Semi-supervised Grounding Alignment for Multi-modal Feature Learning
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Bibliographic record
Abstract
Self-supervised transformer-based architectures, such as ViLBERT [1] and others, have recently emerged as dominant paradigms for multi-modal feature learning. Such architectures leverage large-scale datasets (e.g., Conceptual Captions [2]) and, typically, image-sentence pairings, for self-supervision. However, conventional multi-modal feature learning requires huge datasets and computing for both pre-training and fine-tuning to the target task. In this paper, we illustrate that more granular semi-supervised alignment at a region-phrase level is an additional useful cue and can further improve the performance of such representations. To this end, we propose a novel semi-supervised grounding alignment loss, which leverages an off-the-shelf pre-trained phrase grounding model for pseudo-supervision (by producing region-phrase alignments). This semi-supervised formulation enables better feature learning in the absence of any additional human annotations on the large-scale (Conceptual Captions) dataset. Further, it shows an even larger margin of improvement on smaller data splits, leading to effective data-efficient feature learning. We illustrate the superiority of the learned features by fine-tuning the resulting models to multiple vision-language downstream tasks: visual question answering (VQA), visual commonsense reasoning (VCR), and visual grounding. Experiments on the VQA, VCR, and grounding benchmarks demonstrate the improvement of up to 1.3% in accuracy (in visual grounding) with large-scale training; up to 5.9% (in VQA) with 1/8 of the data for pre-training and fine-tuning<sup>1</sup><sup>1</sup>We will release the code and all pre-trained models upon acceptance..
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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 | 0.000 | 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 | 0.001 |
| 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