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This work proposes FrameLite, a compact cropping model built on Qwen3, which outperforms the compared large-VLM croppers across public benchmarks with a substantially smaller backbone and develops three-stage training to turn this potential into stronger direct predictions.
TOLA is proposed, a Text-aware One-step Latent Adaptation framework without iterative image-text diffusion that consistently surpasses existing diffusion-based TSR methods by at least 2.72 dB in PSNR on CTR-TSR-Test.
It is argued that efficient Real-ISR requires not only a shorter sampling trajectory, but also specialized modeling of faithful reconstruction and perceptual detail synthesis, and proposed PixelIR, a fidelity-perception decoupling framework built upon pixel-space image-residual flow matching.
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