Towards Open-Ended Visual Recognition with Large Language Model (2311.08400v1)

Abstract: Localizing and recognizing objects in the open-ended physical world poses a long-standing challenge within the domain of machine perception. Recent methods have endeavored to address the issue by employing a class-agnostic mask (or box) proposal model, complemented by an open-vocabulary classifier (e.g., CLIP) using pre-extracted text embeddings. However, it is worth noting that these open-vocabulary recognition models still exhibit limitations in practical applications. On one hand, they rely on the provision of class names during testing, where the recognition performance heavily depends on this predefined set of semantic classes by users. On the other hand, when training with multiple datasets, human intervention is required to alleviate the label definition conflict between them. In this paper, we introduce the OmniScient Model (OSM), a novel LLM based mask classifier, as a straightforward and effective solution to the aforementioned challenges. Specifically, OSM predicts class labels in a generative manner, thus removing the supply of class names during both training and testing. It also enables cross-dataset training without any human interference, exhibiting robust generalization capabilities due to the world knowledge acquired from the LLM. By combining OSM with an off-the-shelf mask proposal model, we present promising results on various benchmarks, and demonstrate its effectiveness in handling novel concepts. Code/model are available at https://github.com/bytedance/OmniScient-Model.

• The paper presents the OmniScient Model (OSM), a novel approach that eliminates the need for user-specified class names by leveraging a generative LLM.
• OSM employs a hybrid architecture combining CLIP-ViT, MaskQ-Former, and Mode Query to adaptively extract high-resolution features from images.
• OSM achieves robust cross-dataset generalization and outperforms existing methods on segmentation benchmarks under zero-shot open-vocabulary conditions.

Insights into "Towards Open-Ended Visual Recognition with LLM"

In the academic paper "Towards Open-Ended Visual Recognition with LLM," the authors propose a novel approach to visual recognition by introducing the OmniScient Model (OSM). This model aims to address longstanding challenges associated with open-ended object localization and classification in real-world environments, independent of predefined semantic vocabularies.

Contextual Background and Challenges

Traditional object detection models operate on the basis of a predefined vocabulary necessitating users to specify class names during testing, which inherently restricts their adaptability to novel or unforeseen concepts. The integration of multiple datasets also poses challenges due to label conflicts which require human intervention for resolution. Thus, current open-vocabulary recognition models, despite leveraging class-agnostic mask proposals combined with pre-extracted text embeddings from Vision-LLMs (VLMs) like CLIP, fall short in truly open-ended scenarios.

The OmniScient Model (OSM)

OSM leverages a generative LLM under a framework that transcends these limitations by directly predicting class labels in a generative manner. This eliminates the requirement for user-specified class names during both training and testing. Consequently, OSM supports cross-dataset training seamlessly, utilizing the extensive world knowledge encapsulated within LLMs to enhance its generalization capabilities.

Architectural Composition and Methodology

OSM is comprised of several key components:

• CLIP-ViT: A frozen Visual Transformer backbone utilized for high-resolution feature extraction, applying a sliding-window technique to maintain efficacy with increased input resolutions.
• MaskQ-Former: Inspired by the Q-Former architecture, this component adapts image features based on mask proposals, ensuring the visual summaries are precise and context-appropriate.
• Mode Query: A unique mechanism that enhances the flexibility of predictions by accommodating vocabulary-specific as well as vocabulary-agnostic settings.

Evaluation and Results

The authors evaluate OSM across multiple publicly available segmentation datasets (COCO, LVIS, ADE20K, Cityscapes, ADE-847, and PC-459), revealing insightful outcomes. OSM achieves high accuracy without dependency on predefined vocabulary, substantiating its robust generalization and alignment capabilities. Specifically, it consistently outperforms other discriminative text-embedding based methods, achieving competitive scores even when evaluated under zero-shot open-vocabulary conditions.

Implications and Future Directions

OSM's framework demonstrates significant promise in real-world applications where classes are not predefined. By restructuring the recognition task into a generative problem, it conveniently accommodates cross-dataset efficacy and effectively navigates semantic conflicts that may arise. This paradigm shift suggests an extension beyond traditional recognition, encouraging further exploration into the confluence of generative models and vision-Language integration to unlock new potentials in computer vision.

Looking forward, one could expect further advancements in enhancing the generative capabilities of LLMs to refine recognition and generalization in diverse and evolving datasets. There is also room for exploring optimizations that reconcile the trade-off between accuracy and overfitting concerning training vocabulary. The inclusion of diverse datasets, including part-level and box-level recognitions, expands the semantic depth and application range of models like OSM.

Conclusion

In summary, the OmniScient Model presents a pioneering approach to open-ended visual recognition by cleverly leveraging LLMs to eliminate the shackles of predefined vocabularies. This research holds substantial promise for the domain of machine perception, particularly in enhancing robustness and generalization in unconfined, real-world image recognition tasks. As AI research progresses, such developments may pave the way toward increasingly intuitive and comprehensive recognition systems.