Vision Deep Learning

The video tutorial introduces deep learning and its application in computer vision, focusing on convolutional neural networks (CNNs) and their ability to learn features from pixels. It demonstrates face detection using MTV CNN and MediaPipe, highlighting their capabilities and performance. The tutorial also covers various case studies and applications, such as facial recognition, hand tracking, and road detection, using pre-trained models.

What is the primary advantage of deep learning in computer vision?

• It can learn features directly from pixels.

• It requires manual feature specification.

• It is slower than traditional methods.

• It does not use neural networks.

Which of the following is NOT a package mentioned for pose detection?

• Mt

• CNS

• Media pipe

• TensorFlow

In a convolutional neural network, what is the purpose of pooling layers?

• To add more layers to the network

• To increase the size of the input data

• To reduce the dimensionality of the data

• To train the network faster

What is the final output of a CNN when recognizing a handwritten digit?

• A predicted number

• A pooling layer

• A dense layer

• A set of weights

Which method is used to download an image for face detection in the tutorial?

• Urllib request

• OpenCV

• TensorFlow

• Matplotlib

What is a limitation of the MTV CNN face detection method mentioned?

• It has low detection confidence.

• It requires a complete face in the image.

• It can only detect one face at a time.

• It is not compatible with video input.

What is a key feature of media pipe in face detection?

• It only detects key points on the face.

• It is slower than MTV CNN.

• It builds a complete face mesh.

• It does not work with video input.

Which of the following is NOT a case study mentioned in the tutorial?

• Weather prediction

• Road detection

• Hand tracking

• Facial recognition

What type of neural network is used for soil classification in the tutorial?

• Feedforward neural network

• Generative adversarial network

• Convolutional neural network

• Recurrent neural network

What is the purpose of using pre-trained models in the tutorial?

• To reduce the accuracy of predictions

• To make the model slower

• To increase the complexity of the model

• To avoid training from scratch