Deep Learning II Video Classification

Questions and Answers

Which technique is NOT a type of fusion method used in video classification?

Early Fusion

Deep Fusion (correct)

Late Fusion

Mid-level Fusion

2D CNNs can directly classify entire video clips without additional processing methods.

False

What neural network architecture is primarily used for fusing temporal information in video classification?

3D CNN

The primary purpose of applying convolutional neural networks (CNNs) to video classification is to classify each ______ in the video.

frame Signup and view all the answers

Match the following video classification techniques with their descriptions:

Early Fusion = Combining features from individual frames before classification Mid-level Fusion = Integrating features at an intermediate level of processing Late Fusion = Combining classification results from separate models for decision-making 3D CNN = Using three dimensions to process spatial and temporal information Signup and view all the answers

What is a challenge commonly faced in video processing?

High computational cost Signup and view all the answers

Name one advantage of using 3D CNNs over 2D CNNs for video classification.

Ability to capture both spatial and temporal features Signup and view all the answers

Which of the following architectures is specifically designed for video classification?

TimeSformer Signup and view all the answers

RNNs are primarily used for image classification tasks.

False Signup and view all the answers

What method does TimeSformer use to learn features for video classification?

Self-attention over space and time Signup and view all the answers

The architecture that facilitates the implementation of U-Net from scratch is ____.

PyTorch Signup and view all the answers

Match the video classification architectures with their descriptions:

TimeSformer = Convolution-free approach using self-attention ViViT = Video Vision Transformer Mask R-CNN = Object detection and segmentation U-Net = Image segmentation architecture Signup and view all the answers

Which challenge is often faced in video processing tasks?

Temporal action variation Signup and view all the answers

Deep learning architectures cannot be fused together for enhanced video analysis.

False Signup and view all the answers

What is the approximate number of frames per second for videos?

30 fps Signup and view all the answers

HD video (1920 x 1080) has a size of approximately 1.5 GB per minute.

False Signup and view all the answers

What is a common solution to overcome the challenges in processing videos?

Train on short clips Signup and view all the answers

The size of uncompressed video is calculated as T x ______ x H x W, where each pixel is 3 bytes.

3 Signup and view all the answers

Match the video resolutions with their respective file sizes per minute:

SD video (640 x 480) = ~ 10 GB per minute HD video (1920 x 1080) = ~ 1.5 GB per minute Signup and view all the answers

Which deep learning architecture is commonly applied to classify frames in video classification?

2D CNNs Signup and view all the answers

Identify a primary challenge in processing videos.

Huge computational cost Signup and view all the answers

What technique does RoIAlign use to compute exact values of input features?

Bilinear interpolation Signup and view all the answers

Mask R-CNN architecture has a separate branch for mask prediction.

True Signup and view all the answers

What does RoIPool do?

Quantizes a floating number RoI to the discrete granularity of the feature map. Signup and view all the answers

Mask R-CNN is based on the architecture of Faster R-CNN but adds a ______ branch for mask prediction.

convolution Signup and view all the answers

Match the following architectures with their functions:

ResNet = Feature extraction ResNeXt = Depth configurations (50 and 101 layers) FPN = Feature pyramid network Mask R-CNN = Object detection and segmentation Signup and view all the answers

Which of the following is one of the challenges in processing videos?

Capturing information across frames Signup and view all the answers

A video is represented as a 2D tensor.

False Signup and view all the answers

What is the main goal of Mask R-CNN?

To perform object detection and instance segmentation. Signup and view all the answers

The pixel-pixel mask in RoIAlign aims to preserve the explicit per-pixel spatial ______.

correspondence Signup and view all the answers

Study Notes

Video Classification Techniques

Classification of video frames can be achieved using 2D CNNs, where each frame is treated independently.
Fusion techniques integrate frame information:
- Early Fusion combines data at the input level.
- Mid-level Fusion processes features after initial CNN layers.
- Late Fusion merges predictions from individual classifiers.

3D Convolutional Neural Networks (CNNs)

3D CNNs process entire video clips by applying three-dimensional convolution and pooling, enabling temporal information fusion.
These networks are essential in capturing sequential dynamics within videos.

Video Data Characteristics

Videos typically consist of 30 frames per second (fps).
Uncompressed video sizes are substantial:
- Standard Definition (SD) at 640 x 480 resolution: ~1.5 GB/minute.
- High Definition (HD) at 1920 x 1080 resolution: ~10 GB/minute.
Video is represented as a tensor of shape T x 3 x H x W, where T is time, H is height, and W is width.

Challenges in Video Processing

High computational demands arise from processing large video sizes and frames.
Solutions include training on brief clips with lower fps and spatial resolutions to alleviate resource strain.

Recurrent Neural Networks (RNNs)

RNNs, along with GRUs and LSTMs, excel in sequential modeling tasks such as action recognition and video classification.

TimeSformer

A convolution-free model that uses self-attention mechanisms across space and time for video classification.
Uses a standard Transformer architecture to learn spatio-temporal features from video framed as patches.

ViViT: A Video Vision Transformer

Adopts a transformer-based approach specifically designed for video data classification tasks.

RoI Align in Object Detection

RoI Align is integral for maintaining pixel-level correspondence when extracting masks from Regions of Interest (RoIs).
Differentiates from RoIPool by using bilinear interpolation for precise feature value computation.
Incorporates multi-task loss combining classification, bounding box, and mask losses.

Mask R-CNN Architecture

Comprises two main components:
- Backbone architecture for feature extraction (e.g., ResNet, ResNeXt, Feature Pyramid Networks).
- Network Head includes object detection and segmentation capabilities, functioning similarly to Faster R-CNN with added convolution for mask prediction.

Results of Mask R-CNN

Demonstrated significant performance improvements on MS COCO test set using ResNet-101 architecture.

Definition of Video

Videos are sequences of images, represented as a 4D tensor, formatted as T x 3 x H x W or 3 x T x H x W.
The study of videos includes addressing challenges in capturing frame-to-frame information effectively.

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Description

This quiz focuses on video classification techniques using 2D CNNs. It covers the approach of classifying frames and introduces fusion techniques for improved accuracy. Ideal for students studying deep learning methods in computer vision.

Deep Learning II Video Classification

Questions and Answers

Which technique is NOT a type of fusion method used in video classification?

2D CNNs can directly classify entire video clips without additional processing methods.

What neural network architecture is primarily used for fusing temporal information in video classification?

The primary purpose of applying convolutional neural networks (CNNs) to video classification is to classify each ______ in the video.

Match the following video classification techniques with their descriptions:

What is a challenge commonly faced in video processing?

Name one advantage of using 3D CNNs over 2D CNNs for video classification.

Which of the following architectures is specifically designed for video classification?

RNNs are primarily used for image classification tasks.

What method does TimeSformer use to learn features for video classification?

The architecture that facilitates the implementation of U-Net from scratch is ____.

Match the video classification architectures with their descriptions:

Which challenge is often faced in video processing tasks?

Deep learning architectures cannot be fused together for enhanced video analysis.

What is the approximate number of frames per second for videos?

HD video (1920 x 1080) has a size of approximately 1.5 GB per minute.

What is a common solution to overcome the challenges in processing videos?

The size of uncompressed video is calculated as T x ______ x H x W, where each pixel is 3 bytes.

Match the video resolutions with their respective file sizes per minute:

Which deep learning architecture is commonly applied to classify frames in video classification?

Identify a primary challenge in processing videos.

What technique does RoIAlign use to compute exact values of input features?

Mask R-CNN architecture has a separate branch for mask prediction.

What does RoIPool do?

Mask R-CNN is based on the architecture of Faster R-CNN but adds a ______ branch for mask prediction.

Match the following architectures with their functions:

Which of the following is one of the challenges in processing videos?

A video is represented as a 2D tensor.

What is the main goal of Mask R-CNN?

The pixel-pixel mask in RoIAlign aims to preserve the explicit per-pixel spatial ______.

Study Notes

Video Classification Techniques

3D Convolutional Neural Networks (CNNs)

Video Data Characteristics

Challenges in Video Processing

Recurrent Neural Networks (RNNs)

TimeSformer

ViViT: A Video Vision Transformer

RoI Align in Object Detection

Mask R-CNN Architecture

Results of Mask R-CNN

Definition of Video

Studying That Suits You

Description

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