Adapting Decoder-only Transformer Architecture from Language to Vision Tasks

Alapfogalmak

Through straightforward supervised learning, the decoder-only Transformer architecture used in large language models (LLMs) can be adapted to process visual input effectively, achieving competitive performance compared to encoder-only vision Transformers.

Kivonat

The authors examine whether decoder-only Transformers, such as LLaMA, originally designed for large language models (LLMs), can be adapted to the computer vision field. They first "LLaMAfy" a standard Vision Transformer (ViT) step-by-step to align with LLaMA's architecture, and find that directly applying a causal mask to the self-attention brings an attention collapse issue, resulting in the failure of network training.
To overcome this challenge, the authors propose a post-sequence class token (PS [cls]) technique, which repositions the class token to the end of the image tokens. This enables causal self-attention to efficiently capture the entire image's information. Additionally, they develop a soft mask strategy that gradually introduces a causal mask to the self-attention at the onset of training to facilitate the optimization behavior.
The tailored model, dubbed as image LLaMA (iLLaMA), is akin to LLaMA in architecture and enables direct supervised learning. Its causal self-attention boosts computational efficiency and learns complex representation by elevating attention map ranks. iLLaMA rivals the performance with its encoder-only counterparts, achieving 75.1% ImageNet top-1 accuracy with only 5.7M parameters. Scaling the model to ~310M and pre-training on ImageNet-21K further enhances the accuracy to 86.0%.
The authors conduct extensive experiments to demonstrate iLLaMA's reliable properties, including calibration, shape-texture bias, quantization compatibility, ADE20K segmentation, and CIFAR transfer learning. They hope their study can provide fresh insights for the architectural unification of vision and text models in the era of LLMs.

Statisztikák

73.8% ImageNet top-1 accuracy for ViT-T/16 baseline
74.3% ImageNet top-1 accuracy after replacing MLP with SwiGLU
74.5% ImageNet top-1 accuracy after replacing LN with RMSNorm
71.9% ImageNet top-1 accuracy with post-sequence class token (PS [cls])
72.5% ImageNet top-1 accuracy with modified causal mask

Idézetek

"Through straightforward supervised learning, decoder-only Transformers such as LLaMA, which were originally designed for large language models (LLMs), can be adapted to the computer vision field."
"We suggest to reposition the class token behind the image tokens with a post-sequence class token technique to overcome this challenge, enabling causal self-attention to efficiently capture the entire image's information."
"iLLaMA rivals the performance with its encoder-only counterparts, achieving 75.1% ImageNet top-1 accuracy with only 5.7M parameters. Scaling the model to ~310M and pre-training on ImageNet-21K further enhances the accuracy to 86.0%."

Főbb Kivonatok

Adapting LLaMA Decoder to Vision Transformer

by Jiahao Wang,... : arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06773.pdf

Mélyebb kérdések

How can the proposed techniques in iLLaMA, such as the post-sequence class token and soft mask strategy, be generalized to other vision Transformer architectures beyond LLaMA

The techniques proposed in iLLaMA, such as the post-sequence class token and soft mask strategy, can be generalized to other vision Transformer architectures beyond LLaMA by understanding the underlying principles and adapting them accordingly.

Post-Sequence Class Token: This technique involves repositioning the class token behind the image tokens to overcome attention collapse issues. To generalize this to other architectures, one would need to identify the equivalent components in the target architecture that handle class tokens or global information. By adjusting the position or incorporating a similar mechanism, the architecture can benefit from improved optimization and information flow.

Soft Mask Strategy: The soft mask strategy gradually transitions from bi-directional to casual self-attention during training, aiding in optimization. To apply this to other architectures, one would need to identify the areas where attention mechanisms can be modified dynamically. By introducing a similar soft mask approach, models can potentially improve training behavior and performance.

Adaptation and Experimentation: Generalizing these techniques would involve experimentation and adaptation based on the specific architecture's components and requirements. Understanding the architecture's unique characteristics and how the proposed techniques can enhance its performance is crucial for successful implementation.

By carefully analyzing the architecture, identifying analogous components, and experimenting with adaptations, the post-sequence class token and soft mask strategy can be effectively extended to enhance various vision Transformer architectures beyond LLaMA.

What are the potential limitations or drawbacks of using a decoder-only Transformer design for visual tasks compared to the more prevalent encoder-only architectures

Using a decoder-only Transformer design for visual tasks presents certain limitations and drawbacks compared to the more prevalent encoder-only architectures:

Limited Contextual Information: Decoder-only architectures may struggle to capture long-range dependencies and contextual information compared to encoder-decoder setups. This limitation can impact tasks that require a comprehensive understanding of the input data.

Complexity in Training: Decoder-only models may face challenges in training due to issues like attention collapse, as seen in the context of iLLaMA. Ensuring stable optimization and effective information flow can be more intricate in decoder-only designs.

Potential Overfitting: Decoder-only architectures might be more prone to overfitting, especially in tasks with complex data distributions. The lack of bidirectional information flow and reliance on autoregressive mechanisms can lead to overfitting on training data.

Task Specificity: Decoder-only models may be more suitable for specific tasks like generation or autoregressive tasks, where the sequential nature of decoding is essential. For tasks requiring bidirectional information flow or complex interactions, encoder-decoder architectures might be more effective.

While decoder-only designs offer advantages like simplicity and scalability, they also come with inherent limitations that need to be carefully addressed for optimal performance in visual tasks.

Given the success of iLLaMA on image classification, how might the decoder-only approach be extended to other computer vision tasks like object detection, segmentation, or generation

Extending the decoder-only approach of iLLaMA to other computer vision tasks like object detection, segmentation, or generation involves considering the unique requirements and characteristics of each task:

Object Detection: For object detection, the decoder-only approach can be adapted by incorporating mechanisms for multi-scale feature fusion, object localization, and context aggregation. Techniques like positional encodings, attention mechanisms, and task-specific adaptations can enhance the model's ability to detect objects accurately.

Segmentation: In segmentation tasks, the decoder-only design can be extended by integrating dense prediction layers, skip connections, and spatial context modeling. Leveraging causal self-attention for pixel-wise predictions and incorporating hierarchical features can improve segmentation accuracy and detail preservation.

Generation: For image generation tasks, the decoder-only architecture can be utilized for autoregressive modeling, enabling the generation of high-quality images pixel by pixel. Techniques like conditional generation, latent space manipulation, and attention mechanisms can enhance the model's ability to generate diverse and realistic images.

By tailoring the decoder-only approach of iLLaMA to the specific requirements of each task, incorporating task-specific components and adaptations, the model can be effectively extended to a range of computer vision tasks beyond image classification.