Multi-Modal Dynamic Graph Transformer for Visual Grounding
Why this work is in the frame
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Bibliographic record
Abstract
Visual grounding (VG) aims to align the correct regions of an image with a natural language query about that image. We found that existing VG methods are trapped by the single-stage grounding process that performs a sole evaluate-and-rank for meticulously prepared regions. Their performance depends on the density and quality of the candidate regions, and is capped by the inability to optimize the located regions continuously. To address these issues, we propose to remodel VG into a progressively optimized visual semantic alignment process. Our proposed multi-modal dynamic graph transformer (M-DGT) achieves this by building upon the dynamic graph structure with regions as nodes and their semantic relations as edges. Starting from a few randomly initialized regions, M-DGT is able to make sustainable adjustments (i.e., 2D spatial transformation and deletion) to the nodes and edges of the graph based on multi-modal information and the graph feature, thereby efficiently shrinking the graph to approach the ground truth regions. Experiments show that with an average of 48 boxes as initialization, the performance of M-DGT on the Flickr30k Entities and RefCOCO datasets outperforms existing state-of-the-art methods by a substantial margin, in terms of both accuracy and Intersect over Union (IOU) scores. Furthermore, introducing M-DGT to optimize the predicted regions of existing methods can further significantly improve their performance. The source codes are available at https://github.com/iQua/M-DGT.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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