Graphs offer a flexible structure for vision tasks, with CNNs and Transformers conditioned as two specific cases of graph structures. In CNNs, the input images are treated as graphs where only neighboring patches are connected, whereas Transformers view images as fully connected graphs. To leverage the potential of graphs in video representation learning, effective graph generation and training methods are crucial. To this end, we propose VideoGNN, which represents the video as a discrete time dynamic graph and learns the dynamic graph efficiently. Given the multitude of frames in videos, we introduce an efficient graph generation module characterized by low complexity and high quality, facilitating the transformation of videos into dynamic graphs. Additionally, we introduce a dual-view graph neural network to capture spatial and temporal information from the generated dynamic graphs. Then, a sequential model is applied to capture the long-term temporal information and generate the final frame embeddings. Experiments demonstrate that VideoGNN can achieve competitive results in terms of graph quality assessment and video downstream tasks. The codes are available at https://github.com/Dodo-D-Caster/VideoGNN .

Conventional Image Signal Processors (ISPs) employ manually crafted designs with limited adaptability, resulting in suboptimal performance in dynamic environments for both visual quality and machine vision applications. While deep learning facilitates adaptive AI-ISPs, supervised approaches encounter domain shift limitations and substantial computational demands that impede edge deployment. This work introduces an adaptive zero-shot AI-ISP that dynamically optimizes processing pipelines without requiring paired training data. The proposed architecture implements dual specialized subnetworks for illumination estimation and denoising enhancement, operating collaboratively under Retinex theory principles to achieve boundary-aware illumination mapping and noise-resilient image restoration. Additionally, a physically constrained loss function is introduced to enhance color fidelity and noise suppression. For practical implementation, an FPGA-accelerated computing engine replaces transposed convolution with optimized bilinear interpolation, effectively eliminating artifacting while achieving superior memory efficiency through customized buffering architectures. A comprehensive evaluation demonstrates highly competitive performance, achieving a PSNR of 19.91/16.62 and an SSIM of 0.591/0.475 on LSRW-Huawei/Nikon datasets, alongside NIQE scores of 2.065/3.025 on DCIM and TM-DIED datasets. The hardware implementation attains 42.5 GOPS/W power efficiency, representing 35.4× and 7.3× improvements over conventional CPU and GPU platforms, establishing a comprehensive edge deployment solution for next-generation intelligent image processing systems.

Retrieve the Chengdu Chongqing biomedical patents from 2018 to 2022 in the patent retrieval database of the China National Intellectual Property Administration of the People's Republic of China, and analyze the potential opportunities for the combination of patent IPC classification number characteristics from the perspective of enterprises, universities, hospitals, and scientific research institutions.