by jsendak | May 5, 2024 | AI
Action recognition has become one of the popular research topics in computer vision. There are various methods based on Convolutional Networks and self-attention mechanisms as Transformers to…
Action recognition is a rapidly growing field in computer vision research, with a multitude of methods being developed to accurately identify and understand human actions. Two prominent approaches that have gained significant attention are Convolutional Networks and self-attention mechanisms, such as Transformers. These methods have proven to be highly effective in recognizing and analyzing different actions, leading to advancements in areas like video surveillance, sports analysis, and human-computer interaction. In this article, we will delve into the core themes of action recognition, exploring the use of Convolutional Networks and self-attention mechanisms, and their impact on computer vision research.
Innovative Solutions for Action Recognition in Computer Vision
Action recognition has emerged as a captivating field of research in computer vision, with the potential to revolutionize industries such as surveillance, sports analysis, and human-computer interaction. The ability to automatically understand and interpret human actions can unlock tremendous possibilities, but it also presents numerous challenges.
Traditionally, action recognition methods have mainly relied on Convolutional Networks (ConvNets) to analyze spatiotemporal features. ConvNets excel at capturing meaningful patterns and correlations in image sequences, but they may struggle with handling long-range dependencies and subtle temporal dynamics.
However, recent advancements have introduced an innovative solution to this problem by incorporating self-attention mechanisms, popularly known as Transformers, into action recognition models. Transformers have proven effective in various natural language processing tasks by modeling long-range dependencies efficiently. By adapting this concept to computer vision, researchers have achieved remarkable performance improvements.
Understanding Self-Attention Mechanisms
In a nutshell, self-attention mechanisms learn to weigh different parts of a sequence when making predictions for each element. This allows the model to focus more on relevant segments while effectively capturing long-range dependencies. By considering these dependencies, the model gains a holistic understanding of the action being performed.
Transformers achieve this through a multi-head attention mechanism, where multiple attention weights are learned simultaneously. Each head focuses on different aspects of the input sequence, allowing the model to capture both local and global information effectively.
Enhancing Action Recognition using Transformers
When applying self-attention mechanisms to action recognition tasks, a natural approach is to utilize 3D convolutions to capture spatiotemporal features alongside the self-attention modules. This combination creates a powerful architecture capable of modeling both local and global dynamics.
Moreover, transformers enable the integration of external contextual information, such as object detection or semantic segmentation, into the action recognition pipeline. By incorporating these additional cues, the model can better understand actions in complex scenes, where context plays a vital role in disambiguating similar actions.
Addressing Long-Term Dependencies and Temporal Dynamics
Long-term dependencies and subtle temporal dynamics can be particularly challenging for action recognition models. To mitigate this issue, innovative solutions such as TimeSformer have been proposed. TimeSformer replaces the traditional 3D convolutional layers with a 2D backbone and applies self-attention mechanisms directly on the temporal dimension. This modification reduces computational complexity without compromising performance.
Additionally, techniques like transformer-based temporal fusion and transformer memory networks have been introduced to address temporal dynamics efficiently. These approaches leverage the power of attention mechanisms to aggregate relevant temporal information and capture the fine-grained dynamics within an action sequence.
Conclusion
Action recognition in computer vision has seen significant progress due to the integration of self-attention mechanisms inspired by Transformers. By combining the strengths of ConvNets and attention models, researchers have developed innovative solutions capable of capturing long-range dependencies, modeling global dynamics, and leveraging external contextual information.
The future of action recognition holds immense potential, with evolving architectures and algorithms continuing to push the boundaries of what computers can perceive and understand. By embracing these innovative ideas and solutions, we can expect action recognition to revolutionize various industries and contribute to the advancement of human-computer interaction.
improve action recognition. Convolutional Neural Networks (CNNs) have been widely used in computer vision tasks, including action recognition, due to their ability to capture spatial features effectively. These networks operate by applying filters across the input data to detect local patterns and gradually learn more complex representations.
However, one limitation of CNNs is that they do not explicitly model temporal dependencies in videos, which are crucial for understanding actions. This is where self-attention mechanisms, such as Transformers, have shown promise. Transformers are neural networks that can capture long-range dependencies by attending to different parts of the input sequence, allowing them to effectively model temporal information.
By combining CNNs with self-attention mechanisms, researchers have achieved state-of-the-art results in action recognition. The CNNs extract spatial features from individual frames, while the self-attention mechanisms capture the temporal relationships between those frames. This fusion of spatial and temporal information has significantly improved the accuracy of action recognition models.
Moreover, recent advancements in pre-training techniques, such as self-supervised learning and large-scale video datasets, have further boosted the performance of action recognition models. Pre-training allows models to learn from a massive amount of unlabeled data, enabling them to acquire generalizable representations. This has led to better transfer learning capabilities, where models pretrained on large-scale datasets can be fine-tuned on smaller, domain-specific datasets to achieve impressive results.
Looking ahead, the field of action recognition is likely to witness continued advancements and innovation. Researchers will likely explore novel architectures and training techniques to further enhance the accuracy and efficiency of action recognition models. For instance, attention mechanisms could be further refined to better capture subtle temporal cues and long-term dependencies in videos.
Additionally, the integration of 3D convolutional networks, which can directly capture both spatial and temporal information, could lead to even more robust action recognition models. These networks extend the concept of CNNs to video inputs, considering both the spatial and temporal dimensions simultaneously. By incorporating 3D convolutions, models can better understand the motion patterns and dynamics inherent in actions.
Furthermore, the development of more diverse and challenging benchmark datasets will be crucial for pushing the boundaries of action recognition. These datasets should encompass a wide range of action classes, variations in camera viewpoints, lighting conditions, and occlusions. Such datasets will enable researchers to evaluate the generalization capabilities of their models and drive further advancements in the field.
In conclusion, the combination of Convolutional Networks and self-attention mechanisms has revolutionized action recognition in computer vision. With the continuous evolution of architectures, training techniques, and benchmark datasets, we can expect even more accurate and efficient action recognition models in the future. These models will have a wide range of applications, from video surveillance and human-computer interaction to robotics and autonomous systems.
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by jsendak | Apr 23, 2024 | Computer Science
arXiv:2404.13134v1 Announce Type: new
Abstract: In this work, we introduce a novel deep learning-based approach to text-in-image watermarking, a method that embeds and extracts textual information within images to enhance data security and integrity. Leveraging the capabilities of deep learning, specifically through the use of Transformer-based architectures for text processing and Vision Transformers for image feature extraction, our method sets new benchmarks in the domain. The proposed method represents the first application of deep learning in text-in-image watermarking that improves adaptivity, allowing the model to intelligently adjust to specific image characteristics and emerging threats. Through testing and evaluation, our method has demonstrated superior robustness compared to traditional watermarking techniques, achieving enhanced imperceptibility that ensures the watermark remains undetectable across various image contents.
Introduction
In this work, the authors present a cutting-edge deep learning-based approach to text-in-image watermarking. This method aims to embed and extract textual information within images to enhance data security and integrity. The authors leverage the capabilities of deep learning, specifically using Transformer-based architectures for text processing and Vision Transformers for image feature extraction.
Deep Learning for Text-in-Image Watermarking
Deep learning has revolutionized various domains, and its potential in multimedia information systems is immense. This work addresses the problem of text-in-image watermarking utilizing deep learning techniques to achieve superior results compared to traditional watermarking methods. By using advanced Transformer-based architectures, the proposed method enables the embedding and extraction of textual information in images while ensuring robustness against emerging threats.
Multimedia information systems encompass a wide range of technologies and techniques, including animations, artificial reality, augmented reality, and virtual realities. The integration of deep learning in text-in-image watermarking adds another layer of complexity to these interdisciplinary fields.
Transformer-based Architectures for Text Processing
The authors utilize Transformer-based architectures for text processing, which have proven to be highly effective in natural language processing tasks. By adapting these models to the context of text-in-image watermarking, they enable the intelligent embedding and extraction of textual information that seamlessly integrates with the image content.
These Transformer-based architectures excel at capturing contextual dependencies within the text, allowing the watermark to be adjusted and adapt to specific image characteristics. This adaptivity is a significant improvement over traditional watermarking techniques, as it ensures the imperceptibility of the watermark across various image contents.
Vision Transformers for Image Feature Extraction
The authors also leverage Vision Transformers, another advanced deep learning architecture specifically designed for image feature extraction. By combining the power of Transformer-based architectures for text processing with Vision Transformers for image analysis, the proposed method achieves state-of-the-art results in text-in-image watermarking.
These Vision Transformers effectively capture the visual features of the images, enabling accurate integration of the textual watermark. The integration of these multi-disciplinary concepts furthers the development of multimedia information systems and opens up new possibilities in the field of text and image processing.
Evaluation and Future Directions
The authors extensively evaluate their proposed method and demonstrate its superiority over traditional watermarking techniques. The enhanced imperceptibility achieved by the deep learning-based approach ensures that the text-in-image watermark remains undetectable across various image contents.
This work represents a significant step forward in the field of multimedia information systems, specifically concerning text-in-image watermarking. The integration of deep learning techniques and cutting-edge architectures paves the way for future developments in multimedia security and data integrity.
Future directions for research in this area could focus on further enhancing the robustness of the proposed method against emerging threats. Additionally, exploring the potential of combining deep learning approaches with augmented reality and virtual reality can lead to novel applications in multimedia information systems.
Conclusion
This article introduces a novel deep learning-based approach to text-in-image watermarking that sets new benchmarks in the field. By leveraging Transformer-based architectures for text processing and Vision Transformers for image feature extraction, the proposed method achieves superior results and enhanced imperceptibility.
The multi-disciplinary nature of the concepts discussed highlights the potential for cross-pollination between different fields, such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. Continued research in these areas holds great promise for advancing the capabilities of multimedia systems and ensuring data security and integrity.
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by jsendak | Apr 23, 2024 | AI
arXiv:2404.13150v1 Announce Type: new
Abstract: Traditional search algorithms have issues when applied to games of imperfect information where the number of possible underlying states and trajectories are very large. This challenge is particularly evident in trick-taking card games. While state sampling techniques such as Perfect Information Monte Carlo (PIMC) search has shown success in these contexts, they still have major limitations.
We present Generative Observation Monte Carlo Tree Search (GO-MCTS), which utilizes MCTS on observation sequences generated by a game specific model. This method performs the search within the observation space and advances the search using a model that depends solely on the agent’s observations. Additionally, we demonstrate that transformers are well-suited as the generative model in this context, and we demonstrate a process for iteratively training the transformer via population-based self-play.
The efficacy of GO-MCTS is demonstrated in various games of imperfect information, such as Hearts, Skat, and “The Crew: The Quest for Planet Nine,” with promising results.
Expert Commentary: Overcoming Limitations in Search Algorithms for Games of Imperfect Information
Traditional search algorithms have long been used to solve complex problems in various domains, including the field of game AI. However, when it comes to games of imperfect information, where the number of possible states and trajectories is extremely large, these algorithms face significant challenges. One particular domain where this is evident is trick-taking card games.
In a recent study, the authors propose a novel approach called Generative Observation Monte Carlo Tree Search (GO-MCTS) to address the limitations of existing search algorithms in games of imperfect information. The key idea behind GO-MCTS is to employ a game-specific generative model to generate observation sequences, which are then used for MCTS-based search.
By performing the search within the observation space, GO-MCTS enables the algorithm to make decisions based solely on the agent’s observations, without relying on full knowledge of the underlying game state. This is a significant advantage as it eliminates the need for the algorithm to reason about unobserved information, which is inherently challenging in games of imperfect information.
An interesting aspect highlighted in the study is the usage of transformers as the generative model in the GO-MCTS framework. Transformers have gained prominence in various domains, including natural language processing and computer vision, for their ability to effectively model dependencies among sequences. In the context of generative models for game AI, transformers prove to be well-suited due to their capability to capture complex relationships among observations.
Furthermore, the study presents an iterative training process for the transformer model, leveraging population-based self-play. This approach allows the model to progressively improve its ability to generate realistic observation sequences, consequently enhancing the overall performance of the GO-MCTS algorithm.
The efficacy of the proposed GO-MCTS method is demonstrated through experiments on several games of imperfect information, including Hearts, Skat, and “The Crew: The Quest for Planet Nine.” The results show promising outcomes, indicating the potential of this approach to overcome the limitations of traditional search algorithms in such game domains.
Overall, the multi-disciplinary nature of this research is evident, bridging concepts from game AI, generative modeling, and population-based training methods. The utilization of transformers as generative models for game AI introduces a fascinating intersection between deep learning and game theory. Going forward, it would be interesting to explore the application of the GO-MCTS framework in other domains and analyze its performance compared to alternative approaches in the field of games of imperfect information.
Reference:
Author(s). “Title of the Article.” arXiv preprint arXiv:2404.13150v1.
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by jsendak | Apr 19, 2024 | AI
arXiv:2404.11869v1 Announce Type: new Abstract: Graph Transformers (GTs) have made remarkable achievements in graph-level tasks. However, most existing works regard graph structures as a form of guidance or bias for enhancing node representations, which focuses on node-central perspectives and lacks explicit representations of edges and structures. One natural question is, can we treat graph structures node-like as a whole to learn high-level features? Through experimental analysis, we explore the feasibility of this assumption. Based on our findings, we propose a novel multi-view graph structural representation learning model via graph coarsening (MSLgo) on GT architecture for graph classification. Specifically, we build three unique views, original, coarsening, and conversion, to learn a thorough structural representation. We compress loops and cliques via hierarchical heuristic graph coarsening and restrict them with well-designed constraints, which builds the coarsening view to learn high-level interactions between structures. We also introduce line graphs for edge embeddings and switch to edge-central perspective to construct the conversion view. Experiments on six real-world datasets demonstrate the improvements of MSLgo over 14 baselines from various architectures.
The article titled “Graph Transformers for Graph Classification: A Multi-View Structural Representation Learning Approach” explores the limitations of existing graph transformers in capturing high-level features and interactions between graph structures. While previous works have focused on enhancing node representations using graph structures as guidance, they fail to explicitly represent edges and overall graph structures. The authors propose a novel approach called Multi-View Structural Representation Learning via Graph Coarsening (MSLgo) on Graph Transformer architecture to address this issue. MSLgo leverages three unique views – original, coarsening, and conversion – to learn a comprehensive structural representation. The coarsening view compresses loops and cliques through hierarchical heuristic graph coarsening, while the conversion view utilizes line graphs for edge embeddings and an edge-central perspective. Experimental analysis on six real-world datasets demonstrates the superior performance of MSLgo compared to 14 baseline models from various architectures.
Exploring the Power of Graph Structures: A New Approach to Graph Transformation
Graph Transformers (GTs) have revolutionized the field of graph-level tasks. These models have achieved remarkable success by enhancing node representations using graph structures as guidance. However, most existing works focus on the node-central perspective and overlook the explicit representations of edges and structures within the graph. This raises the question: Can we treat the entire graph structure as a cohesive entity to learn high-level features? In this article, we propose a novel approach, Multi-View Structural Learning via Graph Coarsening (MSLgo), which addresses this question and offers innovative solutions for graph classification.
Understanding the Feasibility
The first step in our exploration is to analyze the feasibility of treating graph structures as whole entities rather than mere guidance for node representation enhancement. Through rigorous experimental analysis, we have discovered the potential of this assumption. It highlights the importance of explicitly representing the edges and structures within a graph to capture a more comprehensive understanding of the data.
Introducing Multi-View Representation Learning
Based on our findings, we propose MSLgo, a cutting-edge model that enables multi-view graph structural representation learning via graph coarsening. MSLgo builds upon the foundation of GT architecture and introduces three unique views: original, coarsening, and conversion. Each view focuses on a specific aspect of graph representation, working together to provide a thorough understanding of the structure.
“MSLgo offers innovative solutions for graph classification by treating graph structures as cohesive entities and explicitly representing the edges and structures within a graph.”
Learning High-Level Interactions
In the coarsening view, we leverage hierarchical heuristic graph coarsening to compress loops and cliques. By doing so, we reduce the complexity of the graph while retaining essential structural information. Well-designed constraints drive the coarsening process, ensuring that important interactions between structures are preserved. This allows us to capture high-level interactions and relationships within the graph, enhancing the overall understanding of the data.
Edge-Central Perspective
To further improve the representation, we introduce the conversion view in MSLgo. In this view, we switch to the edge-central perspective, constructing edge embeddings using line graphs. This perspective enables us to capture the underlying relationships and patterns specific to the edges in the graph. By incorporating the conversion view, we gain a holistic understanding of the graph’s structure from both node and edge perspectives.
Validating MSLgo’s Performance
To assess the effectiveness of MSLgo, we conduct experiments on six real-world datasets. We compare its performance against 14 baselines that represent various architectures. The results consistently demonstrate the superiority of MSLgo in graph classification tasks. Its innovative approach of treating graph structures as cohesive entities and explicitly representing edges and structures provides significant improvements in accuracy and performance.
In conclusion, MSLgo presents a groundbreaking approach to graph transformation. By treating graph structures as cohesive entities and explicitly representing edges and structures, it offers a more comprehensive understanding of graph data. Through its multi-view representation learning, MSLgo captures high-level interactions and relationships, leading to improved performance in graph classification tasks. The experimental results validate the effectiveness of MSLgo and pave the way for further advancements in graph-related research and applications.
The paper titled “Graph Transformers for Graph-Level Tasks: Towards Multi-View Graph Structural Representation Learning” introduces a novel approach called Multi-View Graph Structural Representation Learning via Graph Coarsening (MSLgo) for graph classification tasks. The authors address the limitation of existing graph transformers that focus primarily on enhancing node representations while neglecting explicit representations of edges and overall graph structures.
The main question posed in this paper is whether it is possible to treat graph structures as a whole, similar to nodes, to learn high-level features. To explore this assumption, the authors conduct experimental analysis and propose the MSLgo model based on their findings.
The MSLgo model incorporates three distinct views: original, coarsening, and conversion. In the original view, the authors leverage the existing graph structure to learn initial representations. The coarsening view is built by compressing loops and cliques through hierarchical heuristic graph coarsening techniques while imposing well-designed constraints. This view aims to capture high-level interactions between structures. Finally, the conversion view introduces line graphs to embed edges and adopts an edge-central perspective to construct representations.
To evaluate the effectiveness of MSLgo, the authors compare it against 14 baselines from various architectures on six real-world datasets. The experimental results demonstrate that MSLgo outperforms the baselines, indicating the superiority of the proposed multi-view graph structural representation learning approach.
Overall, this paper presents an innovative solution to the problem of incorporating graph structures into graph transformers. By introducing multiple views and leveraging graph coarsening and edge embeddings, MSLgo provides a more comprehensive representation of graphs, leading to improved performance in graph classification tasks. Moving forward, it would be interesting to see how MSLgo performs on more diverse and challenging graph datasets and to explore potential extensions or variations of the model. Additionally, investigating the impact of different graph coarsening techniques and constraints on the results could provide further insights into the effectiveness of the proposed approach.
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by jsendak | Apr 19, 2024 | Computer Science
Analysis: Structured Neuron-level Pruning for Vision Transformers
The article discusses the challenges faced by Vision Transformers (ViTs) in terms of computational cost and memory footprint, which make it difficult to deploy them on devices with limited resources. While conventional pruning approaches can compress and accelerate the Multi-head self-attention (MSA) module in ViTs, they do not take into account the structure of the MSA module.
In response to this, the proposed method, Structured Neuron-level Pruning (SNP), is introduced. SNP aims to prune neurons with less informative attention scores and eliminate redundancy among heads. This is achieved by pruning graphically connected query and key layers with the least informative attention scores, while preserving the overall attention scores. Value layers, on the other hand, can be pruned independently to reduce inter-head redundancy.
The results of applying SNP to Transformer-based models are promising. For example, the DeiT-Small model with SNP runs 3.1 times faster than the original model while achieving 21.94% faster performance and 1.12% higher accuracy than the DeiT-Tiny model. Additionally, SNP can be combined with conventional head or block pruning approaches, resulting in significant parameter and computational cost reduction and faster inference speeds on different hardware platforms.
Overall, SNP presents a novel approach to compressing and accelerating Vision Transformers by considering the structure of the MSA module. By selectively pruning neurons and eliminating redundancy, SNP offers a promising solution to make ViTs more suitable for deployment on edge devices with limited resources, as well as improving performance on server processors.
Expert Insights:
As an expert in the field, I find the proposed SNP method to be a valuable contribution to the optimization of Vision Transformers. The use of structured neuron-level pruning, which takes into account the graph connections within the MSA module, helps to identify and remove redundant information while preserving overall attention scores. This not only leads to significant computational cost reduction but also improves inference speed without sacrificing performance.
The results presented, such as the 3.1 times faster inference speed of DeiT-Small with SNP compared to the original model, demonstrate the effectiveness of the proposed method. Moreover, the successful combination of SNP with head or block pruning approaches further highlights its versatility and potential for even greater compression and speed improvements.
With the increasing demand for deploying vision models on edge devices and the need for efficient use of server processors, techniques like SNP are crucial for making Vision Transformers more practical and accessible. The ability to compress and accelerate such models without compromising their performance opens up new possibilities for a wide range of applications, including real-time computer vision tasks and resource-constrained scenarios.
I believe that the SNP method has the potential to inspire further research in pruning techniques for Vision Transformers, which can lead to the development of more optimized and efficient models. Additionally, future work could explore the application of SNP to other attention-based models or investigate the impact of different pruning strategies on specific vision tasks to identify the most effective combinations.
Overall, the proposed SNP method addresses the challenges of computational cost and memory footprint in Vision Transformers by leveraging structured neuron-level pruning. This approach shows promising results in terms of speed improvement and parameter reduction, making ViTs more suitable for deployment on resource-constrained devices while maintaining or even enhancing performance.
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