SpinSCI: Mask-Conditioned Spatial-Temporal Spin Transformer for Video Snapshot Compressive Imaging

Video snapshot compressive imaging (SCI) recovers multiple frames from one spatiotemporally coded measurement. This paper proposes *SpinSCI*, a mask-conditioned Spatial-Temporal Spin Transformer whose *Mask-Conditioned Ising Refinement Block (MC-IRB)* combines attention logits and sensing information to form signed spin couplings. Finite mean-field updates gate residual feature refinement. Physics-guided initialization and final data consistency incorporate the measurement model. Compared with the state-of-the-art method HiSViT${}_9$, SpinSCI achieves average PSNR gains of **0.50 dB**, **0.23 dB**, and **0.04 dB** on *small-scale gray, mid-scale color, and large-scale color benchmarks*, respectively, with **16.9–17.0% fewer parameters, 21.0–21.6% fewer FLOPs, 77.0–84.8% lower inference latency, and 77.0–78.0% lower peak memory**. Qualitative comparisons on the *real benchmark* further show recovery of fine details and textures. If the paper is accepted, the code will be released by the camera-ready deadline.