ThinkingViT: Matryoshka Thinking Vision Transformer for Elastic Inference

TL;DR

ThinkingViT introduces a dynamic, nested Vision Transformer architecture that adaptively adjusts inference computation based on input difficulty, improving efficiency and accuracy over fixed-budget models.

Contribution

It proposes a novel nested ViT with progressive thinking stages and token recycling, enabling input-dependent inference and better performance.

Findings

Outperforms nested baselines by up to 2.0 p.p. in accuracy at same throughput

Achieves up to 2.9 p.p. accuracy gain at equal GMACs on ImageNet-1K

Serves as a plug-in upgrade for ViTs and transfers well to other architectures

Abstract

ViTs deliver SOTA performance, yet their fixed computational budget prevents scalable deployment across heterogeneous hardware. Recent Matryoshka-style Transformer architectures mitigate this by embedding nested subnetworks within a single model to enable scalable inference. However, these models allocate the same amount of compute to all inputs, regardless of their complexity, which leads to inefficiencies. To address this, we introduce ThinkingViT, a nested ViT architecture that employs progressive thinking stages to dynamically adjust inference computation based on input difficulty. ThinkingViT first activates a small subset of the most important attention heads to produce an initial prediction. If the prediction confidence exceeds a predefined threshold, inference terminates early. Otherwise, within the same backbone, it activates a larger subset of attention heads and conducts a new forward pass. This process continues iteratively until the model reaches the predefined confidence level or exhausts its maximum capacity. To boost the performance of subsequent rounds, we introduce a Token Recycling approach that fuses the input embeddings with the embeddings from the previous stage. Experiments show that ThinkingViT surpasses nested baselines by up to 2.0 percentage points (p.p.) in accuracy at the same throughput and by up to 2.9 p.p. at equal GMACs on ImageNet-1K. We show that the backbone-preserving design of ThinkingViT allows it to serve as a plug-in upgrade for ViTs in downstream tasks such as semantic segmentation. We also demonstrate that ThinkingViT transfers effectively to other architectures such as Swin Transformers. The source code is available at https://github.com/ds-kiel/ThinkingViT.