Vision Language Models (VLMs) leverage Large Language Models’ strength to comprehend visual data, demonstrating capability in visual question answering and optical character recognition. A study by Tsinghua University and Zhipu AI introduces Chain of Manipulations (CoM) to enable VLMs for visual reasoning, leading to competitive performance on various benchmarks and highlighting potential for accelerated VLM development. [50 words]
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Enhancing Vision-Language Models with Chain of Manipulations: A Leap Towards Faithful Visual Reasoning and Error Traceability
Practical Solutions and Value Highlights
Big Vision Language Models (VLMs) trained to comprehend vision have shown viability in broad scenarios like visual question answering, visual grounding, and optical character recognition, capitalizing on the strength of Large Language Models (LLMs) in general knowledge of the world.
Humans mark or process the provided photos for convenience and rigor to address the intricate visual challenges; this process is known as manipulation. In the initial training round, most VLMs learned a plethora of intrinsic multimodal abilities, such as grounding boxes and word recognition. Models can execute evidential visual reasoning for issue-solving by mimicking basic human-like behaviors (e.g., cropping, zooming in). However, this approach for model training is not used due to two significant obstacles.
The first and foremost requirement is producing copious amounts of training data using the evidential visual reasoning paths from preexisting language instruction-answer pairs.
To build general and reasoning multimodal skills, they offer CogCoM, a 17B VLM trained with a memory-based compatible architecture and a fusion of four categories of data based on the produced data. To arrive at its conclusion, the model uses reasoning to actively adopt various modifications to gain visual contents and referential regions. The outcomes demonstrate that methodology consistently provides competitive or better performance.
The researchers believe that the suggested visual reasoning process may accelerate VLM development in the area of complicated visual problem-solving. Furthermore, the data generation system that has been introduced has the potential to be used in various training scenarios, which could help advance data-driven machine learning.
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