Previously, with the adoption of computer vision, their studies were not content with just scanning 2D arrays of flat “patterns.” Rather, they sought to understand images as projections of 3D scenes. Initially, the researchers created several intermediate tasks to assist in this search. These included learning about optical properties such as reflectance, three-dimensional primitives using multi-view reasoning, geometric reasoning using depth estimation, visual correspondence, recognition, keypoint basing for affordability, and intrinsic images for forensic science. Studies have led to the construction of new tasks, largely articulated in natural language, in the current era of large language models (LLMs), emphasizing the vision-language relationship learned by multimodal LLMs and less on such perceptual tasks. . This could be due to the intrinsic imprecision of language, which makes it difficult to use to mediate many conventional computer vision tasks (e.g., identifying a key spatial point through language is difficult).
This study, a collaborative effort by researchers at the University of Pennsylvania, the University of Washington, the Allen Institute for ai, the University of California, and Columbia University, delves into crucial but overlooked aspects of perception. visual in the evaluation of multimodal LLM. Despite their widespread use as evaluation metrics for fundamental models such as GPT-4V and Gemini-Pro, many of these standards combine perception with linguistic understanding and reasoning. This work reveals that a “blind” GPT-4 performs well on these “multimodal tasks” when a dense task-independent title is used instead of the image.
The study introduces Blink, a new benchmark for multimodal language models (LLM) that focuses exclusively on basic visual perception skills not addressed in other assessments. From basic pattern matching to intermediate reasoning and advanced visual understanding (such as visual similarity), Blink's fourteen classic computer vision challenges cover a wide range. Image assignments are deliberately challenging and are designed to require a genuine understanding of the image content rather than relying on superficial labels.
The researchers revamped each previous task by turning it into a question-and-answer session with pictures or textual answers. Blink has 3,800 questions and 7,300 photos, and each question potentially contains many images selected from multiple data sets. These photographs depict views inside and outside of homes, cities and nature. Humans or data sets are used to generate the questions and options. Usually, a human can answer all the questions (except the IQ test) in the blink of an eye.
At Blink, the team extensively evaluates seventeen multimodal LLMs ranging in size from seven to thirty-four bits. Contrary to popular belief, these problems are quite easy for humans to solve (95.70% average accuracy). However, current equipment finds them incredibly challenging, with the GPT-4V model only achieving an average accuracy of 51.26%. This is 44.44% poorer than humans and 13.17% better than random guessing. Additionally, Blink compared multimodal LLMs with expert vision models and found that the latter perform substantially better. In visual correspondence estimation, for example, the expert outperforms GPT-4V by 62.8%, relative depth estimation by 38.7%, and multiview reasoning by 34.6% in terms of absolute accuracy. .
The research findings call into question previous estimates of the perceptual capabilities of multimodal LLMs, suggesting that they may have been exaggerated. Additionally, these models could potentially benefit from incorporating insights from specialized models that excel in specific domains. The team envisions Blink as a valuable platform for exploring how multimodal LLMs can integrate more conventional perceptual ideas with their next-generation generation capabilities, paving the way for future advances in this field.
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Dhanshree Shenwai is a Computer Science Engineer and has good experience in FinTech companies covering Finance, Cards & Payments and Banking with a keen interest in ai applications. He is excited to explore new technologies and advancements in today's evolving world that makes life easier for everyone.
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