![]() Having co-located data from multiple sensor modes enhances the detection confidence, but the availability of training data in desired sensor mode is not always readily available, which slows down progress. The algorithms providing this capability rely on training data being available in corresponding sensor mode. Object detection is a central theme for many Artificial Intelligence (AI) applications such as autonomous vehicles, surveillance etc. #Deepfocus crop images codeCode will be made available at com/flauted/OpenNMT-py. Finally, we present results for a number of IN1K feature extractors and discuss the relationship between IN1K accuracy and video captioning performance. Moreover, our results validate that CNNs pretrained with weak supervision can effectively transfer to tasks other than classification. Whereas previous works use complicated architectures or multimodal features, we demonstrate state-of-the-art performance on the Microsoft Video Description (MSVD) dataset and competitive results on the Microsoft Research-Video to Text (MSR-VTT) dataset using only the frame-level features from the new CNNs and a basic Transformer as a sequence-to-sequence model. These CNNs are trained on billion-scale weakly supervised datasets constructed from Instagram image-hashtag pairs and then fine-tuned on IN1K. In this paper, we propose using Facebook's ResNeXt Weakly Supervised Learning (WSL) CNNs as fixed feature extractors for video captioning. The features are then fed into a sequence-to-sequence model to produce the text description output. Traditionally, the CNNs are pretrained on the ImageNet-1K (IN1K) classification task. Existing solutions tend to rely on extracting features from frames or sets of frames with pretrained and fixed Convolutional Neural Networks (CNNs). The video captioning problem consists of describing a short video clip with natural language. With the collection of sufficient training data, our deep learning focusing model provides a significantly faster alternative to conventional focusing methods. Furthermore, the rare cases where our algorithm does not find the focal plane can be detected, and a fine-focus algorithm can be applied to correct the result. The model was able to determine the in-focus position with high reliability, and was also significantly faster than conventional methods that rely on classical computer vision. The CNN model was tested on bare semiconductor sample using the projected shape of the F-stop. ![]() The ground truth focal plane was determined using a parabolic autofocus algorithm with the Tenengrad scoring metric. A training dataset was acquired from a semiconductor sample at different surface locations on the sample and at different distances from focus. The difference of these two images is processed through a regression CNN model, which was trained to learn a direct mapping between the amount of defocus aberration and the distance from the focal plane. As an alternative, we developed a deep learning model that predicts in one shot the distance offset to the focal plane from any initial position using an input of only two images taken a set distance apart. Conventional microscopy focusing methods perform a time consuming sweep through the Z-axis in order to estimate the focal plane. ![]()
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