Large Language Models are Good Prompt Learners for Low-Shot Image Classification
(2312.04076)Abstract
Low-shot image classification, where training images are limited or inaccessible, has benefited from recent progress on pre-trained vision-language (VL) models with strong generalizability, e.g. CLIP. Prompt learning methods built with VL models generate text features from the class names that only have confined class-specific information. LLMs, with their vast encyclopedic knowledge, emerge as the complement. Thus, in this paper, we discuss the integration of LLMs to enhance pre-trained VL models, specifically on low-shot classification. However, the domain gap between language and vision blocks the direct application of LLMs. Thus, we propose LLaMP, LLMs as Prompt learners, that produces adaptive prompts for the CLIP text encoder, establishing it as the connecting bridge. Experiments show that, compared with other state-of-the-art prompt learning methods, LLaMP yields better performance on both zero-shot generalization and few-shot image classification, over a spectrum of 11 datasets. Code will be made available at: https://github.com/zhaohengz/LLaMP.
Overview
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The paper addresses the challenge of low-shot image classification and proposes using LLMs to provide encyclopedic knowledge to aid in the task.
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A new framework called LLaMP utilizes LLMs as prompt learners to enhance the capabilities of pre-trained Vision-Language models like CLIP.
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LLaMP uses a 'knowledge cache' and a hybrid tuning strategy that involves prompt learning and low-rank model adaptation to avoid full model training.
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Experimental results show that LLaMP outperforms other models, especially in fine-grained image classification tasks, and with very few training images.
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Future work could explore integrating language knowledge earlier in the vision encoding process to further improve low-shot image classification.
Exploring Low-Shot Image Classification with LLMs
Harnessing Encyclopedic Knowledge
In the realm of AI and machine learning, the ability to accurately classify images using only a few examples, known as low-shot image classification, presents a significant challenge. Traditional methods often struggle due to a lack of comprehensive training data. This is where LLMs come into play. These models, which include well-known examples such as GPT-4 and LLaMA, are trained on extensive text corpora and thus can generate rich, encyclopedic knowledge. However, effectively merging this knowledge with the visual data necessary for image classification demands a nuanced approach to overcome the domain gap.
Bridging Language and Vision
The solution proposed in recent research is an innovative framework named LLaMP, which stands for LLMs as Prompt learners. It helps bridge the gap between the language knowledge from LLMs and the visual understanding of pre-trained Vision-Language (VL) models such as CLIP. The key obstacle is ensuring LLMs' rich textual knowledge can be effectively communicated to vision models' image processing capabilities.
LLaMP generates adaptive prompts for the CLIP text encoder. It does so by first querying the LLM with a text prompt about a specific object category. Then, LLaMP extracts relevant noun phrases from the LLM's response and integrates these descriptive terms into new text prompts. These prompts are designed to enhance the CLIP model's classification process, providing more detailed descriptions and context for each image category.
Avoiding Full Model Training
One standout aspect of LLaMP is that it does not require training the entire LLM, which would be computationally expensive and impractical given the size of such models. Instead, it uses a technique called "knowledge cache" that leverages the LLM's ability to generate informative text descriptions. By creating a cache of these descriptions, LLaMP can quickly and efficiently produce class-specific text feature vectors, making the adaptation process much more feasible.
Additionally, LLaMP employs a hybrid tuning strategy that utilizes both prompt learning and LoRA—a low-rank model adaptation technique—on the vision encoder. This tailored approach efficiently tunes the image encoder while capitalizing on the descriptive power of text prompts informed by rich LLM knowledge.
Evaluation and Results
Experimentation demonstrates that LLaMP outperforms existing state-of-the-art models across an array of datasets, addressing tasks such as general image classification, fine-grained object recognition, and even satellite image interpretation. It performs particularly well in fine-grained datasets that require an acute level of detail for discrimination—demonstrating the value of the encyclopedic knowledge it leverages.
In low-shot scenarios, LLaMP displays impressive improvements in recognizing objects with as few as one to sixteen training images. This ability showcases not only its efficacy but also its potential practicality where collecting large image datasets is challenging or even infeasible. Furthermore, it embodies an intelligent combination of learned text prompts and visual signals, advancing general versatility and applicability.
Future Directions
However, LLaMP is not without its limitations. The research suggests that additional gains could be achieved by integrating language domain knowledge during earlier stages of the vision encoding process. This implies there is still untapped potential in the synergy between language and vision that could further refine low-shot image classification models.
In conclusion, LLaMP marks a noteworthy leap in effectively utilizing the extensive knowledge contained within LLMs to enhance low-shot image classification. Its ability to adaptively marry encyclopedic language knowledge with vision models paves the way for more robust and versatile AI systems capable of understanding and classifying visual data with minimal examples.
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