Efficient ViTs

Efficient Vision Transformer research seeks to make visual models practical at high resolutions and on limited hardware without losing the accuracy needed for classification, detection, and segmentation. Common approaches replace or constrain full self-attention with linear, windowed, or hybrid attention, and reduce work through token pruning or merging, structured pruning, quantization, and adaptive early exits. Researchers evaluate these methods on benchmarks such as ImageNet, COCO, and ADE20K, as well as on edge devices and dense prediction tasks where fine details matter. A central tension is that fewer FLOPs or tokens do not always mean faster real-world inference, and aggressive compression can harm accuracy, robustness, or small-region detail.