Investigating the Power of Image-Language Transformers in Enhancing Verb Comprehension
Introduction:
Grounding language to vision is a crucial challenge for AI systems dealing with image retrieval or generating descriptions for the visually impaired. To accomplish these tasks successfully, models need to connect different elements of language, such as objects and verbs, to images. Understanding verbs is particularly challenging as it requires recognizing objects and understanding their relationships within an image. To address this challenge, we introduce the SVO-Probes dataset, which allows us to probe language and vision models for verb understanding. This dataset consists of 48,000 image-sentence pairs, testing understanding for over 400 verbs. By evaluating models on SVO-Probes, we aim to uncover limitations in verb understanding and inspire the development of more sophisticated language and vision models. Visit our SVO-Probes benchmark and models on GitHub for further exploration.
Full Article: Investigating the Power of Image-Language Transformers in Enhancing Verb Comprehension
Probing Language and Vision Models for Verb Understanding: SVO-Probes Dataset
Grounding language to vision is a fundamental challenge for many real-world AI systems. These systems often need to retrieve images or generate descriptions for the visually impaired. To accomplish these tasks successfully, models must be able to relate various aspects of language, such as objects and verbs, to images.
The Importance of Verb Understanding
Understanding verbs is particularly difficult because it requires models to not only recognize objects but also understand how different objects in an image relate to each other. For instance, distinguishing between two images may require differentiating between verbs like “catch” and “kick.” To tackle this challenge, researchers have introduced the SVO-Probes dataset to probe language and vision models for verb understanding.
Evaluating Multimodal Transformer Models
The SVO-Probes dataset focuses on multimodal transformer models that have shown promising results on various language and vision tasks. However, despite their success on benchmarks, it remains unclear whether these models truly possess fine-grained multimodal understanding. Previous work has revealed that language and vision models can perform well on benchmarks without actually having multimodal understanding. They may rely on language priors or even “hallucinate” objects not present in the images when answering questions or captioning images.
The Importance of SVO-Probes Dataset
To address the limitations of prior probe sets, the SVO-Probes dataset has been developed. This dataset includes 48,000 image-sentence pairs and evaluates understanding for more than 400 verbs. Each sentence is represented as a
Creation of SVO-Probes Dataset
To create the SVO-Probes dataset, a preliminary annotation step involves querying an image search with SVO triplets from a common training dataset called Conceptual Captions. This step is followed by filtering the retrieved images to ensure a clean set of image-SVO pairs. Since transformers are trained on image-sentence pairs rather than image-SVO pairs, sentences that describe each image are collected through annotators. These sentences include the SVO triplet. Each sentence is then paired with a negative image, and annotators verify the negatives in a final annotation step.
Performance of Multimodal Transformers on SVO-Probes Dataset
The SVO-Probes dataset enables the examination of how accurately multimodal transformers can classify examples as positive or negative. Results show that it is a challenging dataset, with a standard multimodal transformer model achieving an overall accuracy of 64.3% (chance is 50%). While accuracy on subjects and objects is 67.0% and 73.4%, respectively, performance drops to 60.8% for verbs. This finding highlights the difficulty of verb recognition for vision and language models.
Exploring Model Architectures on the SVO-Probes Dataset
The SVO-Probes dataset also allows for the exploration of different model architectures. Surprisingly, models with weaker image modeling ability outperform the standard transformer model. One hypothesis suggests that the standard model, with its stronger image modeling ability, tends to overfit the training set. Although these models may perform worse on other language and vision tasks, the targeted probe task of SVO-Probes reveals weaknesses that are not apparent on other benchmarks.
Addressing Fine-Grained Understanding Challenges
In conclusion, despite achieving impressive performance on benchmarks, multimodal transformers still face challenges in fine-grained understanding, particularly in verb recognition. The SVO-Probes dataset aims to drive exploration and improvement in verb understanding for language and vision models. It provides a valuable resource for creating more targeted probe datasets to further enhance these models’ capabilities.
Explore the SVO-Probes Dataset
To learn more about the SVO-Probes dataset and access the benchmark and models, visit the GitHub repository: [link to GitHub repository].
Summary: Investigating the Power of Image-Language Transformers in Enhancing Verb Comprehension
Grounding language to vision is a challenge for AI systems in tasks like image retrieval and generating descriptions for the visually impaired. To address this, the SVO-Probes dataset has been introduced to probe language and vision models for verb understanding. The dataset includes 48,000 image-sentence pairs that test the understanding of over 400 verbs. By isolating different parts of the sentence, the dataset evaluates the model’s performance and highlights verb recognition as a challenging aspect. Surprisingly, models with weaker image modeling perform better on this dataset, revealing weaknesses that are not observed on other benchmarks. The SVO-Probes dataset aims to drive exploration and improvement in verb understanding for language and vision models.
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