Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions
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Bibliographic record
Abstract
Neural module networks (NMN) are a popular approach for grounding visual referring expressions.Prior implementations of NMN use pre-defined and fixed textual inputs in their module instantiation.This necessitates a large number of modules as they lack the ability to share weights and exploit associations between similar textual contexts (e.g."dark cube on the left" vs. "black cube on the left").In this work, we address these limitations and evaluate the impact of contextual clues in improving the performance of NMN models.First, we address the problem of fixed textual inputs by parameterizing the module arguments.This substantially reduce the number of modules in NMN by up to 75% without any loss in performance.Next we propose a method to contextualize our parameterized model to enhance the module's capacity in exploiting the visiolinguistic associations.Our model outperforms the state-of-the-art NMN model on CLEVR-Ref+ dataset with +8.1% improvement in accuracy on the single-referent test set and +4.3% on the full test set.Additionally, we demonstrate that contextualization provides +11.2% and +1.7% improvements in accuracy over prior NMN models on CLO-SURE and NLVR2.We further evaluate the impact of our contextualization by constructing a contrast set for CLEVR-Ref+, which we call CC-Ref+.We significantly outperform the baselines by as much as +10.4% absolute accuracy on CC-Ref+, illustrating the generalization skills of our approach.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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