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Transformation Driven Visual Reasoning

CVPR 2021 · Vision-language and multimodal › Multimodal LLMs · approach: Chain-of-thought multimodal reasoning · 18 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use a single-stream encoder rather than a two-stream encoderTranceNet on TRANCE Basic · TRANCE BasicAcc: 0.9571 -> 0.9810Helped
Add correctness-reward training with REINFORCE (ablation)ResNet⊕ on TRANCE Event · TRANCE EventAcc: 0.5785 -> 0.5978Helped
Add distance-reward training with REINFORCE (ablation)ResNet⊕ on TRANCE Event · TRANCE EventAcc: 0.5785 -> 0.5938Helped
Add both correctness and distance rewards with REINFORCE (ablation)ResNet⊕ on TRANCE Event · TRANCE EventAcc: 0.5785 -> 0.6001Helped
Use DUDA-style feature interaction instead of ResNet⊕TranceNet on TRANCE View · TRANCE ViewAcc: 0.5305 -> 0.4590Hurt
Introduce variant final-camera viewsdeep models evaluated from Event to View · TRANCE ViewAcc: 0.5785 -> 0.5305Hurt
Increase transformation sequence lengthCNN− on TRANCE Event · TRANCE EventLAcc: 95%, 56%, 23%, and 10%Hurt
Predict all atomic transformations without attending to their order constraintsdeep models on order-sensitive TRANCE Event samples · TRANCE Event, 7.8% order-sensitive samplesAcc: 0.3862 -> 0.3205Mixed
Use more training samplesResNet⊕ on TRANCE Basic, Event, and View · TRANCE Basic, Event, and ViewperformanceHelped

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