High-quality reduced graphene oxide-nanocrystalline platinum hybrid materials prepared by simultaneous co-reduction of graphene oxide and chloroplatinic acid

Reduced graphene oxide-nanocrystalline platinum (RGO-Pt) hybrid materials were synthesized by simultaneous co-reduction of graphene oxide (GO) and chloroplatinic acid with sodium citrate in water at 80°C, of pH 7 and 10. The resultant RGO-Pt hybrid materials were characterized using transmission electron microscopy (TEM), powder X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared spectroscopy, and thermogravimetric analysis. Platinum (Pt) nanoparticles were anchored randomly onto the reduced GO (RGO) sheets with average mean diameters of 1.76 (pH 7) and 1.93 nm (pH 10). The significant Pt diffraction peaks and the decreased intensity of (002) peak in the XRD patterns of RGO-Pt hybrid materials confirmed that the Pt nanoparticles were anchored onto the RGO sheets and intercalated into the stacked RGO layers at these two pH values. The Pt loadings for the hybrid materials were determined as 36.83 (pH 7) and 49.18% (pH 10) by mass using XPS analysis. With the assistance of oleylamine, the resultant RGO-Pt hybrid materials were soluble in the nonpolar organic solvents, and the dispersion could remain stable for several months

Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches

Deep learning methods have shown remarkable performance in image denoising, particularly when trained on large-scale paired datasets. However, acquiring such paired datasets for real-world scenarios poses a significant challenge. Although unsupervised approaches based on generative adversarial networks offer a promising solution for denoising without paired datasets, they are difficult in surpassing the performance limitations of conventional GAN-based unsupervised frameworks without significantly modifying existing structures or increasing the computational complexity of denoisers. To address this problem, we propose a SC strategy for multiple denoisers. This strategy can achieve significant performance improvement without increasing the inference complexity of the GAN-based denoising framework. Its basic idea is to iteratively replace the previous less powerful denoiser in the filter-guided noise extraction module with the current powerful denoiser. This process generates better synthetic clean-noisy image pairs, leading to a more powerful denoiser for the next iteration. This baseline ensures the stability and effectiveness of the training network. The experimental results demonstrate the superiority of our method over state-of-the-art unsupervised methods.Comment: Accepted to ICCV 202

ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation

Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a two-stage scheme. The general idea is to first generate class-agnostic region proposals and then feed the cropped proposal regions to CLIP to utilize its image-level zero-shot classification capability. While effective, such a scheme requires two image encoders, one for proposal generation and one for CLIP, leading to a complicated pipeline and high computational cost. In this work, we pursue a simpler-and-efficient one-stage solution that directly extends CLIP's zero-shot prediction capability from image to pixel level. Our investigation starts with a straightforward extension as our baseline that generates semantic masks by comparing the similarity between text and patch embeddings extracted from CLIP. However, such a paradigm could heavily overfit the seen classes and fail to generalize to unseen classes. To handle this issue, we propose three simple-but-effective designs and figure out that they can significantly retain the inherent zero-shot capacity of CLIP and improve pixel-level generalization ability. Incorporating those modifications leads to an efficient zero-shot semantic segmentation system called ZegCLIP. Through extensive experiments on three public benchmarks, ZegCLIP demonstrates superior performance, outperforming the state-of-the-art methods by a large margin under both "inductive" and "transductive" zero-shot settings. In addition, compared with the two-stage method, our one-stage ZegCLIP achieves a speedup of about 5 times faster during inference. We release the code at https://github.com/ZiqinZhou66/ZegCLIP.git.Comment: 12 pages, 8 figure