2.【基础网络架构:CNN】Shift-ConvNets: Small Convolutional Kernel with Large Kernel Effects 论文地址:arxiv.org//pdf/2401.127 开源代码:github.com/lidc54/shift 3.【异常分割】ClipSAM: CLIP and SAM Collaboration for Zero-Shot
来自专栏 · CV每日Paper with code 9 人赞同了该文章 点击@CV计算机视觉,关注更多CV干货 1.【基础网络架构:CNN】Robust Mixture-of-Expert Training for Convolutional Neural Networks 论文地址:arxiv.org//pdf/2308.101 开源代码:github.com/OPTML-Group/ 2.【图像分类】CoNe: Contrast Your Neighbours for ...
CoAt=Convolution + Attention,paperwithcode榜单第一名,通过结合卷积与Transformer实现性能上的突破,方法部分设计非常规整,层层深入考虑模型的架构设计。 引言 Transformer模型的容量大,由于缺乏正确的归纳偏置,泛化能力要比卷积网络差。 提出了CoAtNets模型族: 深度可分离卷积与self-attention能够通过简单的相对注意力来统一...
https://paperswithcode.com/sota/semantic-segmentation-on-ade20k-val 最近Transformer的文章眼花缭乱,但是精度和速度相较于CNN而言还是差点意思,直到Swin Transformer的出现,让人感觉到了一丝丝激动,Swin Transformer可能是CNN的完美替代方案。 作者分析表明,Transformer从NLP迁移到CV上没有大放异彩主要有两点原因: ...
这里有Code+Paper 安妮 编译自 Github 量子位出品 | 公众号 QbitAI 说话人确认(Speaker Verification)是一种以语言特性确认说话人身份的技术。 近日,西弗吉尼亚大学的博士生Amirsina Torfi在Github上发布了用3D卷积神经网络(后简称3D-CNN)确认说话人身份的代码,并公布了研究论文。
code for the paper "In Conclusion Not Repetition:Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point Processes" - thinkwee/DPP_CNN_Summarization
May, 2022: It was found that newertorchaudiopackage has different behavior with older ones in SpecAugment and will cause abug. We find a workaround and fixed it. If you are interested, seehere. March, 2022: We released a new preprintCMKD: CNN/Transformer-Based Cross-Model Knowledge Distilla...
https://github.com/UKPLab/emnlp2017-bilstm-cnn-crf Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering @jamiechoi 推荐 目前coco leaderboard 第二名,来自微软的论文。 提出了自下而上(bottom-up)和自上而下的 attention (top-down) 机制。其中 bottom-up 是利用 Faster...
Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision. When training data exhibit class imbalances, the class-wise reweighted version of logistic and softmax losses are often used to boost ...
Paper tables with annotated results for Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering