MCQs

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Overall MCQs for Reference

What is semantic segmentation?
a. Classifying each pixel in an image
b. Detecting objects with bounding boxes
c. Generating text from images
d. Clustering images
Answer: ajoinmytutor+2
The main goal of semantic segmentation is to:
a. Generate image captions
b. Assign a class to every pixel
c. Detect only edges
d. Reduce image size
Answer: broboflow+2
Which neural network architecture is most commonly used for semantic segmentation tasks?
a. RNN
b. UNet
c. BERT
d. GAN
Answer: bfinalroundai+2
What is the output of a semantic segmentation model?
a. A list of bounding boxes
b. A segmentation mask
c. A class vector
d. A scalar
Answer: bcvat+1
Which domain uses semantic segmentation extensively?
a. Natural language processing
b. Medical imaging
c. Speech processing
d. Game development
Answer: bjoinmytutor+2
Pixel-wise classification in images is called:
a. Object detection
b. Semantic segmentation
c. Clustering
d. Image denoising
Answer: bcoursera+2
Loss functions used for semantic segmentation include:
a. Cross-Entropy Loss
b. Dice Loss
c. IoU Loss
d. All of the above
Answer: droboflow+1
In semantic segmentation, each pixel is assigned to:
a. An individual instance
b. A single class
c. Multiple classes
d. A bounding box
Answer: bfinalroundai+1
Which architecture modifies the encoder-decoder structure for segmentation?
a. UNet
b. ResNet
c. AlexNet
d. LeNet
Answer: av7labs+2
Fully Convolutional Network (FCN) is mainly used for:
a. Classification
b. Semantic segmentation
c. Image super-resolution
d. Denoising
Answer: bv7labs+2
What type of prediction is semantic segmentation?
a. Sparse prediction
b. Dense prediction
c. Sequence prediction
d. Regression prediction
Answer: bcoursera+2
In segmentation, what does a mask represent?
a. Color values
b. Pixel-level labels
c. Bounding boxes
d. Clusters
Answer: bcvat+1
Semantic segmentation is different from object detection because:
a. It uses less computation
b. It classifies every pixel
c. It classifies only objects
d. It ignores backgrounds
Answer: bfinalroundai+2
Which deep learning component is integral to segmentation models?
a. Pooling layers
b. Convolutional layers
c. Recurrent layers
d. Dropout layers
Answer: bv7labs+1
Segmentation in autonomous driving helps with:
a. Traffic signal detection
b. Road and obstacle delineation
c. Speed estimation
d. Tire pressure monitoring
Answer: bjoinmytutor+2
Which of the following is a key feature extracted for segmentation?
a. Image size
b. Texture and color
c. File format
d. Compression ratio
Answer: broboflow+1
Semantic segmentation is also used in:
a. Satellite imagery
b. Retail banking
c. Supply chain
d. Fraud detection
Answer: aencord+1
Instance segmentation differs from semantic in that it:
a. Assigns a class to pixels only
b. Differentiates individual objects
c. Uses clustering
d. Ignores boundaries
Answer: bcoursera+1
Panoptic segmentation combines:
a. Semantic and instance segmentation
b. Clustering and classification
c. Detection and captioning
d. Edge and texture extraction
Answer: av7labs+1
Which metric often evaluates segmentation accuracy?
a. RMSE
b. IoU (Intersection over Union)
c. PSNR
d. F-Score
Answer: bjoinmytutor+2
The backbone network used in segmentation often is:
a. LSTM
b. ResNet
c. Transformer
d. SVM
Answer: broboflow+1
Which is NOT a benefit of semantic segmentation?
a. Full scene understanding
b. Fine object localization
c. Every pixel labeled
d. Speed enhancement for training
Answer: dencord+2
Semantic segmentation produces which type of output?
a. 2D map of class labels
b. 1D vector
c. Scalar
d. Histogram
Answer: acoursera+1
Which of the following best improves segmentation performance?
a. Data augmentation
b. Training fewer epochs
c. Smaller image size
d. Removing convolution layers
Answer: av7labs
Which application is NOT semantic segmentation?
a. Tissue identification in pathology images
b. Lane marking detection
c. Object counting with bounding boxes
d. Land use in remote sensing
Answer: cencord+2
A key difference between FCN and UNet is:
a. Use of skip connections in UNet
b. No convolution in FCN
c. FCN is only for grayscale
d. UNet has no decoder
Answer: afinalroundai+1
Which step comes last in segmentation workflow?
a. Preprocessing
b. Feature extraction
c. Model inference
d. Post-processing
Answer: dcvat+1
Which field has gained from semantic segmentation for real-time applications?
a. Autonomous vehicles
b. Stock trading
c. Speech recognition
d. Text mining
Answer: afinalroundai+1
Atrous convolution is helpful in segmentation for:
a. Keeping output resolution high
b. Increasing color depth
c. Adding noise
d. Random cropping
Answer: aroboflow+1
Semantic segmentation models are trained on:
a. Labeled pixel data
b. Data without labels
c. Audio samples
d. Text documents
Answer: acvat+2
A limitation of semantic segmentation is:
a. Cannot separate instances of same class
b. Needs large text data
c. Only works for tabular data
d. Cannot process images
Answer: acoursera+1
The process of partitioning an image into segments by class is called:
a. Clustering
b. Semantic segmentation
c. Hashing
d. Quantization
Answer: bcvat+2
In segmentation, spatial context refers to:
a. Position plus surrounding pixels’ information
b. File size
c. Compression artifacts
d. Camera model
Answer: aroboflow+1
Which is NOT a challenge in semantic segmentation?
a. Class imbalance
b. Complex boundaries
c. Uniform pixel value
d. Varying object scale
Answer: cv7labs+1
What does upsampling do in segmentation networks?
a. Reduces spatial size
b. Restores resolution
c. Adds noise
d. Performs data augmentation
Answer: bfinalroundai+1
Segmentation masks are stored as:
a. Color images
b. Binary or multi-label matrices
c. Histogram plots
d. Audio files
Answer: bcvat+1
Which image format is NOT suitable for storing masks?
a. PNG
b. JPEG
c. TIFF
d. BMP
Answer: bv7labs
Early segmentation models used which approach?
a. Patch-wise processing
b. Spectral clustering
c. Fully-connected layers
d. Voice recognition
Answer: afinalroundai
Which paradigm shift improved segmentation?
a. Transition from MLPs to CNNs
b. Using more pooling layers
c. Replacing decoders
d. Switching to audio datasets
Answer: afinalroundai+1
CRF post-processing is applied to segmentation output for:
a. Smoothing class boundaries
b. Speeding up inference
c. Increasing training loss
d. Reducing color depth
Answer: av7labs
What’s a common dataset for segmentation benchmarking?
a. ImageNet
b. Cityscapes
c. SQuAD
d. LibriSpeech
Answer: bcoursera+1
PSPNet uses what to address multi-scale context?
a. Pyramid pooling modules
b. LSTM layers
c. Random projections
d. Single pooling layer
Answer: aroboflow
Which technique enhances small-object recognition in segmentation?
a. Higher resolution input
b. Lower learning rate
c. Shorter epochs
d. Larger batch size
Answer: av7labs
What is the advantage of skip connections?
a. Pass information directly from encoder to decoder
b. Discard features
c. Increase memory cost
d. Reduce accuracy
Answer: afinalroundai+1
Which segmentation model is especially used in medical imaging?
a. UNet
b. BERT
c. DeepSpeech
d. GPT
Answer: ajoinmytutor+1
Semantic segmentation handles occluded objects by:
a. Assigning pixels to visible class regions
b. Ignoring all occluded pixels
c. Guessing randomly
d. Cropping the image
Answer: aroboflow+1
Training time for segmentation increases with:
a. Image resolution
b. Label quality
c. Dropout rate
d. Number of colors
Answer: aroboflow+1
Semantic segmentation models are mainly evaluated based on:
a. Test accuracy
b. IoU and pixel accuracy
c. Model size
d. Batch normalization
Answer: bjoinmytutor+2
Which post-processing step is crucial for real-world deployment?
a. Removing false positives
b. Saving intermediate layers
c. Reducing model parameters
d. Increasing input channels
Answer: acvat+1
Modern segmentation relies on which type of neural networks?
a. Fully convolutional networks
b. Recursive neural networks
c. Decision trees
d. Perceptron models
Answer: acvat+2

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