Grounded Multi-modal Conversation for Zero-shot Visual Question Answering
Why this work is in the frame
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Bibliographic record
Abstract
Zero-shot visual question answering (VQA) poses a formidable challenge at the intersection of computer vision and natural language processing. Traditionally, this problem has been tackled using end-to-end pre-trained vision-language models (VLMs). However, recent advancements in large language models (LLMs) demonstrate their exceptional reasoning and comprehension abilities, making them valuable assets in multi-modal tasks, including zero-shot VQA. LLMs have been previously integrated with VLMs to solve zero-shot VQA in a conversation-based approach. However, while the focus in VQA tasks is often on specific regions rather than the entire image, this aspect has been overlooked in previous approaches. Consequently, the overall performance of the framework relies on the ability of the pre-trained VLM to locate the region of interest that is relevant to the requested visual information within the entire image. To address this challenge, this paper proposes Grounded Multi-modal Conversation for Zero-shot Visual Question Answering (GMC-VQA), a region-based framework that leverages the complementary strengths of LLMs and VLMs in a conversation-based approach. We employ a grounding mechanism to refine visual focus according to the semantics of the question and foster collaborative interaction between VLM and LLM, effectively bridging the gap between visual and textual modalities and enhancing comprehension and response generation for visual queries. We evaluate GMC-VQA across three diverse VQA datasets, achieving substantial average improvements of 10.04% over end-to-end VLMs and 2.52% over the state-of-the-art VLM-LLM communication-based framework, respectively. Our code is publicly available at https://github.com/mrzarei5/GMC-VQA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it