Problem
Research questions and friction points this paper is trying to address.
tensor decomposition
multi-dimensional image
common structure
computational cost
learnable parameters
Pre-Trained Low-Rank Tensor Decomposition for Multi-Dimensional Image Recovery
🤖 AI Summary
为解决多维图像恢复中结构共享和计算成本问题，提出预训练低秩张量分解（PLTD）框架，结合预训练大视觉模型与经典张量分解方法。
📝 Abstract
Recently, tensor decompositions are prevalent for multi-dimensional image representation, which learn the instance-specific structure of each image from scratch. However, tensor decompositions neglect the common structure across different images, leading to limited semantic modeling capability, high computational cost, and a large number of learnable parameters. To address this challenge, we suggest the first pre-trained low-rank tensor decomposition (PLTD) framework, which organically integrates the pre-trained large vision model into the classical tensor decomposition framework. Beyond the shallow and untrained deep tensor decomposition, the suggested PLTD achieves an unprecedented balance among higher recovery fidelity, fewer learnable parameters, and smaller carbon footprint. Specifically, PLTD factorizes the target tensor into a latent tensor and a learnable transform that maps the latent tensor back to the original data domain. The latent tensor consists of two indispensable and complementary terms, i.e., a fixed pre-trained latent tensor and a learnable low-rank latent tensor. The fixed pre-trained latent tensor is distilled from a pre-trained large vision model (i.e., DINOv3) to capture the common structure of the target tensor, while the learnable low-rank latent tensor characterizes the instance-specific structure of the target tensor. To examine the potential of PLTD, we develop the corresponding multi-dimensional image recovery model and theoretically justify the advantages of this framework. Additionally, we discuss the connections between PLTD and classical tensor decomposition frameworks. Extensive experiments on multi-dimensional image recovery demonstrate that PLTD consistently achieves superior performance compared with state-of-the-art methods.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Pre-trained Low-Rank Tensor Decomposition (PLTD)
DINOv3
Latent Tensor
Multi-Dimensional Image Recovery
Carbon Footprint
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