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VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations (2207.00221v2)

Published 1 Jul 2022 in cs.CV, cs.CL, and cs.LG

Abstract: Vision-Language Pretraining (VLP) models have recently successfully facilitated many cross-modal downstream tasks. Most existing works evaluated their systems by comparing the fine-tuned downstream task performance. However, only average downstream task accuracy provides little information about the pros and cons of each VLP method, let alone provides insights on how the community can improve the systems in the future. Inspired by the CheckList for testing natural language processing, we exploit VL-CheckList, a novel framework to understand the capabilities of VLP models. The proposed method divides the image-texting ability of a VLP model into three categories: objects, attributes, and relations, and uses a novel taxonomy to further break down these three aspects. We conduct comprehensive studies to analyze seven recently popular VLP models via the proposed framework. Results confirm the effectiveness of the proposed method by revealing fine-grained differences among the compared models that were not visible from downstream task-only evaluation. Further results show promising research direction in building better VLP models. Our data and code are available at: https://github.com/om-ai-lab/VL-CheckList.

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Evaluation of Vision-LLMs with VL-CheckList

The paper "VL-CheckList: Evaluating Pre-trained Vision-LLMs with Objects, Attributes and Relations" presents an innovative framework designed to evaluate Vision-Language Pretraining (VLP) models using a detailed, explainable approach. Vision-LLMs have become instrumental in advancing cross-modal tasks but assessing their capabilities through mere downstream task performance has been insufficient. This framework, inspired by the CheckList methodology for NLP, addresses the need for a deeper and more nuanced understanding of these models.

Framework Overview

VL-CheckList divides the image-text interaction capabilities of VLP models into three primary categories: objects, attributes, and relations. This taxonomy allows for a granular analysis of each model’s strengths and limitations. The evaluation leverages structured negative sampling techniques to test image-text matching (ITM) capabilities across these categories. By focusing on intrinsic aspects of image-text alignment, the framework sidesteps the complexities of downstream task performance evaluation, providing clearer insights into VLP model behaviors.

Unlike traditional evaluation methods that rely solely on downstream task scores, the VL-CheckList framework highlights how models handle each component of the image-text pairing process. For example, it considers how models discern objects regardless of location and size, detect attributes like color and material, and recognize relationships among objects. The meticulous design of the negative sampling process ensures that the framework robustly tests a model’s ability to differentiate between closely matched image-text pairs.

Key Findings

The paper evaluates seven widely-used VLP models using the VL-CheckList framework. Key observations include:

  • Objects: The capability to recognize objects varies significantly with object size and location. Models show improved accuracy with larger, centrally-located objects compared to smaller, peripheral ones. End-to-end models, such as ViLT, demonstrate enhanced robustness to object placement variations compared to region-based models like LXMERT and UNITER.
  • Attributes: Attributes pose substantial challenges due to inherent subjectivity and complex visual cues. The models show varying degrees of success across different attributes, achieving better results for straightforward visual attributes like color compared with more abstract concepts like material and size.
  • Relations: Understanding object relationships remains a complex task, with action-based relations being particularly difficult due to the lack of dynamic information in static images. Region-based models tend to perform better at recognizing spatial relationships than end-to-end counterparts.

The framework reveals that traditional evaluation methods fail to capture these nuanced differences in capability. Consequently, VL-CheckList provides a more comprehensive assessment of model strengths and limitations, offering a path to targeted improvements in VLP systems.

Implications and Future Directions

By exposing the fine-grained capabilities of VLP models, VL-CheckList identifies specific areas for improvement in model design and training. The framework suggests avenues for enhancing VLP models, such as developing better methods for attribute detection and relationship comprehension. Additionally, the results emphasize the importance of diverse training data to cover a broad spectrum of real-world scenarios.

This work paves the way for the creation of more sophisticated and interpretable VLP models. Future research may explore extending the framework to include additional evaluation aspects, refining the taxonomy to cover more nuanced interactions, or integrating temporal dimensions for video-based applications. Ultimately, such advancements could lead to VLP models with more accurate, reliable, and adaptable performance across a range of applications.

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Authors (7)
  1. Tiancheng Zhao (48 papers)
  2. Tianqi Zhang (17 papers)
  3. Mingwei Zhu (10 papers)
  4. Haozhan Shen (8 papers)
  5. Kyusong Lee (16 papers)
  6. Xiaopeng Lu (9 papers)
  7. Jianwei Yin (71 papers)
Citations (76)
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