Search Multilayer Perceptron-Based Fusion for Efficient and Accurate Siamese Tracking

• URL: http://arxiv.org/abs/2603.01706v1
• Date: Mon, 02 Mar 2026 10:30:54 GMT
• Title: Search Multilayer Perceptron-Based Fusion for Efficient and Accurate Siamese Tracking
• Authors: Tianqi Shen, Huakao Lin, Ning An,
• Abstract summary: Multilayer Perception (MLP)-based fusion module enables pixel-level interaction with minimal structural overhead.<n>Differentiable neural architecture search (DNAS) to decouple channel-width optimization from other architectural choices.<n> tracker ranks among the top performers on four general-purpose and three aerial benchmarks.
• Score: 3.7727834708902868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Siamese visual trackers have recently advanced through increasingly sophisticated fusion mechanisms built on convolutional or Transformer architectures. However, both struggle to deliver pixel-level interactions efficiently on resource-constrained hardware, leading to a persistent accuracy-efficiency imbalance. Motivated by this limitation, we redesign the Siamese neck with a simple yet effective Multilayer Perception (MLP)-based fusion module that enables pixel-level interaction with minimal structural overhead. Nevertheless, naively stacking MLP blocks introduces a new challenge: computational cost can scale quadratically with channel width. To overcome this, we construct a hierarchical search space of carefully designed MLP modules and introduce a customized relaxation strategy that enables differentiable neural architecture search (DNAS) to decouple channel-width optimization from other architectural choices. This targeted decoupling automatically balances channel width and depth, yielding a low-complexity architecture. The resulting tracker achieves state-of-the-art accuracy-efficiency trade-offs. It ranks among the top performers on four general-purpose and three aerial tracking benchmarks, while maintaining real-time performance on both resource-constrained Graphics Processing Units (GPUs) and Neural Processing Units (NPUs).

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