Inference-Time Dynamic Modality Selection for Incomplete Multimodal Classification (ICLR 2026)
Siyi Du, Xinzhe Luo, Declan P. O'Regan, and Chen Qin
(a-b) Evidence of the discarding-imputation dilemma: (a-1) vs. (a-2) recovery-free methods (e.g., ModDrop) learn less discriminative features because they ignore highly task-relevant missing modalities {M,T}; (b) recovery-based methods (e.g., MoPoE) generate unreliable reconstructions, e.g., low-fidelity (orange) or misaligned (yellow). (c) Our DyMo, which addresses the dilemma by dynamically fusing task-relevant recovered modalities, improving accuracy by 1.61% on PolyMNIST, 1.68% on MST, and 3.88% on CelebA (Tab 1).
This repository provides pretrained model checkpoints for Inference-Time Dynamic Modality Selection for Incomplete Multimodal Classification.
For the training code, evaluation scripts, and usage instructions, please refer to the official GitHub repository:
👉 https://github.com/siyi-wind/DyMo.