Bundle Adjustment: A Key Technique for 3D Reconstruction and Camera Pose Estimation Bundle adjustment is a crucial optimization technique used in computer vision and photogrammetry for refining 3D structure and camera pose estimation. It plays a vital role in applications such as Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM). However, as the scale of the problem grows, bundle adjustment becomes computationally expensive and faces challenges in terms of memory and efficiency. Recent research has focused on improving the performance of bundle adjustment in various ways. For instance, multi-view large-scale bundle adjustment methods have been developed to handle images from different satellite cameras with varying imaging dates, viewing angles, and resolutions. Another approach, called rotation averaging, optimizes only camera orientations, simplifying the overall algorithm and making it more capable of handling slow or pure rotational motions. Distributed and parallel bundle adjustment techniques have also been proposed to tackle the memory and efficiency issues in large-scale reconstruction. One such method, called square root bundle adjustment, relies on nullspace marginalization of landmark variables by QR decomposition, allowing for solving large-scale problems with single-precision floating-point numbers. Practical applications of bundle adjustment include 3D reconstruction of scenes, camera pose estimation, and large-scale mapping. For example, in the case of uncalibrated multi-camera systems, constrained bundle adjustment can be used to improve the accuracy of 3D dense point clouds. Another application is the spatiotemporal bundle adjustment for dynamic 3D human reconstruction in the wild, which jointly optimizes camera intrinsics and extrinsics, static 3D points, sub-frame temporal alignment, and dynamic point trajectories. A company case study is the use of bundle adjustment in Google's Street View, where it helps to refine the 3D structure and camera poses for accurate and seamless street-level imagery. By leveraging bundle adjustment techniques, Google can provide high-quality, georeferenced images for various applications, such as navigation, urban planning, and virtual tourism. In conclusion, bundle adjustment is a critical technique in computer vision and photogrammetry, with numerous applications and ongoing research to address its challenges. As the field continues to evolve, we can expect further improvements in efficiency, scalability, and robustness, enabling even more accurate and large-scale 3D reconstructions and camera pose estimations.
Byte Pair Encoding (BPE)
What is byte pair encoding (BPE) and how does it work?
Byte Pair Encoding (BPE) is a subword tokenization technique used in natural language processing and machine translation. It helps address the open vocabulary problem by breaking down words into smaller, more manageable units. BPE works by iteratively merging the most frequent character sequences in a text, creating a fixed-size vocabulary of subword units. This approach enables models to learn the compositionality of words and be more robust to segmentation errors.
How does BPE improve natural language processing and machine translation?
BPE improves natural language processing and machine translation by allowing models to better handle rare and out-of-vocabulary words. By breaking down words into smaller units, BPE enables models to learn the compositionality of words, making them more robust to segmentation errors and improving their overall performance.
What is byte-level BPE?
Byte-level BPE is a variant of Byte Pair Encoding that operates at the byte level instead of the character level. This approach allows for even finer-grained tokenization, which can be particularly useful for handling languages with large character sets or for tasks that require byte-level granularity, such as named entity recognition.
Does BERT use byte pair encoding?
Yes, BERT (Bidirectional Encoder Representations from Transformers) uses byte pair encoding for tokenization. BPE helps BERT handle rare and out-of-vocabulary words, improving its performance on various natural language understanding tasks.
What byte-level BPE is used in GPT-2?
GPT-2 (Generative Pre-trained Transformer 2) uses a byte-level BPE tokenizer. This tokenizer operates at the byte level, allowing GPT-2 to handle a wide range of languages and character sets, as well as improving its ability to generate human-like text and perform various natural language understanding tasks.
How is BPE used in text-to-SQL generation?
In text-to-SQL generation, BPE can be used to tokenize both the input text and the SQL queries. By breaking down words into smaller units, BPE allows models to better handle rare and out-of-vocabulary words, improving the accuracy of the generated SQL queries. Recent research has introduced a novel stopping criterion for BPE in text-to-SQL generation, which prevents overfitting the encoding to the training set and further improves the model's performance.
Can BPE be applied to code completion tasks?
Yes, BPE can be applied to code completion tasks. By tokenizing source code using BPE, models can learn the compositionality of programming languages and better handle rare and out-of-vocabulary tokens. This can improve the accuracy and usefulness of code completion suggestions provided by the model.
How does BPE-Dropout improve translation quality?
BPE-Dropout is a subword regularization method that stochastically corrupts the segmentation procedure of BPE, leading to multiple segmentations within the same fixed BPE framework. This approach introduces a form of data augmentation, which helps the model generalize better and improves translation quality compared to conventional BPE.
Byte Pair Encoding (BPE) Further Reading
1.BPE-Dropout: Simple and Effective Subword Regularization http://arxiv.org/abs/1910.13267v2 Ivan Provilkov, Dmitrii Emelianenko, Elena Voita2.Byte-Pair Encoding for Text-to-SQL Generation http://arxiv.org/abs/1910.08962v2 Samuel Müller, Andreas Vlachos3.Code Completion using Neural Attention and Byte Pair Encoding http://arxiv.org/abs/2004.06343v1 Youri Arkesteijn, Nikhil Saldanha, Bastijn Kostense4.A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation http://arxiv.org/abs/1905.10453v2 Shuoyang Ding, Adithya Renduchintala, Kevin Duh5.Learning variable length units for SMT between related languages via Byte Pair Encoding http://arxiv.org/abs/1610.06510v3 Anoop Kunchukuttan, Pushpak Bhattacharyya6.Byte Pair Encoding is Suboptimal for Language Model Pretraining http://arxiv.org/abs/2004.03720v2 Kaj Bostrom, Greg Durrett7.How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation? http://arxiv.org/abs/2208.05225v2 Ali Araabi, Christof Monz, Vlad Niculae8.What changes when you randomly choose BPE merge operations? Not much http://arxiv.org/abs/2305.03029v1 Jonne Sälevä, Constantine Lignos9.Byte Pair Encoding for Symbolic Music http://arxiv.org/abs/2301.11975v1 Nathan Fradet, Jean-Pierre Briot, Fabien Chhel, Amal El Fallah Seghrouchni, Nicolas Gutowski10.A Byte-sized Approach to Named Entity Recognition http://arxiv.org/abs/1809.08386v1 Emily Sheng, Prem NatarajanExplore More Machine Learning Terms & Concepts
Bundle Adjustment Byte-Level Language Models Byte-Level Language Models: A powerful tool for understanding and processing diverse languages. Language models are essential components in natural language processing (NLP) systems, enabling machines to understand and generate human-like text. Byte-level language models are a type of language model that processes text at the byte level, allowing for efficient handling of diverse languages and scripts. The development of byte-level language models has been driven by the need to support a wide range of languages, including those with complex grammar and morphology. Recent research has focused on creating models that can handle multiple languages simultaneously, as well as models specifically tailored for individual languages. For example, Cedille is a large autoregressive language model designed for the French language, which has shown competitive performance with GPT-3 on French zero-shot benchmarks. One of the challenges in developing byte-level language models is dealing with the inherent differences between languages. Some languages are more difficult to model than others due to their complex inflectional morphology. To address this issue, researchers have developed evaluation frameworks for fair cross-linguistic comparison of language models, using translated text to ensure that all models are predicting approximately the same information. Recent advancements in multilingual language models, such as XLM-R, have shown that languages can occupy similar linear subspaces after mean-centering. This allows the models to encode language-sensitive information while maintaining a shared multilingual representation space. These models can extract a variety of features for downstream tasks and cross-lingual transfer learning. Practical applications of byte-level language models include language identification, code-switching detection, and evaluation of translations. For instance, a study on language identification for Austronesian languages demonstrated that a classifier based on skip-gram embeddings achieved significantly higher performance than alternative methods. Another study explored the Slavic language continuum in neural models of spoken language identification, finding that the emergent representations captured language relatedness and perceptual confusability between languages. In conclusion, byte-level language models have the potential to revolutionize the way we process and understand diverse languages. By developing models that can handle multiple languages or cater to specific languages, researchers are paving the way for more accurate and efficient NLP systems. As these models continue to advance, they will enable a broader range of applications and facilitate better communication across language barriers.
