VLMs are powerful tools for capturing visual and textual data, promising advances in tasks such as image captioning and visual question answering. Limited data availability hampers its performance. Recent advances show that pre-training VLM on larger image and text datasets improves downstream tasks. However, creating such datasets faces challenges: scarcity of paired data, high curation costs, low diversity, and noisy data from the Internet.
Previous studies demonstrate the effectiveness of VLMs on tasks such as image captioning, using various architectures and pre-training strategies. Recent advances in high-quality image generators have sparked interest in using generative models for synthetic data generation. This trend affects several computer vision tasks, including semantic segmentation, human motion understanding, and image classification. This study also explores the integration of data-driven generative models within VLM, emphasizing efficiency in generating image embeddings directly integrated into the model, showing superiority over existing approaches.
Google DeepMind researchers have proposed Synthesizer2. This method leverages pre-trained generative text and image models to create synthetic paired data for VLM, addressing the challenges of data sparsity, cost, and noise. Generates text and images synthetically, avoiding dependence on real-world data. The approach operates at the embedding level, avoiding costly pixel space rendering, thus improving efficiency without compromising performance. Pre-training the text-to-image model on the same dataset used for VLM training ensures fair evaluation and prevents unwanted knowledge transfer.
Synth2 leverages pre-trained generative text and image models to create synthetic paired data for VLM training. It includes components for Caption Generation, which uses LLM with class-based cues for various captions, and Image Generation, which uses a controlled text-to-image generator trained on the same data set as the VLM to ensure fair evaluation. The Synth2 VLM architecture integrates VQ-GAN backbones for efficient interaction with synthetically generated image embeddings, avoiding pixel-space processing and enabling perfect training. Additionally, a Perceiver Resampler component facilitates cross-attention between VQ tokens and language tokens in VLM, helping to achieve effective multimodal representations.
When evaluating synthetic images for VLM training, Synth2 significantly improves performance over baselines, even with a smaller volume of human-annotated images. Synthetic images effectively replace real ones, enhancing the capabilities of VLM. Synth2 also outperforms state-of-the-art methods such as ITIT and DC, achieving competitive results with reduced use of data and computational resources. This highlights the effectiveness and efficiency of Synth2 in improving VLM performance.
In conclusion, Google DeepMind researchers proposed Synth2, which uses synthetic image-text pairs to improve VLM training. The results show improved VLM performance compared to baselines, with increased data efficiency and scalability. This method offers customization for specific domains and addresses resource-intensive data acquisition challenges. The findings highlight the potential of synthetic data generation to improve visual language understanding, suggesting avenues for further exploration.
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Asjad is an internal consultant at Marktechpost. He is pursuing B.tech in Mechanical Engineering at Indian Institute of technology, Kharagpur. Asjad is a machine learning and deep learning enthusiast who is always researching applications of machine learning in healthcare.
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