Explore the latest peer-reviewed research with AI-generated summaries, key findings, and insights for easy understanding. All content is legally sourced from academic metadata with links to original papers.
BACKGROUND: Among patients with resectable early-stage non-small-cell lung cancer (NSCLC), a perioperative approach that includes both neoadjuvant and adjuvant immune checkpoint inhibition may provide...
Charge and Energy Transfer Dynamics in Molecular Systems Comprehensive resource offering knowledge on charge and energy transfer dynamics in molecular systems and nanostructures Charge and Energy Tran...
Real-time object detection is one of the most important research topics in computer vision. YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 120 FPS an...
To design fast neural networks, many works have been focusing on reducing the number of floating-point operations (FLOPs). Our large FasterNet-L achieves impressive 83.5% top-1 accuracy, on par with t...
Large text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text prompt. We also provide a new dataset and evaluati...
Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. This co-design...
We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image....
For over two decades, the Concussion in Sport Group has held meetings and developed five international statements on concussion in sport. This consensus process also integrated new features including...
As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. Empirical results across several computer vision tasks such as image classification, o...
Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. Extensive experiments show the effectiveness of the proposed modules, and we furt...
Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. The effectivenes...
Kalman filter (KF) based methods for multi-object tracking (MOT) make an assumption that objects move linearly. It achieves advanced on multiple datasets, including MOT17, MOT20, KITTI, head tracking,...
This research explores Ten concerns of Zn metal anode for rechargeable aqueous zinc..., contributing new insights to the field of Engineering & Technology.
Multi-modality (MM) image fusion aims to render fused images that maintain the merits of different modalities, e.g., functional highlight and detailed textures. We also show that CDDFuse can boost the...
Vision transformers have shown great success due to their high model capabilities. Compared to the recent efficient model MobileViT-XXS, EfficientViT-M2 achieves 1.8% superior accuracy, while running...
Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks. Compared with the advanced method Co-CoOp, MaPLe exhibits favorable performance a...
DreamFusion has recently demonstrated the utility of a pretrained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF) , achieving remarkable text-to-3D synthesis results. User stud...
Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. We instantiate UniAD on the challenging nuScenes benchmark.
We present ImageBind, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. The emergent capabilities improve with the strength of...
Convolutional Neural Networks (CNNs) have achieved remarkable performance in various computer vision tasks but this comes at the cost of tremendous computational resources, partly due to convolutional...