Enhancing Multimodal Reasoning with Transformers
Table of Contents
- Introduction
- Multimodal Reasoning with Transformers
- Visual Question Answering (VQA)
- Multimodal Pipelines for VQA
- The Role of Transformer Architecture
- Object Detection using Transformers
- The Rise of Transformer-based Models in Computer Vision
- Introducing Deter and Deformable Detail
- Combining Transformers for Multimodal Reasoning
- Unified Architecture for Multimodal Reasoning
- Incorporating Deter and Deformable Detail into the Text Model
- Fine-tuning Language and Visual Components
- Assessing the Performance of Transformer-based Detectors
- Comparing Deter, Deformable Detail, and Faster R-CNN
- Pre-training and VQA Accuracy
- Results and Improvements
- Thresholding Object Predictions
- Improving Accuracy through Object Detector Tuning
- Performance of Text Model with Deformable Detail and Liter
- Conclusion
🤖 Multimodal Reasoning with Transformers
In recent years, there has been a growing interest in multimodal reasoning tasks, such as visual question answering (VQA), that require combining visual and textual information. In this article, we explore the use of transformers in the context of multimodal reasoning. We specifically focus on the VQA task and experiment with the VQA version 2 dataset.
📷 Visual Question Answering (VQA)
Visual question answering is a task that involves answering questions about an image using natural language. It requires understanding the visual content of the image and processing the textual input in order to generate accurate answers. Multimodal pipelines for VQA traditionally rely on pre-extracted visual features from popular object detectors like Faster R-CNN.
🚀 Multimodal Pipelines for VQA
Most multimodal pipelines for VQA combine fixed visual features with language models, such as those based on the transformer architecture. However, these pipelines do not allow for end-to-end training, as the visual features from object detectors are fixed and cannot be tuned to the specific multimodal task at HAND.
🏙️ The Role of Transformer Architecture
Transformer-based models have shown remarkable performance in natural language processing tasks and have also gained popularity in computer vision tasks. One example is the detection transformer, known as Deter or Deformable Detail, which offers a conceptually simpler structure compared to Faster R-CNN. These models leverage the transformer architecture to encode and decode image features for object detection. Deformable Detail, in particular, introduces deformable attention and multi-Scale features, resulting in improved performance for smaller objects.
✨ Combining Transformers for Multimodal Reasoning
In this work, we propose a unified architecture for multimodal reasoning where the visual and textual pipelines are tightly integrated and can be trained end-to-end. We introduce Deter and Deformable Detail as object detectors into our multimodal pipeline, which we call the Text Model. The predictions from the object detectors are directly passed to the language model at runtime. By combining the object features, bounding box coordinates, and text embeddings, the language model learns to reason over multimodal inputs.
🔍 Assessing the Performance of Transformer-based Detectors
To evaluate the performance of Deter and Deformable Detail as object detectors, we compare them to the widely used Faster R-CNN. We pre-train these detectors on the Visual Genome dataset and assess their accuracy on the VQA task. The results show that both Deter and Deformable Detail are competitive, even with their lower dimensionality compared to Faster R-CNN.
💡 Results and Improvements
We further enhance the performance of our Text Model by thresholding the object predictions to obtain a manageable number of objects per image. This improves the accuracy of the model on the VQA task. Additionally, fine-tuning the object detectors through the VQA task further improves the accuracy. When combined with Deformable Detail and Liter, our Text Model achieves a VQA accuracy of 69.93%, surpassing the baseline accuracy by 1.1%.
Conclusion
In this work, we have successfully integrated detection transformers into multimodal pipelines for cross-modal reasoning. We have highlighted the importance of global context and a suitable loss function for achieving better performance on the VQA task. Our proposed Text Model, with the inclusion of Deter and Deformable Detail as object detectors, can be trained end-to-end for multimodal reasoning. The results demonstrate the effectiveness of the transformer architecture in the field of computer vision and multimodal reasoning.
Highlights
- Multimodal reasoning combines visual and textual information
- Transformer models show promise in multimodal reasoning tasks
- Introducing Deter and Deformable Detail as object detectors
- Unified architecture for end-to-end multimodal reasoning
- Comparison of Deter, Deformable Detail, and Faster R-CNN
- Improving accuracy through object predictor thresholding
- Fine-tuning object detectors for better performance
- Text Model achieves 69.93% VQA accuracy with Deformable Detail and Liter
FAQ
Q: What is visual question answering (VQA)?
A: Visual question answering is a task that involves answering questions about an image using natural language.
Q: How do multimodal pipelines for VQA work?
A: Multimodal pipelines for VQA combine visual features from object detectors with language models to generate answers to questions.
Q: What is the role of transformer architecture in multimodal reasoning?
A: Transformers offer a powerful architecture for encoding and decoding visual and textual information, enabling effective multimodal reasoning.
Q: How does deformable detail improve object detection performance?
A: Deformable detail introduces deformable attention and multi-scale features, resulting in improved performance for detecting smaller objects.
Q: How does the Text Model achieve end-to-end training for multimodal reasoning?
A: The Text Model combines object detector predictions, object features, bounding box coordinates, and text embeddings, allowing for end-to-end training and reasoning over multimodal inputs.