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Questions and Answers
Which technique is NOT a type of fusion method used in video classification?
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.
2D CNNs can directly classify entire video clips without additional processing methods.
False (B)
What neural network architecture is primarily used for fusing temporal information in video classification?
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.
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:
Match the following video classification techniques with their descriptions:
What is a challenge commonly faced in video processing?
What is a challenge commonly faced in video processing?
Name one advantage of using 3D CNNs over 2D CNNs for video classification.
Name one advantage of using 3D CNNs over 2D CNNs for video classification.
Which of the following architectures is specifically designed for video classification?
Which of the following architectures is specifically designed for video classification?
RNNs are primarily used for image classification tasks.
RNNs are primarily used for image classification tasks.
What method does TimeSformer use to learn features for video classification?
What method does TimeSformer use to learn features for video classification?
The architecture that facilitates the implementation of U-Net from scratch is ____.
The architecture that facilitates the implementation of U-Net from scratch is ____.
Match the video classification architectures with their descriptions:
Match the video classification architectures with their descriptions:
Which challenge is often faced in video processing tasks?
Which challenge is often faced in video processing tasks?
Deep learning architectures cannot be fused together for enhanced video analysis.
Deep learning architectures cannot be fused together for enhanced video analysis.
What is the approximate number of frames per second for videos?
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.
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?
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.
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:
Match the video resolutions with their respective file sizes per minute:
Which deep learning architecture is commonly applied to classify frames in video classification?
Which deep learning architecture is commonly applied to classify frames in video classification?
Identify a primary challenge in processing videos.
Identify a primary challenge in processing videos.
What technique does RoIAlign use to compute exact values of input features?
What technique does RoIAlign use to compute exact values of input features?
Mask R-CNN architecture has a separate branch for mask prediction.
Mask R-CNN architecture has a separate branch for mask prediction.
What does RoIPool do?
What does RoIPool do?
Mask R-CNN is based on the architecture of Faster R-CNN but adds a ______ branch for mask prediction.
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:
Match the following architectures with their functions:
Which of the following is one of the challenges in processing videos?
Which of the following is one of the challenges in processing videos?
A video is represented as a 2D tensor.
A video is represented as a 2D tensor.
What is the main goal of Mask R-CNN?
What is the main goal of Mask R-CNN?
The pixel-pixel mask in RoIAlign aims to preserve the explicit per-pixel spatial ______.
The pixel-pixel mask in RoIAlign aims to preserve the explicit per-pixel spatial ______.
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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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