The prevalent convolution neural network (CNN) and Transformer have revolutionized the area of single-image super-resolution (SISR). Though these models have significantly improved performance, they often struggle with real-time applications or on resource-constrained platforms due to their complexity. In this paper, we propose TBag, a lightweight hybrid network that combines the strengths of CNN and Transformer to address these challenges. Our method simplifies the Transformer block with three key optimizations: 1) No projection layer is applied to the value in the original self-attention operation; 2) The number of tokens is rescaled before the self-attention operation and then rescaled back for easing of computation; 3) The expansion factor of the original feed-forward network (FFN) is adjusted. These optimizations enable the development of an efficient hybrid network tailored for real-time SISR. Notably, the hybrid design of CNN and Transformer further enhances both local detail recovery and global feature modeling. Extensive experiments show that TBag achieves a competitive trade-off between effectiveness and efficiency compared to previous lightweight SISR methods (e.g.,+0.42 dBPSNR with an86.7%reduction in latency). Moreover, TBag's real-time capabilities make it highly suitable for practical applications, with the TBag-Tiny version achieving up to59 FPSon hardware devices. Future work will explore the potential of this hybrid approach in other image restoration tasks, such as denoising and deblurring.

The contrastive vision-language pre-training, known as CLIP, demonstrates remarkable potential in perceiving open-world visual concepts, enabling effective zero-shot image recognition. Nevertheless, few-shot learning methods based on CLIP typically require offline fine-tuning of the parameters on few-shot samples, resulting in longer inference time and the risk of overfitting in certain domains. To tackle these challenges, we propose the Meta-Adapter, a lightweight residual-style adapter, to refine the CLIP features guided by the few-shot samples in an online manner. With a few training samples, our method can enable effective few-shot learning capabilities and generalize to unseen data or tasks without additional fine-tuning, achieving competitive performance and high efficiency. Without bells and whistles, our approach outperforms the state-of-the-art online few-shot learning method by an average of 3.6\% on eight image classification datasets with higher inference speed. Furthermore, our model is simple and flexible, serving as a plug-and-play module directly applicable to downstream tasks. Without further fine-tuning, Meta-Adapter obtains notable performance improvements in open-vocabulary object detection and segmentation tasks.