Video embeddings enable powerful video analysis and retrieval by learning compact representations of video content. Video embeddings are a crucial component in the field of video analysis, allowing for efficient and effective understanding of video content. By synthesizing information from various sources, such as video frames, audio, and text, these embeddings can be used for tasks like video recommendation, classification, and retrieval. Recent research has focused on improving the quality and applicability of video embeddings by incorporating external knowledge, handling incomplete and heterogeneous data, and capturing spatio-temporal dynamics. One recent study proposed a unified model for video understanding and knowledge embedding using a heterogeneous dataset containing multi-modal video entities and common sense relations. This approach not only improves video retrieval performance but also generates better knowledge graph embeddings. Another study introduced a Mixture-of-Embedding-Experts (MEE) model capable of handling missing input modalities during training, allowing for improved text-video embeddings learned simultaneously from image and video datasets. Furthermore, researchers have developed Video Region Attention Graph Networks (VRAG) to improve video-level retrieval by representing videos at a finer granularity and encoding spatio-temporal dynamics through region-level relations. This approach has shown higher retrieval precision than other existing video-level methods and faster evaluation speeds. Practical applications of video embeddings include video recommendation systems, content-based video retrieval, and video classification. For example, a company could use video embeddings to recommend relevant videos to users based on their viewing history or to filter inappropriate content. Additionally, video embeddings can be used to analyze and classify videos for various purposes, such as detecting anomalies or identifying specific actions within a video. In conclusion, video embeddings play a vital role in the analysis and understanding of video content. By leveraging advancements in machine learning and incorporating external knowledge, researchers continue to improve the quality and applicability of these embeddings, enabling a wide range of practical applications and furthering our understanding of video data.
Vision Transformer (ViT)
What is the difference between transformer and ViT?
Transformers are a type of neural network architecture initially designed for natural language processing tasks, such as machine translation and text summarization. They rely on self-attention mechanisms to capture long-range dependencies in the input data. Vision Transformers (ViTs), on the other hand, are an adaptation of the transformer architecture for computer vision tasks, such as image classification and object detection. ViTs process images by dividing them into patches and treating them as word embeddings, allowing the self-attention mechanism to capture spatial relationships between image regions.
What is vision transformer used for?
Vision Transformers (ViTs) are used for various computer vision tasks, including image classification, object detection, and semantic segmentation. They have achieved state-of-the-art performance in these tasks, surpassing traditional convolutional neural networks (CNNs). ViTs are particularly useful in scenarios where capturing long-range dependencies and spatial relationships in images is crucial for accurate predictions.
How do you use a ViT transformer?
To use a Vision Transformer (ViT), follow these steps: 1. Preprocess the input image by resizing and normalizing it. 2. Divide the image into non-overlapping patches of a fixed size. 3. Flatten each patch and linearly embed it into a vector representation. 4. Add positional encodings to the patch embeddings to retain spatial information. 5. Feed the resulting sequence of patch embeddings into a transformer architecture. 6. Train the ViT using a suitable loss function, such as cross-entropy for classification tasks. 7. Fine-tune the model on a specific task or dataset, if necessary. There are pre-trained ViT models and libraries available that can simplify this process, allowing you to focus on fine-tuning and applying the model to your specific problem.
What are the different types of vision transformers?
There are several variants of Vision Transformers (ViTs) that have been proposed to address different challenges and improve performance, robustness, and efficiency. Some notable types include: 1. DeiT (Data-efficient Image Transformers): These ViTs are designed to achieve competitive performance with fewer training samples, making them more data-efficient. 2. As-ViT (Auto-scaling Vision Transformers): This framework automates the design and scaling of ViTs without training, significantly reducing computational costs. 3. UP-ViTs (Unified Pruning Vision Transformers): These ViTs use a unified pruning framework to compress the model while maintaining its structure and accuracy. 4. PSAQ-ViT V2: A data-free quantization framework that achieves competitive results in image classification, object detection, and semantic segmentation tasks without accessing real-world data.
How do Vision Transformers compare to Convolutional Neural Networks?
Vision Transformers (ViTs) have demonstrated superior performance in various computer vision tasks compared to traditional Convolutional Neural Networks (CNNs). ViTs leverage the self-attention mechanism to capture long-range dependencies and spatial relationships in images, which can be advantageous over the local receptive fields used by CNNs. However, CNNs still generally provide better performance in reinforcement learning tasks, and they may be more efficient in terms of computational resources and memory usage for certain problems.
What are the limitations and challenges of Vision Transformers?
While Vision Transformers (ViTs) have shown promising results in various computer vision tasks, they still face some limitations and challenges: 1. Computational complexity: ViTs can be computationally expensive, especially for large-scale problems and high-resolution images. 2. Data requirements: ViTs often require large amounts of labeled data for training, which may not be available for all tasks or domains. 3. Adaptability: Adapting ViTs for reinforcement learning tasks remains a challenge, as convolutional-network architectures still generally provide superior performance in these scenarios. 4. Robustness: ViTs can be sensitive to changes in input data distribution, such as contrast-enhanced images, requiring additional research to improve their robustness. Ongoing research aims to address these limitations and further enhance the capabilities of ViTs, making them more accessible and applicable to a wider range of tasks and industries.
Vision Transformer (ViT) Further Reading
1.Improved Robustness of Vision Transformer via PreLayerNorm in Patch Embedding http://arxiv.org/abs/2111.08413v1 Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang, Dong Gu Lee, Wonseok Jeong, Sang Woo Kim2.Auto-scaling Vision Transformers without Training http://arxiv.org/abs/2202.11921v2 Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou3.Vision Transformer: Vit and its Derivatives http://arxiv.org/abs/2205.11239v2 Zujun Fu4.A Unified Pruning Framework for Vision Transformers http://arxiv.org/abs/2111.15127v1 Hao Yu, Jianxin Wu5.CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity Prediction http://arxiv.org/abs/2203.04570v1 Zhuoran Song, Yihong Xu, Zhezhi He, Li Jiang, Naifeng Jing, Xiaoyao Liang6.When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture http://arxiv.org/abs/2210.07540v1 Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, Yisen Wang7.Reveal of Vision Transformers Robustness against Adversarial Attacks http://arxiv.org/abs/2106.03734v2 Ahmed Aldahdooh, Wassim Hamidouche, Olivier Deforges8.PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers http://arxiv.org/abs/2209.05687v1 Zhikai Li, Mengjuan Chen, Junrui Xiao, Qingyi Gu9.Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels http://arxiv.org/abs/2204.04905v2 Tianxin Tao, Daniele Reda, Michiel van de Panne10.Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training http://arxiv.org/abs/2112.03552v4 Haofei Zhang, Jiarui Duan, Mengqi Xue, Jie Song, Li Sun, Mingli SongExplore More Machine Learning Terms & Concepts
Video embeddings Visual Odometry Visual Odometry: A Key Technique for Autonomous Navigation and Localization Visual odometry is a computer vision-based technique that estimates the motion and position of a robot or vehicle using visual cues from a camera or a set of cameras. This technology has become increasingly important for autonomous navigation and localization in various applications, including mobile robots and self-driving cars. Visual odometry works by tracking features in consecutive images captured by a camera, and then using these features to estimate the motion of the camera between the frames. This information can be combined with other sensor data, such as from inertial measurement units (IMUs) or LiDAR, to improve the accuracy and robustness of the motion estimation. The main challenges in visual odometry include dealing with repetitive textures, occlusions, and varying lighting conditions, as well as ensuring real-time performance and low computational complexity. Recent research in visual odometry has focused on developing novel algorithms and techniques to address these challenges. For example, Deep Visual Odometry Methods for Mobile Robots explores the use of deep learning techniques to improve the accuracy and robustness of visual odometry in mobile robots. Another study, DSVO: Direct Stereo Visual Odometry, proposes a method that operates directly on pixel intensities without explicit feature matching, making it more efficient and accurate than traditional stereo-matching-based methods. In addition to algorithmic advancements, researchers have also explored the integration of visual odometry with other sensors, such as in the Super Odometry framework, which fuses data from LiDAR, cameras, and IMUs to achieve robust state estimation in challenging environments. This multi-modal sensor fusion approach can help improve the performance of visual odometry in real-world applications. Practical applications of visual odometry include autonomous driving, where it can be used for self-localization and motion estimation in place of wheel odometry or inertial measurements. Visual odometry can also be applied in mobile robots for tasks such as simultaneous localization and mapping (SLAM) and 3D map reconstruction. Furthermore, visual odometry has been used in underwater environments for localization and navigation of underwater vehicles. One company leveraging visual odometry is Team Explorer, which has deployed the Super Odometry framework on drones and ground robots as part of their effort in the DARPA Subterranean Challenge. The team achieved first and second place in the Tunnel and Urban Circuits, respectively, demonstrating the effectiveness of visual odometry in real-world applications. In conclusion, visual odometry is a crucial technology for autonomous navigation and localization, with significant advancements being made in both algorithm development and sensor fusion. As research continues to address the challenges and limitations of visual odometry, its applications in various domains, such as autonomous driving and mobile robotics, will continue to expand and improve.
