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Can Large Vision-Language Models Correct Semantic Grounding Errors By Themselves?

Yuan-Hong Liao, Rafid Mahmood, Sanja Fidler, David Acuna

TL;DR

The paper investigates whether large vision-language models can self-correct semantic grounding errors without in-domain data or fine-tuning. It introduces a training-free feedback framework using oracle or self-generated binary feedback, mediated by a Verifier, and evaluates grounding performance on ADE20k and COCO panoptic datasets across open-source and proprietary VLMs. Key findings show that oracle feedback can substantially boost grounding, while iterative binary feedback enables consistent improvements in open-source models; intrinsic self-correction is often ineffective, though publicly accessible models like GPT-4V/4o can benefit from the framework. The work demonstrates a scalable, compute-conscious path to enhance grounding in internet-scale VLMs, albeit with tradeoffs in feedback reliability and computation.

Abstract

Enhancing semantic grounding abilities in Vision-Language Models (VLMs) often involves collecting domain-specific training data, refining the network architectures, or modifying the training recipes. In this work, we venture into an orthogonal direction and explore whether VLMs can improve their semantic grounding by "receiving" feedback, without requiring in-domain data, fine-tuning, or modifications to the network architectures. We systematically analyze this hypothesis using a feedback mechanism composed of a binary signal. We find that if prompted appropriately, VLMs can utilize feedback both in a single step and iteratively, showcasing the potential of feedback as an alternative technique to improve grounding in internet-scale VLMs. Furthermore, VLMs, like LLMs, struggle to self-correct errors out-of-the-box. However, we find that this issue can be mitigated via a binary verification mechanism. Finally, we explore the potential and limitations of amalgamating these findings and applying them iteratively to automatically enhance VLMs' grounding performance, showing grounding accuracy consistently improves using automated feedback across all models in all settings investigated. Overall, our iterative framework improves semantic grounding in VLMs by more than 15 accuracy points under noise-free feedback and up to 5 accuracy points under a simple automated binary verification mechanism. The project website is hosted at https://andrewliao11.github.io/vlms_feedback

Can Large Vision-Language Models Correct Semantic Grounding Errors By Themselves?

TL;DR

The paper investigates whether large vision-language models can self-correct semantic grounding errors without in-domain data or fine-tuning. It introduces a training-free feedback framework using oracle or self-generated binary feedback, mediated by a Verifier, and evaluates grounding performance on ADE20k and COCO panoptic datasets across open-source and proprietary VLMs. Key findings show that oracle feedback can substantially boost grounding, while iterative binary feedback enables consistent improvements in open-source models; intrinsic self-correction is often ineffective, though publicly accessible models like GPT-4V/4o can benefit from the framework. The work demonstrates a scalable, compute-conscious path to enhance grounding in internet-scale VLMs, albeit with tradeoffs in feedback reliability and computation.

Abstract

Enhancing semantic grounding abilities in Vision-Language Models (VLMs) often involves collecting domain-specific training data, refining the network architectures, or modifying the training recipes. In this work, we venture into an orthogonal direction and explore whether VLMs can improve their semantic grounding by "receiving" feedback, without requiring in-domain data, fine-tuning, or modifications to the network architectures. We systematically analyze this hypothesis using a feedback mechanism composed of a binary signal. We find that if prompted appropriately, VLMs can utilize feedback both in a single step and iteratively, showcasing the potential of feedback as an alternative technique to improve grounding in internet-scale VLMs. Furthermore, VLMs, like LLMs, struggle to self-correct errors out-of-the-box. However, we find that this issue can be mitigated via a binary verification mechanism. Finally, we explore the potential and limitations of amalgamating these findings and applying them iteratively to automatically enhance VLMs' grounding performance, showing grounding accuracy consistently improves using automated feedback across all models in all settings investigated. Overall, our iterative framework improves semantic grounding in VLMs by more than 15 accuracy points under noise-free feedback and up to 5 accuracy points under a simple automated binary verification mechanism. The project website is hosted at https://andrewliao11.github.io/vlms_feedback
Paper Structure (25 sections, 16 figures, 7 tables)

This paper contains 25 sections, 16 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Self-Correction in VLMs for Semantic Grounding
  4. Setup: Semantic Grounding and Self-Correction
  5. Research Questions
  6. Can VLMs Receive and Understand Oracle Grounding Feedback?
  7. Can VLMs Give Binary Grounding Feedback for Themselves?
  8. Experiment Protocols
  9. Empirical Findings
  10. Can VLMs Receive and Understand Oracle Grounding Feedback?
  11. Can VLMs Give Binary Grounding Feedback for Themselves?
  12. Can VLMs Correct their Grounding Errors through Self-Correction?
  13. Setup: Iterative Self-Correction in VLMs
  14. Main Results
  15. Conclusion
  16. ...and 10 more sections

Figures (16)

  • Figure 1: Enhancing semantic grounding in VLMs through self-correction. We explore to improve semantic grounding in VLMs through self-correction, without the needs of in-domain data, fine-tuning, or architectural changes. For self-correction, we adopt the setup involving explicit feedback generation. When provided with an image and a specified region, a VLM identifies the semantic properties of the image region. An automated feedback-based verification mechanism facilitates an interaction between the VLM and a 'Verifier' to improve the VLM's initial understanding.
  • Figure 2: Semantic grounding and self-correction framework. Left (Semantic Grounding): Given an image and a text prompt that specifies a region of interest, a VLM is tasked to identify the semantic class best describing the image region. Center (Feedback Generation): For completeness, we explore both oracle and automated feedback generated from VLMs themselves. Oracle Binary Feedback: An oracle provides feedback only on the correctness of the predictions. Oracle Class Label Feedback: An oracle provides explicit feedback on the correct class labels. Automated Binary Feedback: A VLM acts as a 'Verifier', confirms or rejects the previous predictions. Right (Feedback Integration): VLMs correct their own mistakes by taking the feedback.
  • Figure 3: Examples of prompting techniques. Left: Zero-shot CoT prepends a guiding sentence (in red) before VLMs' output. Right: We apply various visual prompting techniques including RoI crop, visual marks, and SoM to modify input images to VLMs to guide the models' attention.
  • Figure 4: Cost-performance tradeoff of GPT-4o in ADE20k
  • Figure 5: Prompt template to produce the base predictions. The text in red represents variables.
  • ...and 11 more figures