LID corpus and NIST LRE 2009.. Results show how LID

Given a 3D scene model, where each 3D point is associated with the image features from which it was triangulated, localizing a new query image against the model is solved by

Neural networks, deep learning, convolutional neural networks, image recognition, Cifar-10, RMSPROP, normalized

Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. Deep convolutional neural networks for hyperspectral image

Plchot, et al., Automatic language identification using deep neural networks, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) IEEE. Dehak,

K eywords Deep learning · Convolutional Neural networks · COVID-19 · Coronavirus · radiology · CT scan · Medical image analysis · Automatic medical diagnosis · lung CT scan

Nonetheless, if this gap (the difference between the training and validation error) is too low, the network might not be properly trained (i.e. it is underfitted) due to a low

Classification with Deep Convolutional Neural Networks, NIPS, 2012..

In this work, we propose a multimodal Recurrent Neural Networks (m-RNN) model 2 to address both the task of generating novel sentences descriptions for images, and the task of image

Classi cation of Imbalanced Cloud Image Data Using Deep Neural Networks –Performance.. Improvement Through a Data