Interview computer vision

Computer Vision Basics Interview Questions & Answers

1. What is Computer Vision?

   Answer: Computer Vision is a field of artificial intelligence that focuses on enabling machines to interpret and understand visual information from the world. It aims to replicate the human visual system by using digital images or videos as input to make decisions or perform tasks.

2. Explain the steps involved in typical Computer Vision tasks.

   Answer: The typical steps in a Computer Vision task are:

   • Image Acquisition: Obtaining images or video frames from cameras or other sources.

   • Preprocessing: This involves tasks like resizing, denoising, and normalizing the images to prepare them for further processing.

   • Feature Extraction: Identifying key characteristics or patterns in the images. This can include edges, corners, textures, etc.

   • Object Recognition or Detection: Identifying and localizing objects within the image.

   • Post-processing: Refining the results, which may include tasks like non-maximum suppression or filtering.

   • Interpretation: Making sense of the results in the context of the specific task.

3. What is the difference between Image Classification and Object Detection?

   Answer:

   • Image Classification is a task where the model predicts a single label or class for an entire image. It doesn’t provide information about the location of objects within the image.

   • Object Detection, on the other hand, not only identifies the objects in an image but also provides their precise location by drawing bounding boxes around them.

4. What is Convolution in Convolutional Neural Networks (CNNs)?

   Answer: Convolution is a mathematical operation that combines two functions to produce a third function. In the context of CNNs, convolution involves sliding a filter (also known as a kernel) over the input image to extract features like edges, corners, textures, etc. This operation is fundamental to CNNs as it allows them to automatically learn relevant features from the data.

5. What is the purpose of Pooling in CNNs?

   Answer: Pooling is used in CNNs to downsample the spatial dimensions of the feature maps while retaining the most important information. It helps reduce the computational complexity and the number of parameters in the network, making it more manageable. Common pooling techniques include max pooling and average pooling.

6. What is the purpose of Activation Functions in neural networks?

   Answer: Activation functions introduce non-linearity into the model, allowing it to learn and approximate complex, non-linear relationships in the data. Without activation functions, the entire network would behave like a linear model, which is not suitable for tasks like image recognition.

7. What is Transfer Learning in the context of Computer Vision?

   Answer: Transfer learning is a technique where a pre-trained neural network, typically on a large dataset, is used as a starting point for a new task. Instead of training a model from scratch, the existing knowledge from the pre-trained network is fine-tuned on a smaller dataset specific to the new task. This is particularly useful when the new task has limited data.

8. What are some popular deep learning frameworks used for Computer Vision?

   Answer: Common deep learning frameworks for Computer Vision include TensorFlow, PyTorch, Keras, and OpenCV.

Classical Computer Vision Interview Questions & Answers

1. What is the difference between Computer Vision and Image Processing?

   Answer:

   • Computer Vision focuses on enabling machines to interpret and understand visual information from the world. It aims to replicate the human visual system to make decisions or perform tasks.

   • Image Processing involves manipulating or enhancing images for a specific purpose, such as improving the quality, extracting features, or applying filters.

2. Explain the concept of Image Thresholding.

   Answer: Image Thresholding is a technique used to separate objects from the background in an image. It involves setting a threshold value, and pixels with intensity values above the threshold are classified as foreground (object), while those below are classified as background. It’s a crucial step in tasks like object segmentation.

3. What is the purpose of Edge Detection in Computer Vision?

   Answer: Edge Detection is a fundamental process in Computer Vision used to identify the edges or boundaries in an image. It’s crucial because edges often correspond to important features in an image, such as object boundaries or textures.

4. Explain the concept of Hough Transform.

   Answer: The Hough Transform is a technique used for detecting simple geometric shapes like lines, circles, or ellipses in an image. It converts points in an image to a parameter space, where the presence of a shape is detected as peaks in this space.

5. What is the Sobel Operator and what is its purpose?

   Answer: The Sobel Operator is a popular edge detection filter used to approximate the gradient of the image intensity function. It highlights regions of rapid intensity change, which typically correspond to edges.

6. Explain the concept of Image Histogram.

   Answer: An Image Histogram is a graphical representation of the distribution of pixel intensities in an image. It shows the number of pixels for each intensity level. Histograms are useful for tasks like contrast enhancement and thresholding.

7. What is Morphological Image Processing?

   Answer: Morphological Image Processing involves operations on the shape or structure of an image. It includes operations like dilation (to expand regions), erosion (to shrink regions), opening (erosion followed by dilation), and closing (dilation followed by erosion). These operations are often used in tasks like noise reduction and object extraction.

8. Explain the concept of Image Segmentation.

   Answer: Image Segmentation involves dividing an image into meaningful regions or segments based on the characteristics of the pixels, such as color, intensity, or texture. It’s used for tasks like object recognition and tracking.

9. What is Feature Matching in Computer Vision?

   Answer: Feature Matching is the process of identifying and matching distinctive features (like corners, keypoints, or edges) between different images. This is used in tasks like object recognition, image stitching, and 3D reconstruction.

10. Explain the concept of Scale-Invariant Feature Transform (SIFT).

    Answer: SIFT is a feature detection and description algorithm that identifies keypoints and extracts feature vectors which are invariant to scaling, rotation, and illumination changes. It’s widely used in tasks like object recognition and image stitching.

Convolution Neural Networks-Based Interview Questions & Answers

1. What is a Convolutional Neural Network (CNN)?

   Answer: A Convolutional Neural Network (CNN) is a type of deep learning neural network specifically designed for processing grid-like data, such as images and videos. It uses a series of convolutional layers to automatically and adaptively learn spatial hierarchies of features from the input data.

2. What is the purpose of Convolutional Layers in a CNN?

   Answer: Convolutional Layers apply filters (kernels) to the input data to detect specific features like edges, textures, or patterns. These filters slide across the input data to generate feature maps that capture hierarchical information.

3. Explain the concept of Pooling in CNNs.

   Answer: Pooling is a downsampling operation that reduces the spatial dimensions of the feature maps while retaining the most important information. Max pooling, for example, takes the maximum value from a region of the feature map, effectively reducing its size.

4. What is the purpose of Activation Functions in CNNs?

   Answer: Activation functions introduce non-linearity into the model, allowing it to learn and approximate complex, non-linear relationships in the data. Common activation functions in CNNs include ReLU (Rectified Linear Unit), Sigmoid, and Tanh.

5. Explain the concept of Fully Connected Layers in a CNN.

   Answer: Fully Connected Layers, also known as dense layers, connect every neuron from the previous layer to every neuron in the current layer. These layers are typically found at the end of a CNN and are responsible for making final decisions based on the extracted features.

6. What is the purpose of Stride in a Convolutional Layer?

   Answer: Stride in a Convolutional Layer determines how much the filter moves across the input data. A larger stride leads to a smaller output size, as the filter skips more pixels. This can help in reducing the spatial dimensions and computational complexity.

7. What is Dropout and why is it used in CNNs?

   Answer: Dropout is a regularization technique used in neural networks, including CNNs, to prevent overfitting. It randomly sets a fraction of the neurons to zero during training, effectively “dropping out” some information. This forces the network to be more robust and prevents it from relying too heavily on specific neurons.

8. What is Batch Normalization and why is it important in CNNs?

   Answer: Batch Normalization is a technique used to stabilize and accelerate the training of neural networks. It normalizes the activations of each layer in a mini-batch, reducing internal covariate shift. This leads to faster convergence and allows for higher learning rates.

9. What is Transfer Learning in the context of CNNs?

   Answer: Transfer Learning is a technique where a pre-trained CNN, typically on a large dataset, is used as a starting point for a new task. Instead of training a model from scratch, the existing knowledge from the pre-trained network is fine-tuned on a smaller dataset specific to the new task.

10. Explain the concept of Data Augmentation in CNNs.

    Answer: Data Augmentation involves applying various transformations to the training data, such as rotations, flips, zooms, and translations. This artificially increases the diversity of the training set, which can lead to a more robust and accurate model.

Object Detection Interview Questions & Answers

1. What is Object Detection?

   Answer: Object Detection is a computer vision task that involves identifying and localizing objects within an image or a video stream. It differs from image classification by not only classifying the objects but also drawing bounding boxes around them.

2. What are some popular techniques for Object Detection?

   Answer: Some popular techniques for Object Detection include:

   • R-CNN (Region-based Convolutional Neural Networks)
   • Fast R-CNN
   • Faster R-CNN
   • YOLO (You Only Look Once)
   • SSD (Single Shot MultiBox Detector)
   • Mask R-CNN (for instance segmentation)

3. Explain how R-CNN works.

   Answer: R-CNN is a multi-step process:

   • It generates region proposals using a selective search algorithm.
   • Each proposal is warped to a fixed size and passed through a pre-trained CNN to extract features.
   • These features are then fed into a set of SVM classifiers to determine the presence of different object classes.
   • Finally, bounding box regression is applied to refine the locations.

4. What are the advantages of Faster R-CNN over R-CNN?

   Answer: Faster R-CNN is an improvement over R-CNN in terms of speed and efficiency. It introduces the Region Proposal Network (RPN), which shares the computations for generating region proposals with the rest of the network. This makes the process end-to-end trainable, resulting in significantly faster inference times.

5. Explain how YOLO (You Only Look Once) works.

   Answer: YOLO divides the input image into a grid and predicts bounding boxes and class probabilities directly from the grid cells. It predicts bounding box coordinates and class probabilities simultaneously using a single neural network. This makes YOLO extremely fast, as it only requires one forward pass through the network.

6. What is Non-Maximum Suppression (NMS) in Object Detection?

   Answer: Non-Maximum Suppression is a post-processing technique used to remove duplicate or overlapping bounding boxes generated by the object detection model. It keeps the bounding box with the highest confidence score while suppressing others that have significant overlap with it.

7. What is Anchor Box in Object Detection?

   Answer: Anchor boxes are a set of predefined bounding boxes with varying sizes and aspect ratios. They are used in algorithms like YOLO and SSD to predict bounding boxes of different scales and shapes for objects in the image.

8. Explain the concept of Intersection over Union (IoU) in Object Detection.

   Answer: IoU is a metric used to measure the overlap between two bounding boxes. It’s calculated by dividing the area of overlap by the area of union between the two bounding boxes. IoU is crucial for tasks like Non-Maximum Suppression.

9. What are some challenges in Object Detection?

   Answer: Challenges in Object Detection include:

   • Scale Variation: Objects may appear at different scales.
   • Occlusion: Objects may be partially or fully occluded by other objects.
   • Cluttered Background: Complex backgrounds can make object detection more challenging.
   • Class Imbalance: Some classes may be rare in the dataset, leading to imbalanced training data.

10. What are some practical applications of Object Detection?

    Answer: Object Detection has various applications including:

    • Autonomous Vehicles: Detecting pedestrians, vehicles, and traffic signs.
    • Surveillance Systems: Identifying and tracking objects or persons of interest.
    • Medical Imaging: Locating and analyzing specific features in medical images.
    • Retail: Shelf monitoring, product recognition, and inventory management.

Image Segmentation Interview Questions & Answers

1. What is Image Segmentation?

   Answer: Image Segmentation is a computer vision task that involves dividing an image into distinct, meaningful regions or segments based on certain criteria, such as color, intensity, texture, or other features. Each segment represents a region of similar characteristics.

2. What are the different types of Image Segmentation?

   Answer: There are three main types of Image Segmentation:

   • Semantic Segmentation: Assigns a label to every pixel in an image to represent the class of the object or region it belongs to.

   • Instance Segmentation: Identifies individual objects or instances in an image and assigns a unique label to each one.

   • Panoptic Segmentation: Combines both semantic and instance segmentation, providing a comprehensive understanding of the image by labeling all pixels with object classes and instance IDs.

3. What is the difference between Semantic Segmentation and Instance Segmentation?

   Answer:

   • Semantic Segmentation assigns a label to every pixel in an image based on the category it belongs to (e.g., road, sky, person), without distinguishing between individual instances of the same class.

   • Instance Segmentation goes further by not only assigning a label to each pixel, but also differentiating between different instances of the same class (e.g., distinguishing between different people in an image).

4. Explain the concept of Convolutional Neural Networks (CNNs) in Image Segmentation.

   Answer: CNNs are used in Image Segmentation for their ability to automatically learn hierarchical features from the data. In Segmentation tasks, CNNs typically have an encoder-decoder architecture, where the encoder extracts features from the input image and the decoder generates a segmentation map with the same spatial dimensions.

5. What are some popular architectures used for Image Segmentation?

   Answer:

   • U-Net: Known for its symmetrical encoder-decoder structure, widely used for biomedical image segmentation.
   • Mask R-CNN: Combines object detection and instance segmentation, popular for precise object delineation.
   • FCN (Fully Convolutional Network): Converts any pre-trained classification network into a segmentation network.
   • DeepLab: Utilizes atrous convolutions to capture multi-scale context.

6. Explain the concept of Atrous (Dilated) Convolution in Image Segmentation.

   Answer: Atrous Convolution allows for an increased receptive field without increasing the number of parameters. It introduces gaps or dilations between the weights in the kernel, effectively increasing the stride of the convolution operation. This is particularly useful in capturing features at different scales.

7. What are some challenges in Image Segmentation?

   Answer: Challenges in Image Segmentation include:

   • Boundary Ambiguity: Determining precise object boundaries can be difficult, especially in regions with gradual transitions.
   • Class Imbalance: Some classes may be underrepresented, leading to imbalanced training data.
   • Variability in Object Appearance: Objects of the same class can have significant variation in appearance, making it challenging to generalize.

8. What are some practical applications of Image Segmentation?

   Answer: Image Segmentation has various applications, including:

   • Medical Imaging: Identifying and analyzing specific structures or anomalies in medical images.
   • Autonomous Vehicles: Segmenting objects in the environment for navigation and object detection.
   • Satellite Imagery: Land cover classification, urban planning, and environmental monitoring.
   • Biometrics: Face or fingerprint segmentation for identification.

Practical Computer Vision Interview Questions & Answers

1. Can you explain a real-world application where Object Detection is crucial?

   Answer: One practical application is in autonomous vehicles. Object Detection helps the vehicle identify and localize various objects in its surroundings, such as pedestrians, vehicles, traffic signs, and obstacles. This information is crucial for making decisions about navigation, collision avoidance, and overall safety.

2. How would you approach building a system for detecting defects in manufactured products using Computer Vision?

   Answer:

   • Data Collection: Gather a diverse dataset of images containing both defect-free and defective products.

   • Data Preprocessing: Normalize, resize, and clean the images. Annotate the images to mark the location and type of defects.

   • Model Selection: Choose a suitable architecture like a CNN. Consider transfer learning if a pre-trained model is available.

   • Training and Validation: Split the data into training and validation sets. Train the model on the training set, validate it on the validation set, and fine-tune as needed.

   • Testing and Deployment: Evaluate the model on a separate test set. Deploy it in the manufacturing environment, integrating it with the production line.

3. In a scenario where you have to detect and recognize multiple types of fruits in an image, how would you go about it?

   Answer:

   • Dataset Preparation: Gather a dataset with images of various fruits, each labeled with the corresponding fruit type.

   • Data Augmentation: Apply techniques like rotation, flipping, and scaling to increase the diversity of the dataset.

   • Model Selection: Use a CNN architecture for feature extraction. Consider approaches like YOLO or SSD for object detection.

   • Training and Evaluation: Train the model on the dataset and evaluate its performance using metrics like precision, recall, and F1-score.

   • Post-Processing: Apply Non-Maximum Suppression to remove duplicate detections.

   • Testing and Deployment: Test the model on new images, and if it performs well, deploy it for practical use.

4. How would you build a system to recognize handwritten digits in a mobile application?

   Answer:

   • Data Collection: Use a dataset like MNIST that contains images of handwritten digits (0-9).

   • Model Selection: Choose a CNN architecture, as they excel at image recognition tasks.

   • Training and Validation: Split the data into training and validation sets. Train the model and fine-tune it using backpropagation.

   • Integration with Mobile App: Use a framework like TensorFlow Lite or Core ML to convert the model to a format suitable for mobile deployment.

   • User Interface: Design a user-friendly interface for capturing or uploading images of handwritten digits within the mobile app.

   • Inference and Display: Implement code to process the image through the model and display the recognized digit.

5. How would you handle the issue of class imbalance in an Image Segmentation project?

   Answer:

   • Data Augmentation: Apply data augmentation techniques to artificially increase the number of training samples for the underrepresented class.

   • Weighted Loss Function: Assign higher weights to the loss associated with the minority class during training to give it more importance.

   • Oversampling/Undersampling: Either duplicate samples from the minority class (oversampling) or remove samples from the majority class (undersampling) to balance the classes.

   • Use of Generative Models: Techniques like Generative Adversarial Networks (GANs) can be used to generate synthetic samples for the underrepresented class.

6. What considerations should be taken into account when deploying a Computer Vision model on edge devices with limited computational resources?

   Answer:

   • Model Size: Choose a lightweight model architecture with fewer parameters to reduce memory and computation requirements.

   • Quantization: Convert the model to a lower precision format (e.g., INT8) to reduce memory and computation needs.

   • Hardware Acceleration: Utilize specialized hardware like GPUs, TPUs, or dedicated inference accelerators if available.

   • Optimization Techniques: Apply techniques like model pruning, weight sharing, and quantization-aware training to further optimize the model.

   • Model Updates: Consider strategies for remote model updates to improve performance or adapt to new conditions.

Advanced Computer Vision Interview Questions & Answers

1. Explain the concept of Generative Adversarial Networks (GANs) and how they can be used in Computer Vision.

   Answer: GANs are a type of generative model consisting of two neural networks: a generator and a discriminator. The generator aims to generate data that is indistinguishable from real data, while the discriminator tries to differentiate between real and generated data. Through adversarial training, GANs learn to generate highly realistic images. In Computer Vision, GANs are used for tasks like image synthesis, style transfer, and super-resolution.

2. What is Transfer Learning and how can it be applied to advanced Computer Vision tasks?

   Answer: Transfer Learning is a technique where a pre-trained neural network is used as a starting point for a new task. In advanced Computer Vision tasks, where large datasets may not be readily available, transfer learning is invaluable. By fine-tuning a pre-trained model on a specific task, it can learn to recognize complex features related to that task, saving significant time and resources.

3. Explain the concept of Attention Mechanisms in Computer Vision.

   Answer: Attention Mechanisms allow a model to focus on specific parts of an input while processing it. In the context of Computer Vision, attention mechanisms enable the model to selectively weigh different regions of an image, enhancing its capability to attend to relevant features. This is particularly useful for tasks like object detection in cluttered scenes.

4. What is Visual Question Answering (VQA) and how can it be approached in advanced Computer Vision?

   Answer: VQA is a task where the model is given an image and a natural language question about the image, and it must generate a relevant answer. In advanced Computer Vision, this can be tackled by combining Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) or transformers for language processing. The image features and the question are fused together to generate the answer.

5. Explain the concept of 3D Convolutional Neural Networks and their applications.

   Answer: 3D CNNs extend the concept of 2D convolutions to 3D, allowing them to process spatiotemporal information in video data. They have applications in tasks like action recognition, video classification, and medical imaging (e.g., 3D medical image analysis or video-based surgery assistance).

6. What is Instance Segmentation and how does it differ from Semantic Segmentation?

   Answer: Instance Segmentation aims to identify individual objects in an image and assign each a unique label. It goes a step further than Semantic Segmentation, which assigns a label to each pixel but does not differentiate between different instances of the same class. Instance Segmentation is used in scenarios where precise object delineation is necessary, such as in robotics and medical imaging.

7. Explain the concept of One-shot Learning and how it can be applied in Computer Vision.

   Answer: One-shot Learning involves training a model to recognize new classes with very limited examples (even just one example per class). This is crucial in scenarios where obtaining a large dataset for each class is not feasible. Techniques like Siamese Networks, which learn to differentiate between pairs of images, or Meta-Learning, which trains models to quickly adapt to new tasks, are used in one-shot learning approaches in Computer Vision.

8. What are some challenges in advanced Computer Vision tasks, particularly in tasks involving real-world applications?

   Answer: Challenges in advanced Computer Vision tasks include:

   • Robustness to Variability: Real-world scenarios often have high variability in lighting, background, and object appearance.
   • Limited Data: Gathering large, diverse datasets for specific advanced tasks can be difficult.
   • Real-time Processing: Many applications require real-time or near-real-time processing, which demands efficient algorithms and hardware.
   • Ethical and Privacy Concerns: Deploying Computer Vision systems in sensitive contexts may raise ethical issues related to privacy and bias.

Image Generation-Based Interview Questions & Answers

1. What is Image Generation?

   Answer: Image Generation is a task in computer vision where a model generates new images that are not part of the original dataset. This is typically done by training a generative model on a dataset and using it to create novel, realistic images.

2. Explain the concept of Generative Adversarial Networks (GANs) in the context of Image Generation.

   Answer: GANs are a type of generative model consisting of two neural networks: a generator and a discriminator. The generator aims to generate data that is indistinguishable from real data, while the discriminator tries to differentiate between real and generated data. Through adversarial training, GANs learn to generate highly realistic images. They have been widely used for image generation tasks.

3. What are some challenges in training GANs for image generation?

   Answer:

   • Mode Collapse: GANs can sometimes generate limited varieties of images, known as mode collapse.
   • Training Instability: Finding the right balance between the generator and discriminator can be challenging.
   • Evaluation of Results: Determining the quality and diversity of generated images can be subjective and challenging to measure quantitatively.

4. Explain the concept of Variational Autoencoders (VAEs) in the context of Image Generation.

   Answer: VAEs are generative models that combine elements of both autoencoders and generative models. They aim to learn a low-dimensional representation of data (latent space) and generate new samples from this space. In the context of image generation, VAEs can be used to generate new images by sampling from the learned latent space.

5. What is the difference between GANs and VAEs in terms of their approach to image generation?

   Answer:

   • GANs (Generative Adversarial Networks): GANs generate images by training a generator to create realistic-looking samples, while a discriminator tries to differentiate between real and generated images. They focus on producing high-quality, realistic images but do not provide explicit control over the generated samples.

   • VAEs (Variational Autoencoders): VAEs learn a probabilistic mapping between the data and a latent space. They focus on learning a continuous, probabilistic representation of the data. While they may not produce images of the same quality as GANs, they offer better control over the generated samples.

6. Explain the concept of StyleGAN in image generation.

   Answer: StyleGAN is a specific type of GAN architecture designed for high-quality image synthesis. It introduces a style-based generator that separates the content and the style of an image. This allows for more fine-grained control over the generated images, enabling the manipulation of features like pose, expression, and more.

7. How can conditional GANs be used in image generation tasks?

   Answer: Conditional GANs (cGANs) allow for the generation of images conditioned on specific attributes or labels. By providing additional information during the training process, such as class labels or other attributes, cGANs can be used to generate images with desired characteristics, like generating images of specific objects or in specific styles.

8. What are some practical applications of Image Generation?

   Answer: Image Generation has various applications, including:

   • Data Augmentation: Generating additional training data to improve the performance of machine learning models.
   • Super-Resolution: Generating high-resolution images from lower-resolution inputs.
   • Artistic Style Transfer: Creating images with the artistic style of another image.
   • Face Aging and De-aging: Simulating the aging or de-aging of faces in images.

Ref

Link 1

Internet

Hết.

• Previous
  Tổng quan về simulation transmission data via satellite
• Next
  Interview: apply AI into satellite network