Enhanced Multi-Instance Partial Label Learning via Average Gradient Outer Product

Multi-instance partial-label learning (MIPL) is a recently proposed dual weakly supervised learning framework where each training bag is annotated with a candidate label set containing one true label and several false positives. The key challange of MIPL problem is that the relations between the key instances and the ground-truth labels are much more covered due to the false positive labels. Existing methods usually rely on the model's own predictions and roughly aggregate instance features according to the learned attention weights, which is easily misled by false positives and lets key instances be overlooked. Here, we propose Average Gradient Outer Product based Multi-instance Partial-Label Learning (AGOPMIPL) method, where the average gradient outer product (AGOP) is directly calculated from the bag-level features and the model prediction, then it is integrated in an attention module to amplify discriminative feature directions and thereby helps key-instance identification. Moreover, the feature prototypes and a progressive disambiguation strategy are introduced to further suppress noisy candidates. The experimental studies on four MIPL benchmarks and the real-world CRC-MIPL dataset are performed and AGOPMIPL consistently outperforms five state-of-the-art baselines, with up to $25.9\%$ relative gain on CRC-MIPL-KMeansSeg.