GPT4Vis: What Can GPT-4 Do for Zero-shot Visual Recognition? (2311.15732v2)

Abstract: This paper does not present a novel method. Instead, it delves into an essential, yet must-know baseline in light of the latest advancements in Generative Artificial Intelligence (GenAI): the utilization of GPT-4 for visual understanding. Our study centers on the evaluation of GPT-4's linguistic and visual capabilities in zero-shot visual recognition tasks: Firstly, we explore the potential of its generated rich textual descriptions across various categories to enhance recognition performance without any training. Secondly, we evaluate GPT-4's visual proficiency in directly recognizing diverse visual content. We conducted extensive experiments to systematically evaluate GPT-4's performance across images, videos, and point clouds, using 16 benchmark datasets to measure top-1 and top-5 accuracy. Our findings show that GPT-4, enhanced with rich linguistic descriptions, significantly improves zero-shot recognition, offering an average top-1 accuracy increase of 7% across all datasets. GPT-4 excels in visual recognition, outshining OpenAI-CLIP's ViT-L and rivaling EVA-CLIP's ViT-E, particularly in video datasets HMDB-51 and UCF-101, where it leads by 22% and 9%, respectively. We hope this research contributes valuable data points and experience for future studies. We release our code at https://github.com/whwu95/GPT4Vis.

Analyzing GPT-4's Contribution to Zero-shot Visual Recognition

The research presented in the paper titled "GPT4Vis: What Can GPT-4 Do for Zero-shot Visual Recognition?" critically examines the utility of GPT-4, specifically its vision-enabled version, to advance zero-shot visual recognition tasks. This paper is not aimed at introducing new methodologies but rather at assessing a significant baseline in the context of Generative Artificial Intelligence (GenAI). By leveraging GPT-4's robust capabilities, this research endeavors to dissect its impact on zero-shot visual recognition across different modalities, namely images, videos, and point clouds.

Key Findings

The research primarily investigates two dimensions of GPT-4's competence: linguistic and visual capabilities, in contexts devoid of training or fine-tuning. By utilizing 16 benchmark datasets, the paper explores how these capabilities can be harnessed to achieve enhanced recognition performance, notably without prior exposure to these datasets.

Linguistic Capability: The paper employs GPT-4 to generate enriched textual descriptions for category names, which are then compared with embeddings derived from visual content using CLIP models. This approach yields noticeable performance gains across multiple datasets. The experimental results highlight significant improvements, with an average top-1 accuracy increase of 7% facilitated by richer linguistic descriptions.

Visual Capability: GPT-4's direct visual processing, via its multimodal API, shows promise in accurately diagnosing visual content. Notably, GPT-4V demonstrates superior performance in video datasets, such as HMDB-51 and UCF-101, outperforming prominent models like OpenAI-CLIP's ViT-L and challenging the larger EVA-CLIP model. In video recognition tasks, GPT-4's visual reasoning shows marked improvements over baseline CLIP models, particularly in scene-based recognition, although it faces challenges in datasets requiring temporal analysis like Something-Something V1.

Methodology and Experimentation

The methodology pivots around both exploring and quantifying GPT-4's performance utilizing a broad spectrum of descriptive prompts generated by the model itself. Furthermore, it evaluates GPT-4V's standalone visual prediction competencies using extensive datasets that span 11 image datasets, 4 video datasets, and one point cloud dataset.

Experiments reveal that employing GPT-4 for generating detailed category descriptions leads to significant recognition gains across various classification tasks. For certain datasets, such as EuroSAT and RAF-DB, performance improvements are exceedingly profound, highlighting GPT-4's capability to surpass traditional CLIP baseline accuracies notably. Conversely, GPT-4V's visual capability assessments show it adeptly handles straightforward visual categories but struggles with tasks requiring longitudinal human or object actions.

Implications

The implications of this paper are manifold. Primarily, it underscores the potential of using pre-trained generative models like GPT-4 to enhance zero-shot learning outcomes. This is particularly relevant as it presents a viable alternative to existing visual LLMs (VLMs), showcasing that LLMs with visual capabilities can significantly elevate performance in tasks traditionally dominated by models trained on vast visual datasets.

Moreover, the findings encourage further exploration into enriching prompt engineering for multimodal models, suggesting a future direction where tailored prompts could yield even more remarkable outcomes than observed in this paper.

Conclusion and Future Directions

The research solidifies GPT-4's prowess as a crucial baseline but also delineates its limitations, particularly in complex temporal visual tasks. The potential for utilizing generative models in visual recognition remains vast and promises both academic inquiry and practical application to solve broader AI challenges.

Future work could focus on addressing GPT-4's limitations in temporal reasoning by developing hybrid models that integrate more sophisticated temporal encoding mechanisms. Additionally, extending quantitative analyses to encompass other visual tasks such as object detection would provide broader insights into the applications of multimodal generative AI technologies. As this research trailblazes, it lays the groundwork for invigorating discussions and developments in multimodal AI capabilities.