Semantic segmentation vs instance segmentation in Computer Vision - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Difference between semantic and instance segmentation

Which statement correctly describes the difference between semantic segmentation and instance segmentation?

A Semantic segmentation detects objects with bounding boxes, while instance segmentation labels pixels with classes.

B Instance segmentation only works on images with a single object, semantic segmentation works on multiple objects.

C Instance segmentation labels each pixel with a class, while semantic segmentation distinguishes different objects of the same class.

D Semantic segmentation labels each pixel with a class, while instance segmentation also distinguishes different objects of the same class.

💡 Hint

Think about whether the method separates individual objects or just labels the type of object.

Semantic segmentation assigns a class label to every pixel but does not separate different objects of the same class. Instance segmentation does both: it labels pixels and separates individual objects.

❓ Predict Output

intermediate

Output shape of semantic vs instance segmentation masks

Given an input image of size 256x256 pixels, what is the shape of the output mask for semantic segmentation and instance segmentation respectively?

A Semantic: (256, 256), Instance: (256, 256)

B Semantic: (256, 256, num_classes), Instance: (num_instances, 256, 256)

C Semantic: (num_instances, 256, 256), Instance: (256, 256, num_classes)

D Semantic: (256, 256, 3), Instance: (256, 256, 3)

💡 Hint

Semantic segmentation outputs a class label per pixel; instance segmentation outputs masks per object.

Semantic segmentation outputs a mask with one channel per class, so shape is (height, width, num_classes). Instance segmentation outputs a mask per detected object, so shape is (num_instances, height, width).

❓ Model Choice

advanced

Choosing a model for instance segmentation

You want to detect and separate multiple objects of the same class in images. Which model architecture is best suited for this task?

A Mask R-CNN

B ResNet for classification

C Fully Convolutional Network (FCN)

D U-Net

💡 Hint

Look for a model that can detect objects and segment them individually.

Mask R-CNN performs instance segmentation by detecting objects and generating masks for each instance. U-Net and FCN are mainly for semantic segmentation. ResNet is for classification.

❓ Metrics

advanced

Evaluating semantic vs instance segmentation

Which metric is commonly used to evaluate both semantic and instance segmentation performance?

A Mean Intersection over Union (mIoU)

B Mean Squared Error (MSE)

C BLEU score

D Accuracy of bounding box coordinates

💡 Hint

Think about a metric that compares predicted and true pixel areas.

Mean Intersection over Union (mIoU) measures overlap between predicted and true masks and is widely used for both semantic and instance segmentation. Bounding box accuracy is for detection, BLEU is for language, MSE is for regression.

🔧 Debug

expert

Debugging instance segmentation mask output

You trained an instance segmentation model but the output masks overlap heavily, merging different objects into one mask. What is the most likely cause?

A The input images are too small, causing poor resolution.

B The learning rate is too low, causing slow convergence.

C The model's non-maximum suppression (NMS) threshold is too high, causing overlapping masks to merge.

D The model uses semantic segmentation instead of instance segmentation.

💡 Hint

Consider how overlapping predictions are filtered in instance segmentation.

Non-maximum suppression (NMS) removes overlapping detections. If its threshold is too high, masks that should be separate merge. Learning rate and image size affect training quality but not mask merging. Using semantic segmentation would not produce instance masks.

Practice

1. What is the main difference between semantic segmentation and instance segmentation in computer vision?

easy

A. Semantic segmentation detects objects, instance segmentation only classifies images.

B. Semantic segmentation groups all objects of the same class together, while instance segmentation separates each object instance.

C. Instance segmentation groups all objects of the same class, semantic segmentation separates each object instance.

D. Semantic segmentation works only on black and white images, instance segmentation works on color images.

Solution

1. Step 1: Understand semantic segmentation

   Semantic segmentation labels every pixel by its class, grouping all objects of the same type together without distinguishing individual objects.

2. Step 2: Understand instance segmentation

   Instance segmentation labels pixels by class and also separates each object instance uniquely, so different objects of the same class get different labels.

3. Final Answer:

   Semantic segmentation groups all objects of the same class together, while instance segmentation separates each object instance. -> Option B

4. Quick Check:

   Semantic vs instance = group vs separate [OK]

Hint: Semantic groups classes; instance separates objects [OK]

Common Mistakes:

• Confusing instance segmentation with object detection
• Thinking semantic segmentation separates individual objects
• Assuming semantic segmentation only works on grayscale images

2. Which of the following is the correct way to describe the output of an instance segmentation model?

easy

A. Multiple masks, each representing a unique object instance with class labels.

B. A list of class names without pixel-level information.

C. A bounding box around the entire image.

D. A single mask labeling all pixels of the same class identically.

Solution

1. Step 1: Recall instance segmentation output

   Instance segmentation outputs multiple masks, each mask corresponds to a unique object instance and includes class labels.

2. Step 2: Compare options

   A single mask labeling all pixels of the same class identically describes semantic segmentation output. A list of class names lacks pixel-level information. A bounding box does not provide pixel-level masks. Multiple masks each representing a unique object instance with class labels is correct.

3. Final Answer:

   Multiple masks, each representing a unique object instance with class labels. -> Option A

4. Quick Check:

   Instance segmentation = multiple unique masks [OK]

Hint: Instance segmentation outputs multiple unique masks [OK]

Common Mistakes:

• Choosing single mask output for instance segmentation
• Confusing bounding boxes with masks
• Ignoring pixel-level labeling

3. What is the shape of the output mask from a semantic segmentation model if the input image is 256x256 pixels and there are 3 classes?

medium

A. (256, 256, 1) with binary mask

B. (256, 256, 3) with one-hot encoding per pixel

C. (3, 256, 256) with separate masks per class

D. (256, 256) with each pixel labeled by class index

Solution

1. Step 1: Understand semantic segmentation output format

   Semantic segmentation typically outputs a mask of shape (height, width) where each pixel is labeled with a class index, often obtained by applying argmax to the model's per-pixel class logits.

2. Step 2: Apply to given input

   For 256x256 image and 3 classes, output mask shape is (256, 256) with each pixel labeled by class index.

3. Final Answer:

   (256, 256) with each pixel labeled by class index -> Option D

4. Quick Check:

   Semantic output shape = H x W x class_index [OK]

Hint: Semantic masks use (H, W) class index shape [OK]

Common Mistakes:

• Confusing channel order or shape dimensions
• Assuming single channel mask for multiple classes
• Mixing instance segmentation mask shapes

4. You have a semantic segmentation model that outputs a mask with shape (256, 256), but you want to separate individual objects of the same class. What is the main issue with this output?

medium

A. The mask does not distinguish between different instances of the same class.

B. The mask shape is incorrect; it should be (256, 256, 3).

C. The model output is missing bounding boxes for objects.

D. The mask labels pixels with RGB values instead of class indices.

Solution

1. Step 1: Analyze semantic segmentation mask

   A (256, 256) mask labels each pixel by class but does not separate individual objects of the same class.

2. Step 2: Identify limitation for instance separation

   Since all objects of the same class share the same label, individual instances cannot be distinguished.

3. Final Answer:

   The mask does not distinguish between different instances of the same class. -> Option A

4. Quick Check:

   Semantic mask shape limits instance separation [OK]

Hint: Semantic masks don't separate object instances [OK]

Common Mistakes:

• Thinking shape alone solves instance separation
• Confusing bounding boxes with pixel masks
• Assuming RGB values represent classes

5. You want to build a system to count the number of cars in a parking lot image. Which segmentation approach is best and why?

hard

A. Semantic segmentation, because it labels all car pixels together for easy counting.

B. Semantic segmentation, because it is faster and does not require distinguishing objects.

C. Instance segmentation, because it separates each car as a unique object for accurate counting.

D. Neither, object detection is the only way to count cars.

Solution

1. Step 1: Understand the task requirement

   Counting cars requires distinguishing each car as a separate object instance.

2. Step 2: Choose appropriate segmentation type

   Instance segmentation separates each object uniquely, enabling accurate counting of individual cars.

3. Step 3: Evaluate other options

   Semantic segmentation groups all cars together, making counting difficult. Object detection can count but does not provide pixel-level masks.

4. Final Answer:

   Instance segmentation, because it separates each car as a unique object for accurate counting. -> Option C

5. Quick Check:

   Counting objects needs instance segmentation [OK]

Hint: Count objects? Use instance segmentation [OK]

Common Mistakes:

• Choosing semantic segmentation for counting objects
• Ignoring instance separation importance
• Assuming object detection provides pixel masks