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: , locking it in its inactive state. CONCLUSIONS: -mutated NSCLC, adagrasib showed clinical efficacy without new safety signals.
Since the publication of the Revised European-American Classification of Lymphoid Neoplasms in 1994, subsequent updates of the classification of lymphoid neoplasms have been generated through iterativ...
TrackMate is an automated tracking software used to analyze bioimages and is distributed as a Fiji plugin. We illustrate qualitatively and quantitatively that these new capabilities function effective...
Vocabulary is now well recognized as an important focus in language teaching and learning. It also includes a new chapter on out of-classroom learning, which explores the effect of the Internet and el...
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve advanced synthesis results on image data and beyond. By introducing c...
The “Roaring 20s” of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the advanced image classification model. The outcome of this exp...
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks....
We present techniques for scaling Swin Transformer up to 3 billion parameters and making it capable of training with images of up to 1,536x1,536 resolution. Using these techniques and self-supervised...
In this paper, we present Uformer, an effective and efficient Transformer-based architecture for image restoration, in which we build a hierarchical encoder-decoder network using the Transformer block...
This research explores timescale identification decoupling complicated kinetic proc..., contributing new insights to the field of Physics & Space Science.
This document is an Internet-Draft and is in full conformance with all provisions of Section 10 of RFC 2026. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, r...
The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks. The locality o...
Though neural radiance fields (NeRF) have demon-strated impressive view synthesis results on objects and small bounded regions of space, they struggle on “un-bounded” scenes, where the camera may poin...
With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential to investigate ways to adapt these models to downstream datasets. Extensive experiments show that CoCoOp ge...
Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Re-Paint outperforms advanced Autoregressive, and GAN approaches for at least f...
Being able to spot defective parts is a critical component in large-scale industrial manufacturing. We further report competitive results on two additional datasets and also find competitive results i...
We revisit large kernel design in modern convolutional neural networks (CNNs). Our study further reveals that, in contrast to small-kernel CNNs, large-kernel CNNs have much larger effective receptive...
The ability to learn richer network representations generally boosts the performance of deep learning models. Adding a Split-Attention module into the architecture design space of RegNet-Y and FBNetV2...
We introduce Plenoxels (plenoptic voxels), a systemfor photorealistic view synthesis. On standard, benchmark tasks, Plenoxels are optimized two orders of magnitude faster than Neural Radiance Fields w...
We present CSWin Transformer, an efficient and effective Transformer-based backbone for general-purpose vision tasks. By further pretraining on the larger dataset ImageNet-21K, we achieve 87.5% Top-1...