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Systematic Visual Reasoning through Object-Centric Relational Abstraction

NeurIPS 2023 · Vision-language and multimodal › Human and embodied modeling · approach: Abstract visual reasoning benchmarks · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add slot attention (vs 4x4 grid patches) (ablation)OCRA · ART RMTS/ID m=95test accuracy (%): 56.58 -> 85.31 (RMTS); 84.42 -> 92.80 (ID)Helped
add slot attention pre-training (ablation)OCRA · ART RMTS/ID m=95test accuracy (%): 58.69 -> 85.31 (RMTS); 53.48 -> 92.80 (ID)Helped
add relational embeddings before transformer (ablation)OCRA · ART RMTS/ID m=95test accuracy (%): 74.79 -> 85.31 (RMTS); 81.21 -> 92.80 (ID)Helped
add factorized feature/position embeddings (ablation)OCRA · ART RMTS/ID m=95test accuracy (%): 50.26 -> 85.31 (RMTS); 75.40 -> 92.80 (ID)Helped
add relational bottleneck (vs pairwise MLP) (ablation)OCRA · ART RMTS/ID m=95test accuracy (%): 63.62 -> 85.31 (RMTS); 76.56 -> 92.80 (ID)Helped
use inner product instead of learned linear bottleneck (ablation)OCRA · ART RMTS/ID m=95test accuracy (%): 50.70 -> 85.31 (RMTS); 48.07 -> 92.80 (ID)Helped
use inner product instead of outer product (ablation)OCRA · ART RMTS/ID m=95test accuracy (%): 62.84 -> 85.31 (RMTS); 69.58 -> 92.80 (ID)Helped
add transformer for higher-order relations (vs MLP sum) (ablation)OCRA · ART RMTS/ID m=95test accuracy (%): 51.29 -> 85.31 (RMTS); 44.18 -> 92.80 (ID)Helped
OCRA vs GAMR at hardest regimeGAMR · ART same/different m=95test accuracy (%): 83.49 -> 87.95Helped
OCRA vs Slot-Transformer at hardest regimeSlot-Transformer · ART distribution-of-3 m=95test accuracy (%): 60.61 -> 86.42Helped
OCRA vs Slot-Transformer in easiest regimeSlot-Transformer · ART same/different m=0test accuracy (%): 98.47 -> 97.31No clear effect
OCRA vs Slot-Transformer on CLEVR-ART identity rulesSlot-Transformer · CLEVR-ART IDtest accuracy (%): 78.81 -> 77.06No clear effect

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