ViLBERT-Multi-Task is an extension of the ViLBERT model, designed to learn joint representations of vision and language through multi-task learning. It processes visual and textual inputs in separate streams that interact through co-attentional transformer layers. The model is pretrained on the Conceptual Captions dataset and fine-tuned on various vision-and-language tasks, including visual question answering, referring expression comprehension, and image retrieval. This approach enables the model to generalize across multiple tasks with shared representations
Key Features
Multi-Task Learning: Trains on 12 vision-and-language tasks simultaneously, enhancing generalization across tasks.
Co-Attentional Transformer Layers: Facilitates interaction between visual and textual modalities.
Pretraining on Conceptual Captions: Utilizes a large-scale dataset to learn joint representations.
Fine-Tuning on Diverse Tasks: Adapts the pretrained model to specific tasks like VQA, image retrieval, and referring expression comprehension.
Open Source Implementation: Provides code and pretrained models for research and development.
Android DevHub
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