GLIPv2: Unifying Localization and Vision-Language Understanding (2206.05836v2)

Abstract: We present GLIPv2, a grounded VL understanding model, that serves both localization tasks (e.g., object detection, instance segmentation) and Vision-Language (VL) understanding tasks (e.g., VQA, image captioning). GLIPv2 elegantly unifies localization pre-training and Vision-Language Pre-training (VLP) with three pre-training tasks: phrase grounding as a VL reformulation of the detection task, region-word contrastive learning as a novel region-word level contrastive learning task, and the masked LLMing. This unification not only simplifies the previous multi-stage VLP procedure but also achieves mutual benefits between localization and understanding tasks. Experimental results show that a single GLIPv2 model (all model weights are shared) achieves near SoTA performance on various localization and understanding tasks. The model also shows (1) strong zero-shot and few-shot adaption performance on open-vocabulary object detection tasks and (2) superior grounding capability on VL understanding tasks. Code will be released at https://github.com/microsoft/GLIP.

Overview of GLIPv2: Unifying Localization and Vision-Language Understanding

The paper introduces GLIPv2, a unified model designed for both localization tasks (like object detection and instance segmentation) and Vision-Language (VL) understanding tasks such as Visual Question Answering (VQA) and image captioning. This work builds upon the growing interest in creating versatile vision systems that can handle a wide range of tasks using a single model architecture.

Model Architecture and Pre-training

GLIPv2 leverages a novel approach to unify these tasks through a shared architecture known as Architecture $\mathbf{\Pi}$. This consists of a dual encoder for images and text, alongside a fusion encoder, allowing for comprehensive cross-modality feature extraction. The model employs a unified pre-training process that translates localization tasks into VL grounding tasks, utilizing synthesized sentences to represent category names and self-training on large-scale image-text pairs.

The pre-training is structured around three core tasks:

1. Phrase Grounding: Reformulating detection tasks to enhance VL grounding.
2. Region-Word Contrastive Learning: Introducing a batch-wise contrastive loss to improve feature discrimination.
3. Masked LLMing: Incorporating semantic understanding from masked tokens.

Experimental Results

Empirical results demonstrate that GLIPv2 achieves near state-of-the-art (SoTA) performance across various benchmarks. Specifically, it excels in:

• Object Detection and Instance Segmentation: Showing robust zero-shot and few-shot capabilities.
• VL Understanding Tasks: Providing strong grounding capabilities beneficial for VQA and image captioning.

The paper highlights model efficiency with shared weights across different tasks, minimizing the need for task-specific tuning while maintaining competitive performance.

Implications and Future Directions

The unification of localization and VL understanding in GLIPv2 presents several practical and theoretical implications. Practically, it simplifies deployment in real-world applications where multi-task handling is crucial. Theoretically, it challenges the traditional separation of vision and language tasks, encouraging further research into integrated vision-LLMs.

Future work could explore scaling the model with additional weakly-supervised data, potentially improving the diversity of recognized concepts. The grounded VL understanding paradigm enables richer interpretability, fostering advancements in explainable AI.

Overall, GLIPv2 represents a promising step towards highly adaptive vision-language systems, setting a foundation for broader applications and more cohesive AI models.