Visual reasoning is dominated by end-to-end neural networks scaled to
billions of model parameters and training examples. However, even the largest
models struggle with compositional reasoning, generalization, fine-grained
spatial and temporal reasoning, and counting. Visual reasoning with large
language models (LLMs) as controllers can, in principle, address these
limitations by decomposing the task and solving subtasks by orchestrating a set
of (visual) tools. Recently, these models achieved great performance on tasks
such as compositional visual question answering, visual grounding, and video
temporal reasoning. Nevertheless, in their current form, these models heavily
rely on human engineering of in-context examples in the prompt, which are often
dataset- and task-specific and require significant labor by highly skilled
programmers. In this work, we present a framework that mitigates these issues
by introducing spatially and temporally abstract routines and by leveraging a
small number of labeled examples to automatically generate in-context examples,
thereby avoiding human-created in-context examples. On a number of visual
reasoning tasks, we show that our framework leads to consistent gains in
performance, makes LLMs as controllers setup more robust, and removes the need
for human engineering of in-context examples.
Visual Reasoning with Large Language Models
Visual reasoning is a complex task that involves analyzing and understanding visual information to answer questions, make predictions, or solve problems. While end-to-end neural networks have shown remarkable success in this area, they still struggle with certain aspects such as compositional reasoning, generalization, fine-grained spatial and temporal reasoning, and counting.
To address these limitations, researchers have turned to large language models (LLMs) as controllers for visual reasoning. By decomposing the task and using a set of visual tools, LLMs can potentially overcome the challenges faced by traditional neural networks. Recent advancements in this field have seen LLMs achieve impressive performance on tasks like compositional visual question answering, visual grounding, and video temporal reasoning.
However, current LLMs heavily rely on human-engineered in-context examples in the prompt, which are often specific to the dataset and task at hand. This requires significant effort from skilled programmers and limits the scalability of these models. To overcome this challenge, a new framework has been proposed that introduces spatially and temporally abstract routines and leverages a small number of labeled examples to automatically generate in-context examples.
By automating the generation of in-context examples, the need for human engineering is eliminated, making LLMs as controllers more robust and scalable. This framework has shown consistent gains in performance across multiple visual reasoning tasks, highlighting its effectiveness in addressing the limitations of current models.
Multi-disciplinary Nature of the Concepts
The concepts discussed in this article encompass multiple disciplines such as computer vision, natural language processing, and artificial intelligence. Visual reasoning requires understanding both visual information and textual prompts, necessitating the integration of computer vision and natural language processing techniques. The use of large language models as controllers further combines the fields of natural language processing and machine learning.
The framework presented in this work also incorporates elements of automation and algorithmic design, which are fundamental to computer science. By automating the generation of in-context examples, the framework reduces the dependency on human expertise and significantly improves efficiency in developing visual reasoning models.
Furthermore, the framework’s emphasis on addressing the limitations of current models through decomposition of tasks and leveraging spatially and temporally abstract routines highlights the importance of problem-solving strategies, cognitive science, and human-computer interaction. These disciplines play a crucial role in understanding how humans reason visually and designing effective approaches to replicate this process in machines.
- Overall, the advancement of visual reasoning with large language models showcases the multi-disciplinary nature of solving complex problems at the intersection of computer vision, natural language processing, machine learning, algorithmic design, cognitive science, and human-computer interaction.
- Through continued research and development, we can expect further improvements in the performance, scalability, and applicability of visual reasoning models. The integration of additional domains, such as reinforcement learning or knowledge graphs, may enhance the capabilities of these models and enable them to tackle more challenging tasks.
- This new framework serves as a stepping stone towards more autonomous visual reasoning systems that can generalize across diverse domains and effectively understand and interpret visual information in real-world scenarios.