insight - Computer Vision - # Open-Vocabulary Hallucination Mitigation in Image Captioning

Mitigating Open-Vocabulary Hallucinations in Image Captioning Models

Q: How can the proposed open-vocabulary hallucination mitigation framework be extended to other vision-language tasks beyond image captioning, such as visual question answering or visual instruction following

The open-vocabulary hallucination mitigation framework proposed for image captioning can be extended to other vision-language tasks by adapting the reward function and optimization process to suit the specific requirements of tasks like visual question answering (VQA) or visual instruction following. For VQA, the framework can be modified to focus on hallucinations related to answering questions about visual content. The reward function can be adjusted to prioritize factual correctness in responses to visual questions, ensuring that the generated answers are grounded in the image content. The optimization process can be tailored to reinforce the model's ability to provide accurate and relevant information in response to diverse visual queries. Similarly, for visual instruction following tasks, the framework can be customized to address hallucinations that may arise when interpreting and executing visual instructions. The reward function can emphasize the fidelity of the generated instructions to the visual input, ensuring that the model accurately captures the necessary details for successful task completion. The optimization process can be fine-tuned to optimize for both accuracy and completeness in generating instructional text based on visual stimuli. By adapting the open-vocabulary hallucination mitigation framework to these tasks, researchers can enhance the reliability and trustworthiness of vision-language models across a range of applications, ensuring that the generated outputs are both informative and factually grounded.

Q: What are the potential limitations of using synthetic data for benchmarking hallucinations, and how can the OpenCHAIR benchmark be further improved to better reflect real-world hallucination patterns

Using synthetic data for benchmarking hallucinations may introduce certain limitations that need to be addressed to improve the effectiveness of the OpenCHAIR benchmark in reflecting real-world hallucination patterns. Some potential limitations of using synthetic data include: Generalization to real-world data: Synthetic data may not fully capture the complexity and variability of real-world images, leading to discrepancies in hallucination patterns between synthetic and real data. Limited diversity: Synthetic data generation techniques may not encompass the full range of visual scenarios and objects present in real-world images, potentially limiting the benchmark's coverage of hallucination patterns. Biases in data generation: Synthetic data generation processes may inadvertently introduce biases or artifacts that do not reflect the true distribution of visual content, impacting the benchmark's validity. To address these limitations and improve the OpenCHAIR benchmark, researchers can consider the following strategies: Augmentation with real-world data: Incorporating real-world images and captions into the benchmark alongside synthetic data can enhance its diversity and alignment with real-world hallucination patterns. Adversarial training: Introducing adversarial examples during data generation can help simulate challenging scenarios and improve the benchmark's robustness to hallucinations. Human evaluation: Conducting human evaluations on both synthetic and real data can provide valuable insights into the effectiveness of the benchmark in capturing hallucination patterns and guide further improvements. By iteratively refining the benchmark through a combination of synthetic and real data, researchers can create a more comprehensive and representative evaluation framework for hallucination mitigation in vision-language tasks.

Core Concepts

Existing image captioning models suffer from the issue of hallucinations, generating spurious details that cannot be inferred from the input image. This work proposes a framework to address hallucinations in the open-vocabulary setting, including a new benchmark (OpenCHAIR) to evaluate open-vocabulary object hallucinations, and an optimization-based approach (MOCHa) to mitigate such hallucinations without relying on a closed object list.

Abstract

The paper addresses the problem of hallucinations in image captioning, where models generate spurious details that cannot be inferred from the input image. Existing methods have largely focused on closed-vocabulary object lists, ignoring the long-tailed nature of hallucinations in practice.

The authors first introduce OpenCHAIR, a new benchmark for evaluating open-vocabulary object hallucinations in image captioning. OpenCHAIR leverages generative foundation models to produce diverse synthetic caption-image pairs, allowing for a more comprehensive assessment of hallucination types compared to the existing closed-vocabulary CHAIR benchmark.

To mitigate open-vocabulary hallucinations, the authors propose MOCHa, a reinforcement learning-based framework that jointly optimizes for caption fidelity (avoiding hallucinations) and adequacy (including sufficient details). MOCHa uses a multi-objective reward function that combines metrics like natural language inference and BERTScore, without requiring any strong supervision.

Experiments show that MOCHa improves a variety of state-of-the-art image captioning models, as captured by the OpenCHAIR benchmark and other existing metrics. The authors demonstrate that their open-vocabulary approach outperforms prior works that rely on closed-vocabulary object lists. The paper also provides ablation studies and qualitative examples to illustrate the effectiveness of the proposed framework.

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"A group of people jumping on a skateboard."
"Several people jumping up and down a flight of stairs."

Quotes

"While recent years have seen rapid progress in image-conditioned text generation, image captioning still suffers from the fundamental issue of hallucinations, namely, the generation of spurious details that cannot be inferred from the given image."
"To this end, we propose a framework for addressing hallucinations in image captioning in the open-vocabulary setting."
"Our key insight is that these two goals [fidelity and adequacy] can be jointly optimized at the sequence-level by applying RL with a multi-objective reward function."

Key Insights Distilled From

Mitigating Open-Vocabulary Caption Hallucinations

by Assaf Ben-Ki... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2312.03631.pdf

Mitigating Open-Vocabulary Caption Hallucinations

Deeper Inquiries

How can the proposed open-vocabulary hallucination mitigation framework be extended to other vision-language tasks beyond image captioning, such as visual question answering or visual instruction following

The open-vocabulary hallucination mitigation framework proposed for image captioning can be extended to other vision-language tasks by adapting the reward function and optimization process to suit the specific requirements of tasks like visual question answering (VQA) or visual instruction following.
For VQA, the framework can be modified to focus on hallucinations related to answering questions about visual content. The reward function can be adjusted to prioritize factual correctness in responses to visual questions, ensuring that the generated answers are grounded in the image content. The optimization process can be tailored to reinforce the model's ability to provide accurate and relevant information in response to diverse visual queries.
Similarly, for visual instruction following tasks, the framework can be customized to address hallucinations that may arise when interpreting and executing visual instructions. The reward function can emphasize the fidelity of the generated instructions to the visual input, ensuring that the model accurately captures the necessary details for successful task completion. The optimization process can be fine-tuned to optimize for both accuracy and completeness in generating instructional text based on visual stimuli.
By adapting the open-vocabulary hallucination mitigation framework to these tasks, researchers can enhance the reliability and trustworthiness of vision-language models across a range of applications, ensuring that the generated outputs are both informative and factually grounded.

What are the potential limitations of using synthetic data for benchmarking hallucinations, and how can the OpenCHAIR benchmark be further improved to better reflect real-world hallucination patterns

Using synthetic data for benchmarking hallucinations may introduce certain limitations that need to be addressed to improve the effectiveness of the OpenCHAIR benchmark in reflecting real-world hallucination patterns. Some potential limitations of using synthetic data include:

Generalization to real-world data: Synthetic data may not fully capture the complexity and variability of real-world images, leading to discrepancies in hallucination patterns between synthetic and real data.

Limited diversity: Synthetic data generation techniques may not encompass the full range of visual scenarios and objects present in real-world images, potentially limiting the benchmark's coverage of hallucination patterns.

Biases in data generation: Synthetic data generation processes may inadvertently introduce biases or artifacts that do not reflect the true distribution of visual content, impacting the benchmark's validity.

To address these limitations and improve the OpenCHAIR benchmark, researchers can consider the following strategies:

Augmentation with real-world data: Incorporating real-world images and captions into the benchmark alongside synthetic data can enhance its diversity and alignment with real-world hallucination patterns.

Adversarial training: Introducing adversarial examples during data generation can help simulate challenging scenarios and improve the benchmark's robustness to hallucinations.

Human evaluation: Conducting human evaluations on both synthetic and real data can provide valuable insights into the effectiveness of the benchmark in capturing hallucination patterns and guide further improvements.

By iteratively refining the benchmark through a combination of synthetic and real data, researchers can create a more comprehensive and representative evaluation framework for hallucination mitigation in vision-language tasks.

Given the rapid progress in large language models and their increasing integration with vision, how might future research on hallucination mitigation need to adapt to address challenges posed by the evolving capabilities of these multimodal systems

As large language models (LLMs) continue to advance and integrate with vision capabilities, future research on hallucination mitigation will need to adapt to address the evolving challenges posed by these multimodal systems. Some key considerations for adapting to the changing landscape of multimodal LLMs include:

Complex interactions: Multimodal LLMs exhibit intricate interactions between visual and textual modalities, leading to nuanced hallucination patterns that may require specialized mitigation strategies. Future research will need to explore how these interactions influence hallucinations and develop targeted approaches to address them effectively.

Fine-grained analysis: With the increasing sophistication of multimodal models, researchers may need to conduct more fine-grained analyses of hallucination types and sources to tailor mitigation techniques accordingly. This may involve distinguishing between different levels of hallucinations (e.g., object-level vs. attribute-level) and designing specific interventions for each.

Adversarial robustness: Given the susceptibility of LLMs to adversarial attacks and biases, future research on hallucination mitigation will need to prioritize adversarial robustness and fairness considerations. Developing techniques to detect and mitigate adversarial hallucinations will be crucial for ensuring the reliability and trustworthiness of multimodal systems.

Interdisciplinary collaboration: As the field of multimodal LLMs evolves, researchers from diverse backgrounds, including computer vision, natural language processing, and cognitive science, may need to collaborate to address the complex challenges of hallucination mitigation comprehensively. Interdisciplinary approaches can offer fresh perspectives and innovative solutions to tackle emerging issues in multimodal model development.

By adapting to the evolving capabilities of multimodal LLMs and addressing the unique challenges they present, future research on hallucination mitigation can contribute to the continued advancement and responsible deployment of vision-language technologies.