DIVE-Doc: Downscaling Foundational Image Visual Encoder into Hierarchical Architecture for DocVQA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the DocVQA context, current end-to-end models ei-ther use lightweight architectures that run efficiently on small devices but have limited performance or rely on LVLMs that achieve high performance at significant computational cost. Thus, we present DIVE-Doc, an end-to-end model that bridges this gap by distilling a 400M-parameter SigLIP visual encoder into a small hierarchical Swin transformer, preserving LVLM performance with only one-fifth of the visual encoder's parameters. We investi-gate two distillation strategies: Fixed-Resolution Distillation (FRD), which matches teacher-student patch counts by forcing student input resolution, and Adaptive-Resolution Distillation (ARD), which aligns mismatched sequences via parameter-free interpolation, enabling various input reso-lutions. Fine-tuned with QLoRA, DIVE-Doc attains 82.7% ANLS, outperforming lightweight models and sitting within 2 ANLS of its teacher PaliGEMMA on DocVQA, while halving the teacher visual encoder's latency and supporting higher input resolutions. Analysis on RVL-CDIP and Do-cLayNet shows that the visual encoder captures document-level structure but delegates fine-grained layout reasoning to the language model decoder. The code is available at https://github.com/JayRay5/DIVE-Doc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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