[Paper Review] RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning

Xiaojian Ma, Weili Nie+6 more|arXiv (Cornell University)|Apr 24, 2022

Multimodal Machine Learning Applications7 citations

TL;DR

This paper proposes RelViT, a concept-guided Vision Transformer that enhances visual relational reasoning by introducing a concept-feature dictionary to enable concept-guided global and local auxiliary tasks. The method significantly improves performance on HICO and GQA benchmarks, achieving 16% and 13% gains on original splits and 43% and 18% on systematic generalization splits, respectively.

ABSTRACT

Reasoning about visual relationships is central to how humans interpret the visual world. This task remains challenging for current deep learning algorithms since it requires addressing three key technical problems jointly: 1) identifying object entities and their properties, 2) inferring semantic relations between pairs of entities, and 3) generalizing to novel object-relation combinations, i.e., systematic generalization. In this work, we use vision transformers (ViTs) as our base model for visual reasoning and make better use of concepts defined as object entities and their relations to improve the reasoning ability of ViTs. Specifically, we introduce a novel concept-feature dictionary to allow flexible image feature retrieval at training time with concept keys. This dictionary enables two new concept-guided auxiliary tasks: 1) a global task for promoting relational reasoning, and 2) a local task for facilitating semantic object-centric correspondence learning. To examine the systematic generalization of visual reasoning models, we introduce systematic splits for the standard HICO and GQA benchmarks. We show the resulting model, Concept-guided Vision Transformer (or RelViT for short) significantly outperforms prior approaches on HICO and GQA by 16% and 13% in the original split, and by 43% and 18% in the systematic split. Our ablation analyses also reveal our model's compatibility with multiple ViT variants and robustness to hyper-parameters.

Motivation & Objective

• Address the challenge of systematic generalization in visual relational reasoning on real-world datasets.
• Improve vision transformer performance by leveraging concepts (objects and relations) as inductive biases.
• Enable better object-centric and relational representation learning without architectural modifications.
• Develop a plug-and-play framework compatible with existing ViT training pipelines for improved reasoning.
• Evaluate generalization on novel object-relation combinations using new systematic splits for HICO and GQA.

Proposed method

• Introduce a concept-feature dictionary where keys are concepts (e.g., object labels) and values are queues of image features sharing that concept.
• Use the dictionary to enable flexible, concept-based image feature retrieval during training.
• Design a global concept-guided task to cluster images with the same concept, promoting semantically consistent relational representations.
• Design a local concept-guided task to learn semantic correspondence across images for the same concept, enhancing object-centric learning.
• Integrate both auxiliary tasks into the ViT training pipeline using exponential moving average (EMA) for feature queue updates.
• Apply the method to standard ViT backbones without modifying architecture, enabling plug-and-play deployment.

Experimental results

Research questions

• RQ1 Can concept-guided auxiliary tasks improve visual relational reasoning in Vision Transformers?
• RQ2 How does the concept-feature dictionary enable better feature retrieval and representation learning?
• RQ3 To what extent does the proposed method improve systematic generalization on real-world visual reasoning benchmarks?
• RQ4 How does RelViT compare to prior approaches in terms of performance and robustness across different ViT variants?
• RQ5 Can the method be effectively applied to standard benchmarks like HICO and GQA without architectural changes?

Key findings

• RelViT achieves a 16% absolute improvement over the best baseline on the original HICO benchmark split.
• On the new systematic generalization split of HICO, RelViT improves performance by 43% over the best baseline.
• On the original GQA benchmark split, RelViT achieves a 13% improvement over the best baseline.
• On the systematic generalization split of GQA, RelViT improves performance by 18% over the best baseline.
• Ablation studies confirm that both the global and local auxiliary tasks contribute significantly to performance gains.
• The method shows strong compatibility with multiple ViT variants and robustness to hyperparameter settings.