DiamondBlock: A Plug-and-Play Upsampling Module with Memory-Inspired Training for Visual Representation Enhancement

Convolutional neural networks and Transformer-based architectures have achieved remarkable progress in image classification and downstream vision tasks. However, multi-stage downsampling, while enhancing representation, often causes small-object information loss and limited receptive fields that hinder detection and segmentation. To address these issues, we propose DiamondBlock, a plug-and-play architectural module positioned at the front of backbone networks. DiamondBlock performs multiple rounds of upsampling on the original image, followed by symmetric downsampling and skip connections for multi-scale feature fusion. This design enhances fine-grained and large-scale representations with architecture-dependent parameter overhead but nontrivial computational overhead, since early-stage upsampling increases spatial resolution and thus FLOPs/latency depending on the number of DiamondBlock stages. Additionally, inspired by human memory mechanisms, we introduce a training paradigm based on the Ebbinghaus forgetting curve. Instead of purely random sampling, we design a periodic sample revisit strategy that simulates human review behavior to reinforce memory retention, requiring no architectural or optimizer changes. Extensive experiments on ImageNet-1K, COCO, and ADE20K validate the effectiveness of our approach. We explicitly report the accuracy-efficiency trade-off using FLOPs and throughput/latency, and recommend cost-aware configurations for different budgets. Integrating DiamondBlock into ViT backbones yields up to a 5.6% Top-1 accuracy improvement on ImageNet-1K. Moreover, under a step-matched training budget (i.e., keeping the total number of optimizer updates fixed), the Ebbinghaus-based training provides consistent but modest Top-1 gains when applied on top of DiamondBlockequipped backbones (typically around ∼0.1 percentage-point, up to +0.17 percentagepoint), improving generalization without architectural or optimizer changes. Overall, DiamondBlock combined with memory-driven training provides consistent improvements across classification, detection, and segmentation tasks.