From e2443998eeb5671e2469057d4715ac8facc2692a Mon Sep 17 00:00:00 2001 From: Woojun Jung <46880056+jungnerd@users.noreply.github.com> Date: Sat, 5 Oct 2024 14:06:17 +0900 Subject: [PATCH 01/11] Add `rnn-based-video-model.mdx` file --- .../rnn-based-video-model.mdx | 101 ++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 chapters/en/unit7/video-processing/rnn-based-video-model.mdx diff --git a/chapters/en/unit7/video-processing/rnn-based-video-model.mdx b/chapters/en/unit7/video-processing/rnn-based-video-model.mdx new file mode 100644 index 000000000..321ea57db --- /dev/null +++ b/chapters/en/unit7/video-processing/rnn-based-video-model.mdx @@ -0,0 +1,101 @@ +# Introduction[[introduction]] + +## Videos as Sequence Data[[video-as-sequence-data]] + +Videos are made up of a series of images called frames that are played one after another to create motion. Each frame captures spatial information—the objects and scenes in the image. When these frames are shown in sequence, they also provide temporal information—how things change and move over time. +Because of this combination of space and time, videos contain more complex information than single images. To analyze videos effectively, we need models that can understand both the spatial and temporal aspects. + +## The Role and Need for RNNs in Video Processing[[the-role-and-need-for-rnns-in-video-processing]] + +Convolutional Neural Networks (CNNs) are excellent at analyzing spatial features in images. +However, they aren't designed to handle sequences where temporal relationships matter. This is where Recurrent Neural Networks (RNNs) come in. +RNNs are specialized for processing sequential data because they have a "memory" that captures information from previous steps. This makes them well-suited for understanding how video frames relate to each other over time. + +## Understanding Spatio-Temporal Modeling[[understanding-spatiotemporal-modeling]] + +In video analysis, it's important to consider both spatial (space) and temporal (time) features together—this is called spatio-temporal modeling. Spatial modeling looks at what's in each frame, like objects or people, while temporal modeling looks at how these things change from frame to frame. +By combining these two, we can understand the full context of a video. Techniques like combining CNNs and RNNs or using special types of convolutions that capture both space and time are ways researchers achieve this. + +# RNN-Based Video Modeling Architectures[[rnn-based-video-modeling-architectures]] + +## Long-term Recurrent Convolutional Networks(LRCN)[[lrcn]] + + + +**Overview** +Long-term Recurrent Convolutional Networks (LRCN) are models introduced by researchers Donahue et al. in 2015. +They combine CNNs and Long Short-Term Memory networks (LSTMs), a type of RNN, to learn from both the spatial and temporal features in videos. +The CNN processes each frame to extract spatial features, and the LSTM takes these features in sequence to learn how they change over time. + +**Key Features** +- **Combining CNN and LSTM:** Spatial features from each frame are fed into the LSTM to model the temporal relationships. +- **Versatile Applications:** LRCNs have been used successfully in tasks like action recognition (identifying actions in videos) and video captioning (generating descriptions of videos). + +**Why It Matters** +LRCN was one of the first models to effectively handle both spatial and temporal aspects of video data. It paved the way for future research by showing that combining CNNs and RNNs can be powerful for video analysis. + +## Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting(ConvLSTM)[[convlstm]] + + + +**Overview** + +The Convolutional LSTM Network (ConvLSTM) was proposed by Shi et al. in 2015. It modifies the traditional LSTM by incorporating convolutional operations within the LSTM's structure. This means that instead of processing one-dimensional sequences, ConvLSTM can handle two-dimensional spatial data (like images) over time. + +**Key Features** +- **Spatial Structure Preservation:** By using convolutions, ConvLSTM maintains the spatial layout of the data while processing temporal sequences. +- **Effective for Spatiotemporal Prediction:** It's particularly useful for tasks that require predicting how spatial data changes over time, such as weather forecasting or video frame prediction. + +**Why It Matters** +ConvLSTM introduced a new way to process spatiotemporal data by integrating convolution directly into the LSTM architecture. This has been influential in fields that need to predict future states based on spatial and temporal patterns. + +## Unsupervised Learning of Video Representations using LSTMs[[unsupervised-lstms]] + +**Overview** +In 2015, Srivastava et al. introduced a method for learning video representations without labeled data, known as unsupervised learning. This paper utilizes a multi-layer LSTM model to learn video representations. The model consists of two main components: an Encoder LSTM and a Decoder LSTM. The Encoder maps video sequences of arbitrary length (in the time dimension) to a fixed-size representation. The Decoder then uses this representation to either reconstruct the input video sequence or predict the subsequent video sequence. + +**Key Features** +- **Unsupervised Learning:** The model doesn't require labeled data, making it easier to work with large amounts of video. + +**Why It Matters** +This approach showed that it's possible to learn useful video representations without the need for extensive labeling, which is time-consuming and expensive. It opened up new possibilities for video analysis and generation using unsupervised methods. + +## Describing Videos by Exploiting Temporal Structure[[temporal-structure]] + + + +**Overview** +In 2015, Yao et al. introduced attention mechanisms in video models, specifically for video captioning tasks. This approach leverages attention to selectively focus on important temporal and spatial features within the video, allowing the model to generate more accurate and contextually relevant descriptions. + +**Key Features** +- **Temporal and Spatial Attention:** The attention mechanism dynamically identifies the most relevant frames and regions in a video, ensuring that both local actions (e.g., specific movements) and global context (e.g., overall activity) are considered. +- **Enhanced Representation:** By focusing on significant features, the model combines local and global temporal structures, leading to improved video representations and more precise caption generation. + +**Why It Matters** +Incorporating attention mechanisms into video models has transformed how temporal data is processed. This method enhances the model’s capacity to handle the complex interactions in video sequences, making it an essential component in modern neural network architectures for video analysis and generation. + + +# Limitations of RNN-Based Models [[limitations]] +- **Challenges with Long-Term Dependencies** + + RNNs, including LSTMs, can struggle to maintain information over long sequences. This means they might "forget" important details from earlier frames when processing long videos. This limitation can affect the model's ability to understand the full context of a video. + +- **Computational Complexity and Processing Time** + + Because RNNs process data sequentially—one step at a time—they can be slow, especially with long sequences like videos. This sequential processing makes it difficult to take advantage of parallel computing resources, leading to longer training and inference times. + +- **Emergence of Alternative Models** + + Newer models like Transformers have been developed to address some of the limitations of RNNs. Transformers use attention mechanisms to handle sequences and can process data in parallel, making them faster and more effective at capturing long-term dependencies. + +# Conclusion[[conclusion]] + +RNN-based models have significantly advanced the field of video analysis by providing tools to handle temporal sequences effectively. Models like LRCN, ConvLSTM, and those incorporating attention mechanisms have demonstrated the potential of combining spatial and temporal processing. However, limitations such as difficulty with long sequences, computational inefficiency, and high data requirements highlight the need for continued innovation. + +Future research is likely to focus on overcoming these challenges, possibly by adopting newer architectures like Transformers, improving training efficiency, and enhancing model interpretability. These efforts aim to create models that are both powerful and practical for real-world video applications. + +### Reference +1. [Long-term Recurrent Convolutional Networks paper](https://arxiv.org/pdf/1411.4389) +2. [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting paper](https://proceedings.neurips.cc/paper_files/paper/2015/file/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf) +3. [Unsupervised Learning of Video Representations using LSTMs paper](https://arxiv.org/pdf/1502.04681) +4. [Describing Videos by Exploiting Temporal Structure paper](https://arxiv.org/pdf/1502.08029) \ No newline at end of file From 27beba62bb04f8d87cd052bb9c6dae4ed03f11c7 Mon Sep 17 00:00:00 2001 From: Woojun Jung <46880056+jungnerd@users.noreply.github.com> Date: Sat, 5 Oct 2024 14:12:32 +0900 Subject: [PATCH 02/11] Rename mdx file, add name in welcome.mdx, add _toctree --- chapters/en/_toctree.yml | 2 ++ chapters/en/unit0/welcome/welcome.mdx | 2 +- .../{rnn-based-video-model.mdx => rnn-based-video-models.mdx} | 0 3 files changed, 3 insertions(+), 1 deletion(-) rename chapters/en/unit7/video-processing/{rnn-based-video-model.mdx => rnn-based-video-models.mdx} (100%) diff --git a/chapters/en/_toctree.yml b/chapters/en/_toctree.yml index 8869b603f..2bfe6508d 100644 --- a/chapters/en/_toctree.yml +++ b/chapters/en/_toctree.yml @@ -126,6 +126,8 @@ local: "unit7/video-processing/video-processing-basics" - title: Overview of the previous SOTA models local: "unit7/video-processing/overview-of-previous-sota-models" + - title: RNN Based Video Model + local: "unit7/rnn-based-video-models" - title: Unit 8 - 3D Vision, Scene Rendering and Reconstruction sections: - title: Introduction diff --git a/chapters/en/unit0/welcome/welcome.mdx b/chapters/en/unit0/welcome/welcome.mdx index 1cfb65734..ef3c70e37 100644 --- a/chapters/en/unit0/welcome/welcome.mdx +++ b/chapters/en/unit0/welcome/welcome.mdx @@ -126,7 +126,7 @@ Our goal was to create a computer vision course that is beginner-friendly and th **Unit 7 - Video and Video Processing** - Reviewers: [Ameed Taylor](https://github.com/atayloraerospace), [Isabella Bicalho-Frazeto](https://github.com/bellabf) -- Writers: [Diwakar Basnet](https://github.com/DiwakarBasnet), [Chulhwa Han](https://github.com/cjfghk5697) +- Writers: [Diwakar Basnet](https://github.com/DiwakarBasnet), [Chulhwa Han](https://github.com/cjfghk5697), [Woojun Jung](https://github.com/jungnerd) **Unit 8 - 3D Vision, Scene Rendering, and Reconstruction** diff --git a/chapters/en/unit7/video-processing/rnn-based-video-model.mdx b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx similarity index 100% rename from chapters/en/unit7/video-processing/rnn-based-video-model.mdx rename to chapters/en/unit7/video-processing/rnn-based-video-models.mdx From bf0bac1d8e9c44f5e242ef76bac013de0a15dbe8 Mon Sep 17 00:00:00 2001 From: Woojun Jung <46880056+jungnerd@users.noreply.github.com> Date: Sat, 5 Oct 2024 14:15:13 +0900 Subject: [PATCH 03/11] fix mdx file --- .../en/unit7/video-processing/rnn-based-video-models.mdx | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx index 321ea57db..2d11526ae 100644 --- a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx +++ b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx @@ -20,7 +20,7 @@ By combining these two, we can understand the full context of a video. Technique ## Long-term Recurrent Convolutional Networks(LRCN)[[lrcn]] - + **Overview** Long-term Recurrent Convolutional Networks (LRCN) are models introduced by researchers Donahue et al. in 2015. @@ -36,7 +36,7 @@ LRCN was one of the first models to effectively handle both spatial and temporal ## Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting(ConvLSTM)[[convlstm]] - + **Overview** @@ -62,7 +62,7 @@ This approach showed that it's possible to learn useful video representations wi ## Describing Videos by Exploiting Temporal Structure[[temporal-structure]] - + **Overview** In 2015, Yao et al. introduced attention mechanisms in video models, specifically for video captioning tasks. This approach leverages attention to selectively focus on important temporal and spatial features within the video, allowing the model to generate more accurate and contextually relevant descriptions. @@ -94,7 +94,7 @@ RNN-based models have significantly advanced the field of video analysis by prov Future research is likely to focus on overcoming these challenges, possibly by adopting newer architectures like Transformers, improving training efficiency, and enhancing model interpretability. These efforts aim to create models that are both powerful and practical for real-world video applications. -### Reference +### Reference[[reference]] 1. [Long-term Recurrent Convolutional Networks paper](https://arxiv.org/pdf/1411.4389) 2. [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting paper](https://proceedings.neurips.cc/paper_files/paper/2015/file/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf) 3. [Unsupervised Learning of Video Representations using LSTMs paper](https://arxiv.org/pdf/1502.04681) From bb71ed3f8852caab4f8acca18bee60d85dd8bb22 Mon Sep 17 00:00:00 2001 From: Woojun Jung <46880056+jungnerd@users.noreply.github.com> Date: Sat, 5 Oct 2024 14:17:06 +0900 Subject: [PATCH 04/11] fix mdx file --- .../en/unit7/video-processing/rnn-based-video-models.mdx | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx index 2d11526ae..ceedb58cc 100644 --- a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx +++ b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx @@ -20,7 +20,7 @@ By combining these two, we can understand the full context of a video. Technique ## Long-term Recurrent Convolutional Networks(LRCN)[[lrcn]] - + **Overview** Long-term Recurrent Convolutional Networks (LRCN) are models introduced by researchers Donahue et al. in 2015. @@ -36,7 +36,7 @@ LRCN was one of the first models to effectively handle both spatial and temporal ## Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting(ConvLSTM)[[convlstm]] - + **Overview** @@ -62,7 +62,7 @@ This approach showed that it's possible to learn useful video representations wi ## Describing Videos by Exploiting Temporal Structure[[temporal-structure]] - + **Overview** In 2015, Yao et al. introduced attention mechanisms in video models, specifically for video captioning tasks. This approach leverages attention to selectively focus on important temporal and spatial features within the video, allowing the model to generate more accurate and contextually relevant descriptions. From 7294a8dbd5990329c9cdc9fa27cf2cb7bc5d6df5 Mon Sep 17 00:00:00 2001 From: Woojun Jung <46880056+jungnerd@users.noreply.github.com> Date: Tue, 8 Oct 2024 14:18:25 +0900 Subject: [PATCH 05/11] Apply suggestions from code review Co-authored-by: Jiwook Han <33192762+mreraser@users.noreply.github.com> --- .../video-processing/rnn-based-video-models.mdx | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx index ceedb58cc..0a602787c 100644 --- a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx +++ b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx @@ -20,7 +20,7 @@ By combining these two, we can understand the full context of a video. Technique ## Long-term Recurrent Convolutional Networks(LRCN)[[lrcn]] - +/* A image of the model architecture will be here */ **Overview** Long-term Recurrent Convolutional Networks (LRCN) are models introduced by researchers Donahue et al. in 2015. @@ -36,7 +36,7 @@ LRCN was one of the first models to effectively handle both spatial and temporal ## Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting(ConvLSTM)[[convlstm]] - +/* A image of the model architecture will be here */ **Overview** @@ -44,10 +44,10 @@ The Convolutional LSTM Network (ConvLSTM) was proposed by Shi et al. in 2015. It **Key Features** - **Spatial Structure Preservation:** By using convolutions, ConvLSTM maintains the spatial layout of the data while processing temporal sequences. -- **Effective for Spatiotemporal Prediction:** It's particularly useful for tasks that require predicting how spatial data changes over time, such as weather forecasting or video frame prediction. +- **Effective for Spatio-Temporal Prediction:** It's particularly useful for tasks that require predicting how spatial data changes over time, such as weather forecasting or video frame prediction. **Why It Matters** -ConvLSTM introduced a new way to process spatiotemporal data by integrating convolution directly into the LSTM architecture. This has been influential in fields that need to predict future states based on spatial and temporal patterns. +ConvLSTM introduced a new way to process spatio-temporal data by integrating convolution directly into the LSTM architecture. This has been influential in fields that need to predict future states based on spatial and temporal patterns. ## Unsupervised Learning of Video Representations using LSTMs[[unsupervised-lstms]] @@ -62,7 +62,7 @@ This approach showed that it's possible to learn useful video representations wi ## Describing Videos by Exploiting Temporal Structure[[temporal-structure]] - +/* A image of the model architecture will be here */ **Overview** In 2015, Yao et al. introduced attention mechanisms in video models, specifically for video captioning tasks. This approach leverages attention to selectively focus on important temporal and spatial features within the video, allowing the model to generate more accurate and contextually relevant descriptions. @@ -94,7 +94,7 @@ RNN-based models have significantly advanced the field of video analysis by prov Future research is likely to focus on overcoming these challenges, possibly by adopting newer architectures like Transformers, improving training efficiency, and enhancing model interpretability. These efforts aim to create models that are both powerful and practical for real-world video applications. -### Reference[[reference]] +### References[[references]] 1. [Long-term Recurrent Convolutional Networks paper](https://arxiv.org/pdf/1411.4389) 2. [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting paper](https://proceedings.neurips.cc/paper_files/paper/2015/file/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf) 3. [Unsupervised Learning of Video Representations using LSTMs paper](https://arxiv.org/pdf/1502.04681) From d804817f1da8572a275a88905a3e1e88ebaee1f2 Mon Sep 17 00:00:00 2001 From: Woojun Jung <46880056+jungnerd@users.noreply.github.com> Date: Tue, 8 Oct 2024 14:50:41 +0900 Subject: [PATCH 06/11] Fix _toctree.yml --- chapters/en/_toctree.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/chapters/en/_toctree.yml b/chapters/en/_toctree.yml index 2bfe6508d..4bb4bafe3 100644 --- a/chapters/en/_toctree.yml +++ b/chapters/en/_toctree.yml @@ -127,7 +127,7 @@ - title: Overview of the previous SOTA models local: "unit7/video-processing/overview-of-previous-sota-models" - title: RNN Based Video Model - local: "unit7/rnn-based-video-models" + local: "unit7/video-processing/rnn-based-video-models" - title: Unit 8 - 3D Vision, Scene Rendering and Reconstruction sections: - title: Introduction From 58c60aefbb70cb69975e4a0c5798692ebbd7ea10 Mon Sep 17 00:00:00 2001 From: Woojun Jung <46880056+jungnerd@users.noreply.github.com> Date: Tue, 8 Oct 2024 15:01:36 +0900 Subject: [PATCH 07/11] Add image files --- .../video-processing/rnn-based-video-models.mdx | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx index 0a602787c..c3f5ca735 100644 --- a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx +++ b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx @@ -6,7 +6,9 @@ Videos are made up of a series of images called frames that are played one after Because of this combination of space and time, videos contain more complex information than single images. To analyze videos effectively, we need models that can understand both the spatial and temporal aspects. ## The Role and Need for RNNs in Video Processing[[the-role-and-need-for-rnns-in-video-processing]] - +
+ RNN architecture +
Convolutional Neural Networks (CNNs) are excellent at analyzing spatial features in images. However, they aren't designed to handle sequences where temporal relationships matter. This is where Recurrent Neural Networks (RNNs) come in. RNNs are specialized for processing sequential data because they have a "memory" that captures information from previous steps. This makes them well-suited for understanding how video frames relate to each other over time. @@ -20,7 +22,9 @@ By combining these two, we can understand the full context of a video. Technique ## Long-term Recurrent Convolutional Networks(LRCN)[[lrcn]] -/* A image of the model architecture will be here */ +
+ LRCN model architecture +
**Overview** Long-term Recurrent Convolutional Networks (LRCN) are models introduced by researchers Donahue et al. in 2015. @@ -36,7 +40,9 @@ LRCN was one of the first models to effectively handle both spatial and temporal ## Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting(ConvLSTM)[[convlstm]] -/* A image of the model architecture will be here */ +
+ ConvLSTM model architecture +
**Overview** @@ -62,7 +68,9 @@ This approach showed that it's possible to learn useful video representations wi ## Describing Videos by Exploiting Temporal Structure[[temporal-structure]] -/* A image of the model architecture will be here */ +
+ +
**Overview** In 2015, Yao et al. introduced attention mechanisms in video models, specifically for video captioning tasks. This approach leverages attention to selectively focus on important temporal and spatial features within the video, allowing the model to generate more accurate and contextually relevant descriptions. From cef976db32845a087c9ab16614fca8eae98addb1 Mon Sep 17 00:00:00 2001 From: Woojun Jung <46880056+jungnerd@users.noreply.github.com> Date: Sun, 3 Nov 2024 13:38:45 +0900 Subject: [PATCH 08/11] Update chapters/en/unit7/video-processing/rnn-based-video-models.mdx Co-authored-by: Johannes Kolbe <2843485+johko@users.noreply.github.com> --- chapters/en/unit7/video-processing/rnn-based-video-models.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx index c3f5ca735..ac88bc3b0 100644 --- a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx +++ b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx @@ -1,4 +1,4 @@ -# Introduction[[introduction]] +# Introduction ## Videos as Sequence Data[[video-as-sequence-data]] From 457a445a4c96f0e3516fba7490b7e93c4e5c0ae4 Mon Sep 17 00:00:00 2001 From: Woojun Jung <46880056+jungnerd@users.noreply.github.com> Date: Sun, 3 Nov 2024 13:39:27 +0900 Subject: [PATCH 09/11] Update chapters/en/unit7/video-processing/rnn-based-video-models.mdx Co-authored-by: Johannes Kolbe <2843485+johko@users.noreply.github.com> --- chapters/en/unit7/video-processing/rnn-based-video-models.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx index ac88bc3b0..7adbbb7c0 100644 --- a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx +++ b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx @@ -2,7 +2,7 @@ ## Videos as Sequence Data[[video-as-sequence-data]] -Videos are made up of a series of images called frames that are played one after another to create motion. Each frame captures spatial information—the objects and scenes in the image. When these frames are shown in sequence, they also provide temporal information—how things change and move over time. +Videos are made up of a series of images called frames that are played one after another to create motion. Each frame captures spatial information — the objects and scenes in the image. When these frames are shown in sequence, they also provide temporal information — how things change and move over time. Because of this combination of space and time, videos contain more complex information than single images. To analyze videos effectively, we need models that can understand both the spatial and temporal aspects. ## The Role and Need for RNNs in Video Processing[[the-role-and-need-for-rnns-in-video-processing]] From 4bd1101f40fa446d5ec5c2f3ea450b4a555e26a3 Mon Sep 17 00:00:00 2001 From: Woojun Jung <46880056+jungnerd@users.noreply.github.com> Date: Tue, 5 Nov 2024 15:06:06 +0900 Subject: [PATCH 10/11] Apply suggestions from code review --- .../rnn-based-video-models.mdx | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx index 7adbbb7c0..d232b60b7 100644 --- a/chapters/en/unit7/video-processing/rnn-based-video-models.mdx +++ b/chapters/en/unit7/video-processing/rnn-based-video-models.mdx @@ -1,11 +1,11 @@ # Introduction -## Videos as Sequence Data[[video-as-sequence-data]] +## Videos as Sequence Data Videos are made up of a series of images called frames that are played one after another to create motion. Each frame captures spatial information — the objects and scenes in the image. When these frames are shown in sequence, they also provide temporal information — how things change and move over time. Because of this combination of space and time, videos contain more complex information than single images. To analyze videos effectively, we need models that can understand both the spatial and temporal aspects. -## The Role and Need for RNNs in Video Processing[[the-role-and-need-for-rnns-in-video-processing]] +## The Role and Need for RNNs in Video Processing
RNN architecture
@@ -13,14 +13,14 @@ Convolutional Neural Networks (CNNs) are excellent at analyzing spatial features However, they aren't designed to handle sequences where temporal relationships matter. This is where Recurrent Neural Networks (RNNs) come in. RNNs are specialized for processing sequential data because they have a "memory" that captures information from previous steps. This makes them well-suited for understanding how video frames relate to each other over time. -## Understanding Spatio-Temporal Modeling[[understanding-spatiotemporal-modeling]] +## Understanding Spatio-Temporal Modeling In video analysis, it's important to consider both spatial (space) and temporal (time) features together—this is called spatio-temporal modeling. Spatial modeling looks at what's in each frame, like objects or people, while temporal modeling looks at how these things change from frame to frame. By combining these two, we can understand the full context of a video. Techniques like combining CNNs and RNNs or using special types of convolutions that capture both space and time are ways researchers achieve this. -# RNN-Based Video Modeling Architectures[[rnn-based-video-modeling-architectures]] +# RNN-Based Video Modeling Architectures -## Long-term Recurrent Convolutional Networks(LRCN)[[lrcn]] +## Long-term Recurrent Convolutional Networks(LRCN)
LRCN model architecture @@ -38,7 +38,7 @@ The CNN processes each frame to extract spatial features, and the LSTM takes the **Why It Matters** LRCN was one of the first models to effectively handle both spatial and temporal aspects of video data. It paved the way for future research by showing that combining CNNs and RNNs can be powerful for video analysis. -## Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting(ConvLSTM)[[convlstm]] +## Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting(ConvLSTM)
ConvLSTM model architecture @@ -55,7 +55,7 @@ The Convolutional LSTM Network (ConvLSTM) was proposed by Shi et al. in 2015. It **Why It Matters** ConvLSTM introduced a new way to process spatio-temporal data by integrating convolution directly into the LSTM architecture. This has been influential in fields that need to predict future states based on spatial and temporal patterns. -## Unsupervised Learning of Video Representations using LSTMs[[unsupervised-lstms]] +## Unsupervised Learning of Video Representations using LSTMs **Overview** In 2015, Srivastava et al. introduced a method for learning video representations without labeled data, known as unsupervised learning. This paper utilizes a multi-layer LSTM model to learn video representations. The model consists of two main components: an Encoder LSTM and a Decoder LSTM. The Encoder maps video sequences of arbitrary length (in the time dimension) to a fixed-size representation. The Decoder then uses this representation to either reconstruct the input video sequence or predict the subsequent video sequence. @@ -66,7 +66,7 @@ In 2015, Srivastava et al. introduced a method for learning video representation **Why It Matters** This approach showed that it's possible to learn useful video representations without the need for extensive labeling, which is time-consuming and expensive. It opened up new possibilities for video analysis and generation using unsupervised methods. -## Describing Videos by Exploiting Temporal Structure[[temporal-structure]] +## Describing Videos by Exploiting Temporal Structure
@@ -83,7 +83,7 @@ In 2015, Yao et al. introduced attention mechanisms in video models, specificall Incorporating attention mechanisms into video models has transformed how temporal data is processed. This method enhances the model’s capacity to handle the complex interactions in video sequences, making it an essential component in modern neural network architectures for video analysis and generation. -# Limitations of RNN-Based Models [[limitations]] +# Limitations of RNN-Based Models - **Challenges with Long-Term Dependencies** RNNs, including LSTMs, can struggle to maintain information over long sequences. This means they might "forget" important details from earlier frames when processing long videos. This limitation can affect the model's ability to understand the full context of a video. @@ -96,13 +96,13 @@ Incorporating attention mechanisms into video models has transformed how tempora Newer models like Transformers have been developed to address some of the limitations of RNNs. Transformers use attention mechanisms to handle sequences and can process data in parallel, making them faster and more effective at capturing long-term dependencies. -# Conclusion[[conclusion]] +# Conclusion RNN-based models have significantly advanced the field of video analysis by providing tools to handle temporal sequences effectively. Models like LRCN, ConvLSTM, and those incorporating attention mechanisms have demonstrated the potential of combining spatial and temporal processing. However, limitations such as difficulty with long sequences, computational inefficiency, and high data requirements highlight the need for continued innovation. Future research is likely to focus on overcoming these challenges, possibly by adopting newer architectures like Transformers, improving training efficiency, and enhancing model interpretability. These efforts aim to create models that are both powerful and practical for real-world video applications. -### References[[references]] +### References 1. [Long-term Recurrent Convolutional Networks paper](https://arxiv.org/pdf/1411.4389) 2. [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting paper](https://proceedings.neurips.cc/paper_files/paper/2015/file/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf) 3. [Unsupervised Learning of Video Representations using LSTMs paper](https://arxiv.org/pdf/1502.04681) From 47f9d166a02db64d90511f1b3b82d8fa38dd52a4 Mon Sep 17 00:00:00 2001 From: Woojun Jung <46880056+jungnerd@users.noreply.github.com> Date: Fri, 15 Nov 2024 03:42:05 +0900 Subject: [PATCH 11/11] fix toctree.yml --- chapters/en/_toctree.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/chapters/en/_toctree.yml b/chapters/en/_toctree.yml index 6a4fe5b32..d80c9eb36 100644 --- a/chapters/en/_toctree.yml +++ b/chapters/en/_toctree.yml @@ -126,8 +126,6 @@ local: "unit7/video-processing/introduction-to-video" - title: Video Processing Basics local: "unit7/video-processing/video-processing-basics" - - title: Overview of the previous SOTA models - local: "unit7/video-processing/overview-of-previous-sota-models" - title: CNN Based Video Model local: "unit7/video-processing/cnn-based-video-model" - title: RNN Based Video Model