The introduction of generative models has significantly advanced image super-resolution (SR) in handling real-world degradations. However, they often incur fidelity-related issues, particularly distorting textual structures. In this paper, we introduce a novel diffusion-based SR framework, namely TADiSR, which integrates text-aware attention and joint segmentation decoders to recover not only natural details but also the structural fidelity of text regions in degraded real-world images. Moreover, we propose a complete pipeline for synthesizing high-quality images with fine-grained full-image text masks, combining realistic foreground text regions with detailed background content. Extensive experiments demonstrate that our approach substantially enhances text legibility in super-resolved images, achieving state-of-the-art performance across multiple evaluation metrics and exhibiting strong generalization to real-world scenarios. Our code is available at [here](https://github.com/mingcv/TADiSR).

3D object recognition has received extensive attention in recent years. Many existing methods tackle the task by rendering 3D objects from multiple views. However, most multi-view recognition methods do not utilize fine-grained information from different views, which is found to be crucial for improving 3D object representation in the multi-view setting. In this paper, we propose a transformer-based method, referred to as MVCFormer, for multi-view feature clustering and fusion. MVCFormer clusters semantically similar tokens at the same stages and selects representative fine-grained features, which helps to eliminate feature redundancy and remove cluttered backgrounds and make the selected features more diverse. On the other hand, our model also integrates selected features from all stages to obtain a discriminative 3D object representation by a cross-attention fusion method. Extensive experiments on benchmark datasets (e.g., ModelNet40, ModelNet10, ShapeNetCore55, and RGBD) clearly demonstrate the effectiveness of our proposed MVCFormer over existing baselines.