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

Tiancheng Zhao, Tianqi Zhang, Mingwei Zhu, Haozhan Shen, Kyusong Lee, Xiaopeng Lu, Jianwei Yin

arXiv:2207.00221v2cs.CVcs.CLcs.LG

TL;DR

Fine-tuned downstream-task accuracy alone reveals little about VLP models’ strengths and weaknesses. VL-CheckList evaluates image-text matching across object, attribute, and relation capabilities, then analyzes seven popular models. Its results reveal fine-grained differences not visible from downstream-task-only evaluation.

  • Problem

    Fine-tuned downstream-task evaluation gives limited insight into VLP models’ strengths and weaknesses, and can be difficult to interpret or compare across models.

    Source: §1 · p. 1

  • Method

    VL-CheckList evaluates image-text matching by organizing VLP capabilities into object, attribute, and relation categories and using linguistic-aware hard negatives.

    Source: §1 · pp. 1–2

  • Results

    The analysis of seven popular VLP models reveals fine-grained differences that are difficult to obtain from downstream-task scores alone.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The framework provides explainable, fine-grained evaluation reports intended to support deeper understanding and ideas for improving VLP models.

    Source: §1 · pp. 1–2

Abstract

from arXiv · show

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.

1 Introduction

Fine-tuned downstream-task scores provide limited insight into VLP models’ strengths and weaknesses. VL-CheckList instead evaluates fundamental image-text capabilities through a fine-grained taxonomy and reports analyses of seven popular models.

1 Introduction

  • Downstream-task evaluation offers limited interpretability, makes comparisons difficult across different task choices, and may overestimate real-world performance because of data-distribution bias.The introduction also notes uncertainty about robustness to input changes such as replacing a verb with a synonym.Source: §1 · p. 1
  • VL-CheckList evaluates image-text matching and organizes VLP capabilities into object, attribute, and relation categories, further divided into fine-grained variables.The paper gives color, material, and size as examples of attribute variables.Source: §1 · pp. 1–2
  • A linguistic-aware negative-sampling strategy creates hard negatives by making minor changes to input text, testing models’ discriminative power.VL-CheckList is also implemented as a toolbox for use in evaluation pipelines.Source: §1 · p. 2
  • The evaluation reveals fine-grained insights about seven popular VLP models that are difficult to obtain from downstream-task scores alone.The study uses four datasets to generate capability-specific test sets.Source: §1 · p. 2
  • The paper presents VL-CheckList as an explainable framework for generating fine-grained, disentangled VLP evaluation reports.It also provides an open-source package intended for community use.Source: §1 · p. 2

2 Related Work

Prior work includes fine-grained evaluation tools for machine learning and analyses of VLP models. This paper distinguishes itself by evaluating VLP models across object, attribute, and relation capabilities at scale.

2 Related Work

  • Fine-grained machine-learning evaluation is motivated by the difficulty of understanding model strengths and weaknesses from benchmark performance alone.Related work cites evidence that state-of-the-art systems may remain insufficient in real-world applications (Ribeiro et al., 2020).Source: §2 · p. 2
  • Prior evaluation tools include CheckList for NLP, Vision CheckList for visual transformations, and TIDE for object-detection error analysis.The related work also discusses Dynabench as a dynamic benchmark dataset.Source: §2 · p. 2
  • Earlier VLP analyses examined training variables and components, while another study found verb understanding harder than understanding objects and subjects.The cited analyses cover datasets, attention, objectives, encoders, fusion, and architecture (Hendricks et al., 2021; Hendricks and Nematzadeh, 2021; Dou et al., 2022).Source: §2 · p. 3
  • The paper characterizes its study as the first, to its knowledge, to analyze VLP models through object, attribute, and relation capabilities and test seven models on four corpora.Source: §2 · p. 3

3 VL-CheckList

VL-CheckList uses image-text matching to compare vision-language models without downstream fine-tuning, then profiles object, attribute, and relation capabilities with targeted tests. It creates rewritten negatives and reports results by evaluation aspect.

3 VL-CheckList

  • Image-text matching (ITM) scores whether an image matches a sentence, enabling model-agnostic comparison without downstream fine-tuning.The paper notes that ITM is commonly used across VLP models and applies to different fusion architectures.Source: §3 · p. 3

3.1 Taxonomy of VL-CheckList

  • The taxonomy tests object recognition and location/size invariance, attribute changes, and relations divided into spatial and action types.Attribute categories include color, material, size, state, and action; relation tests replace a predicate between two objects.Source: §3.1 · pp. 3–4

3.2 Negative sampling generation

  • Hard negatives are generated by rewriting descriptions, changing one image-related attribute, relation, or semantic role to challenge model discrimination.For VG, candidate replacements are selected by type and cosine similarity, with a 0.5 threshold; some unsuitable caption pairs are manually modified.Source: §3.2 · p. 4

3.3 Visualization & Metric

  • The report combines quantitative tables, taxonomy-based radar charts, Grad-CAM attention maps, and intermediate metrics; accuracy treats an original caption scoring above its negatives as positive.Figure 3 provides positive and negative sample examples for each evaluation aspect.Source: §3.3 · pp. 4–5, Figure 3 · p. 5

4 Experiment Setup

The experiments compare seven VLP methods using object, attribute, and relation evaluation. They include dual-encoder, region-based cross-attention, and end-to-end model families.

4 Experiment Setup

  • The experiments compare seven state-of-the-art VLP methods using the proposed evaluation.Source: §4 · p. 5

4.1 Dual-Encoders

  • CLIP is a dual-encoder model that independently encodes image and text, with an ITM score based on cosine distance between embeddings.Source: §4.1 · p. 5

4.2 Region-based Cross-Attention Models

  • Region-based models use off-the-shelf object detectors; LXMERT, UNITER, and OSCAR differ in their transformer designs and pretraining features.LXMERT uses separate object-relation and text encoders plus a cross-modal encoder; UNITER uses a shared transformer, while OSCAR adds object tags as input features.Source: §4.2 · p. 5

4.3 End-to-end Cross-Attention Models

  • End-to-end models encode images directly: ViLT combines image patches and words in one transformer, while ALBEF fuses separately encoded modalities with a cross-modal transformer.TCL is described as an improved ALBEF with three contrastive modules; Figure 4 presents radar charts for the compared models.Source: §4.3 · pp. 5–6, Figure 4 · p. 6

5 Results and Analysis

Across the evaluated categories, ViLT achieves the best overall score, while relation and attribute understanding are more challenging than object understanding. The analyses also identify model-family differences and specific weaknesses across object, relation, and attribute tests.

5 Results and Analysis

  • ViLT achieves the best overall score, while Relation and Attribute are more challenging than Object.Source: §5 · p. 6, Table 2 · p. 7

5.1 Evaluation of Object

  • CLIP drops 13.12% from Large to Small and 12.01% from Center to Margin; E2E location drops average 2.82% versus 6.44% for region-based models.The authors speculate that detectors can miss marginal objects; Figure 5 contrasts a missed car detection with ALBEF focusing on the car’s true position.Source: §5.1 · p. 6, Table 3 · p. 7, Figure 5 · p. 8

5.2 Evaluation of Relation

  • 74.07% Action and 49.1% Spatial: CLIP is best on Action but worst on Spatial; region-based location degradation is 3.44% on Relation.The analysis also relates relation performance more closely to text-level information than to image-level changes such as location and size.Source: §5.2 · pp. 6, 8, Table 4 · p. 7

5.3 Evaluation of Attribute

  • E2E models score 9.42% higher on average than region-based models on both image and text levels; dual-architecture models score 1.97% below region-based and 11.39% below E2E models.Source: Table 5 · p. 7, §5.3 · p. 8

5.2 Evaluation of Relation

  • For “working,” OSCAR and ALBEF attend reasonably to the hands and keyboard, LXMERT attends less there, and ViLT attends to paper and books rather than the person.Source: Figure 6 · p. 8, §5.2 · p. 8

5.3 Evaluation of Attribute

  • All models score relatively high on Color and Material, low on Size, State, and Action, and worst on Size; size terms lack a fixed visual scale, and some actions require frame sequences.Source: §5.3 · p. 8

6 Conclusion

The paper uses VL-CheckList to analyze VLP models across objects, attributes, and relations, benchmarking seven models with four corpora and a carefully constructed taxonomy. The experiments highlight object size and location as critical and relation recognition as especially challenging.

6 Conclusion

  • VL-CheckList analyzes VLP models from three perspectives: objects, attributes, and relations.Source: §6 · p. 8
  • The study uses four corpora and a carefully constructed taxonomy to produce fine-grained benchmark results for seven popular VLP models.Source: §6 · p. 8
  • Object size and location are critical, while relation recognition is one of the most challenging capabilities for VLP models.Source: §6 · p. 9

7 Limitations

VL-CheckList aims to quickly evaluate robustness to linguistic variations using ITM. This work does not yet explain why high ITM scores can coincide with lower downstream-task scores.

7 Limitations

  • VL-CheckList targets quick evaluation of robustness to linguistic variations using ITM.Source: §7 · p. 9
  • The study does not yet investigate why models with high ITM scores sometimes achieve lower downstream-task scores.Source: §7 · p. 9
  • For a given task, fine-tuning is more critical to model performance, and a detailed study is left to future work.Source: §7 · p. 9

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