A Unified Vision-Language Transformer Model with Enhanced Visual Grounding and Generalization Capabilities

Core Concepts

A unified vision-language transformer model, ViLaM, that integrates instruction tuning based on large language models to optimally utilize their knowledge and reasoning capacities for a variety of language and vision tasks, while employing cycle training of referring expressions to address the need for high-quality, paired referring expression datasets.

Abstract

The paper introduces ViLaM, a unified vision-language transformer model that aims to enhance visual grounding and generalization capabilities by leveraging large language models (LLMs). The key highlights are: Architecture: ViLaM integrates instruction tuning based on LLMs to optimally utilize their knowledge and reasoning capacities for a variety of language and vision tasks. It employs frozen pre-trained encoders to encode and align both image and text features, enabling ViLaM to handle a variety of visual tasks following textual instructions. Cycle Training of Referring Expressions: The authors designed a cycle training approach for referring expressions to address the need for high-quality, paired referring expression datasets for training large models. The cycle training consists of two subtasks: referring expression generation (REG) and referring expression comprehension (REC), which are formulated as a visual question answering (VQA) task. This cycle training helps to learn the alignment relationships between bounding boxes and referring expressions, and also enables the expansion of the dataset by using normal object detection datasets without referring expressions. Evaluation and Generalization: ViLaM demonstrates state-of-the-art performance on public visual grounding datasets, such as RefCOCO, RefCOCO+, and RefCOCOg. The model also exhibits strong generalization capabilities in medical tasks, such as foreign object detection and disease localization in chest X-ray images, even in a zero-shot setting. Ablation studies confirm the importance of each component, including coordinates activation, cycle training, and data augmentation, in enhancing the model's visual grounding capabilities. Overall, the paper presents a comprehensive approach to developing a generalist vision-language model that can effectively handle a variety of language and vision tasks, with a particular focus on visual grounding and generalization.

Stats

The model achieves an Acc@0.5 of 92.99% in the val set, 95.90% in the testA set, and 90.39% in the testB set of the RefCOCO dataset. On the Object-CXR dataset for foreign object detection in chest X-rays, the model achieves an AUC of 93.1%, surpassing the JF Healthcare baseline of 92.1%. When fine-tuned on the TBX11K dataset for tuberculosis localization in chest X-rays, the model achieves an Acc@0.5 of 30.84% in the 20-shot setting, outperforming other methods. On the RSNA Pneumonia dataset, the model achieves an Acc@0.5 of 28% in the 20-shot setting for pneumonia localization, again outperforming other methods.

Quotes

"We incorporate the large language models into multi-modality systems, utilizing instruction tuning to maximize the use of the knowledge and inferential abilities of these pre-trained language models for intricate visual grounding tasks." "We design the cycle training of referring expressions, satisfying the requirements of paired referring expression datasets for the large model training, both in quantity and quality." "We assess the superior performance of ViLaM in public general datasets, and demonstrate its generalization in medical datasets. Besides, we observe the excellent zero-shot capability of the proposed method, suggesting the potential application of ViLaM in the medical field."

Key Insights Distilled From

Enhancing Visual Grounding and Generalization: A Multi-Task Cycle Training Approach for Vision-Language Models

by Xiaoyu Yang,... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2311.12327.pdf

Deeper Inquiries

How can the cycle training of referring expressions be further improved to enhance the quality and diversity of the generated referring expressions

To enhance the quality and diversity of the generated referring expressions through cycle training, several strategies can be implemented: Data Augmentation: Introducing diverse and complex referring expressions during training can help the model learn to handle a wider range of linguistic variations. This can involve incorporating synonyms, antonyms, and different linguistic structures to enrich the dataset. Fine-tuning with Domain-Specific Data: Fine-tuning the model with domain-specific datasets related to medical imaging can improve the model's ability to generate accurate and contextually relevant referring expressions in the medical field. Adversarial Training: Implementing adversarial training techniques can help the model generate more robust and diverse referring expressions by exposing it to challenging and diverse examples during training. Enforcing Consistency: Ensuring consistency in the generated referring expressions by incorporating constraints or regularization techniques can help maintain coherence and accuracy in the model's outputs. Human-in-the-Loop Validation: Incorporating human validation and feedback loops can help refine the generated referring expressions, ensuring they are contextually accurate and diverse.

What are the potential limitations of the current approach in handling more complex medical imaging tasks, such as multi-label disease classification or segmentation

The current approach may face limitations when handling more complex medical imaging tasks, such as multi-label disease classification or segmentation, due to the following reasons: Limited Training Data: Complex tasks like multi-label disease classification require a large amount of labeled data, which may be scarce in the medical domain. Insufficient data can hinder the model's ability to generalize effectively. Model Complexity: Multi-label disease classification and segmentation tasks often require intricate model architectures and training procedures to capture the nuances of different diseases and image features. The current model may need enhancements to handle such complexity. Interpretability: Complex medical imaging tasks often require interpretable models to provide insights into the decision-making process. Ensuring the proposed model maintains interpretability while handling complex tasks is crucial. Scalability: Scaling the model to handle a larger number of disease labels or segmentation tasks may pose computational challenges and require efficient optimization strategies.

How can the proposed vision-language model be extended to incorporate other modalities, such as audio or video, to enable more comprehensive multimodal understanding and reasoning

To extend the proposed vision-language model to incorporate other modalities like audio or video for comprehensive multimodal understanding and reasoning, the following steps can be taken: Multimodal Fusion Techniques: Implement fusion mechanisms that can effectively combine information from different modalities, such as audio, video, and text, to enable cross-modal reasoning and understanding. Cross-Modal Attention Mechanisms: Develop attention mechanisms that can capture correlations between different modalities, allowing the model to focus on relevant information across modalities during inference. Dataset Augmentation: Curate multimodal datasets that include audio, video, and text inputs to train the model on diverse and comprehensive data, enabling it to learn robust representations across modalities. Architecture Adaptation: Modify the model architecture to accommodate multiple modalities, ensuring seamless integration and processing of information from different sources. Evaluation Metrics: Define appropriate evaluation metrics that can assess the model's performance across multiple modalities, considering factors like accuracy, diversity, and coherence in multimodal outputs.