Notes on Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution

Link to paper: https://arxiv.org/abs/2307.06304

Paper published on: 2023-07-12

Paper's authors: Mostafa Dehghani, Basil Mustafa, Josip Djolonga, Jonathan Heek, Matthias Minderer, Mathilde Caron, Andreas Steiner, Joan Puigcerver, Robert Geirhos, Ibrahim Alabdulmohsin, Avital Oliver, Piotr Padlewski, Alexey Gritsenko, Mario Lučić, Neil Houlsby

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Imagine if you could take a popular model for computer vision tasks, the Vision Transformer (ViT), and enhance it to process inputs of arbitrary resolutions and aspect ratios. The NaViT (Native Resolution ViT) model, introduced in the research paper, does just that. It introduces a novel method called Patch n' Pack, which allows for the processing of inputs of any resolution. This is akin to having a telescope that can adjust to any distance, providing a clear view regardless of how far or close the object is.

The NaViT model is a bit like a tailor, adjusting and fitting the input data to suit its needs. In the training process, NaViT uses sequence packing to combine multiple patches from different images into a single sequence. This is like piecing together different parts of a puzzle to form a complete picture. Randomly sampling resolutions during the training process not only reduces the training cost but also improves the model's performance.

NaViT is not just a one-trick pony; it can be efficiently transferred to standard computer vision tasks, such as image and video classification, object detection, and semantic segmentation. It also improves training efficiency for large-scale supervised and contrastive image-text pretraining. This is like a Swiss Army knife, versatile and efficient in different tasks.

The researchers made architectural changes to the NaViT model, including masked self-attention and pooling, and introduced factorized and fractional positional embeddings. These changes are akin to modifying the design of a car for better performance and efficiency. Training changes in NaViT include continuous token dropping and resolution sampling, like tuning the engine of a car for optimal performance.

The NaViT model offers computational efficiency during pre-training and fine-tuning and can be applied successfully to multiple resolutions. This is like having a car that can efficiently run on different types of fuel. NaViT allows for flexible inference and opens up new possibilities for innovation and advancement in computer vision.

The researchers also tackled the issue of memory cost in transformer models. They found that memory-efficient methods can address the memory cost of self-attention in extremely long sequences. This is like using efficient storage methods to store more items in a limited space.

The researchers introduced a method called continuous token dropping, enabled by sequence packing, which improves performance. This is like strategically dropping weights from a hot air balloon to increase its altitude. They also explored the use of factorized embeddings and their design choices, which outperformed baseline ViT and learned 2D embeddings.

The NaViT model shows improved out-of-distribution generalization compared to ViT models, like a student who performs well not just in familiar topics but also in unfamiliar ones. The researchers also quantified the quality of uncertainty computed by NaViT models and found a stable calibration error.

The NaViT model provides higher prediction accuracy for fairness signal annotation compared to ViT, like a more accurate weather forecast. It also transfers competitively to semantic segmentation and outperforms ViT at the same maximum finetuning resolution.

The NaViT model also performs better than the ViT model on both common and unseen LVIS "rare" classes in object detection tasks. This is like a detector that can accurately detect both common and rare minerals.

The researchers introduced a new method, Patch n' Pack, which improves training efficiency by enabling mixed resolutions without complex schedules or training pipelines. This is like a flexible workout plan that can adjust to your schedule and fitness level.

The packing algorithm used for batching examples into sequences is a simple greedy approach that adds examples to the first sequence with enough remaining space and fills the remaining sequences with padding tokens. This is like packing a suitcase by adding items to the first compartment with enough space and filling the remaining compartments with padding materials.

The researchers explored different methods for sampling token dropping rates, including using a beta distribution and a truncated normal distribution. They presented a token dropping schedule that varies with the total number of images seen during training.

The positional embeddings in the model were evaluated based on their performance within the training distribution of input sizes and their performance on image sizes outside of the training distribution. The researchers discussed different types of positional embeddings, including learned, parametric, fixed, absolute, fractional, and factorized embeddings.

The researchers performed out-of-distribution evaluation using different strategies for cropping and resizing images. They found that using native image resolution improves the performance of fairness signal annotation. This is like finding that using a native language improves the performance of translation.

The research focuses on reducing labeling errors in fairness signals to improve bias mitigation and post-hoc auditing. This is like focusing on reducing measurement errors in an experiment to improve the accuracy of the results.

The NaViT model performs well on the "model-vs-human" benchmark, achieving similar performance to fine-tuning one NaViT model per test resolution. The ViT baseline performs worse than NaViT models for lower resolutions and better for higher resolutions in the "model-vs-human" benchmark. This is like comparing the performance of two athletes at different altitudes; one performs better at lower altitudes, and the other performs better at higher altitudes.

In conclusion, the NaViT model, with its novel Patch n' Pack method and various architectural and training changes, provides a new way to process inputs of arbitrary resolutions and aspect ratios. It offers computational efficiency and flexibility, and it opens up new possibilities for advancement in computer vision.