Persistent Memory for Visual Recognition under Corruption and Missing Evidence

Can persistent memory support visual recognition when the input is corrupted or incomplete, particularly when labeled training data are limited? We investigate this question through the Vision Hopfield Memory Network (V-HMN), a model that integrates associative retrieval with iterative feature refinement across layers. Separate local and global memory banks store training-derived representations of patch neighborhoods and image-level context. During inference, each branch retrieves a weighted combination of stored patterns and uses the discrepancy between the retrieved and current representations to guide feature updates. This design makes information from training examples available through both learned parameters and persistent representations, while keeping the memory fixed at evaluation. The retrieved entries and their weights also make the memory component of inference inspectable. We study this mechanism through common image corruptions, controlled occlusion and masking, and training with limited labels, restricting memory construction to the declared training budget. Comparisons with the same architectural scaffold trained without retrieval, alongside interventions that disable reads or alter memory contents, distinguish the effects of learning with memory from those of accessing it during inference. Our analyses examine whether memory contributes beyond clean recognition performance, how its contribution changes with label availability, and when retrieval corrects predictions or introduces errors. By connecting these outcomes to local and global retrieval, memory coverage, and the entries used during computation, we investigate both the potential and the limitations of persistent memory for recognition under degraded evidence.