It’s another pleasant weekend for learning and Q&A time (•́へ•́╬) What are SIMT and SIMD? When discussing SIMT (Single Instruction, Multiple Threads) and SIMD (Single Instruction, Multiple Data), we are dealing with two common models of parallel computing. Both are technologies used to handle large-scale data and improve computational efficiency. Here is a brief introduction … Read more
From | Zhihu Author | Anticoder Link | https://zhuanlan.zhihu.com/p/59413549 Background: Focusing only on a single model may overlook potential information that could enhance the target task from related tasks. By sharing parameters to some extent among different tasks, the original task may generalize better. Generally speaking, as long as there are multiple losses, it counts … Read more
Join the professional CV group at Jishi, and interact with 10,000+ visual developers from top universities and companies such as HKUST, Peking University, Tsinghua University, Chinese Academy of Sciences, CMU, Tencent, Baidu! We also provide monthly live sharing sessions with experts, real project demand connections, and a summary of valuable information for industry technical exchanges. … Read more
Join the professional CV group at Jishi, and interact with 10,000+ visual developers from prestigious institutions like HKUST, Peking University, Tsinghua University, Chinese Academy of Sciences, CMU, Tencent, Baidu, and more! We also provide monthly expert live streams, real project demand connections, valuable information summaries, and industry technical exchanges. Follow the Jishi Platform public account, … Read more
According to Lei Feng Network, this article is based on Tan Xu’s answer to the question “How to Understand Nvidia’s Multi-GPU Communication Framework NCCL?” on Zhihu, and Lei Feng Network has obtained authorization for reprint. Question Details: In deep learning, multi-GPU parallel training is often required, and Nvidia’s NCCL library NVIDIA/nccl (https://github.com/NVIDIA/nccl) is frequently used … Read more
↑ ClickBlue Text Follow the Jishi PlatformAuthor | Yumu Linfeng Source | AICV and Frontiers Editor | Jishi Platform Jishi Introduction The article proposes a new Superpixel Token Interaction Network (SPIN). This method uses superpixels to cluster locally similar pixels, forming interpretable local regions and achieving local information interaction through attention within superpixels. >> Join … Read more
↑ ClickBlue TextFollow the Jishi platformAuthor丨Pai Pai XingSource丨CVHub Jishi Introduction This article presents a simple yet efficient modern inverted residual mobile module designed for mobile applications. The proposed efficient model (Efficient MOdel, EMO) achieves excellent overall performance on the ImageNet-1K, COCO2017, and ADE20K benchmarks, surpassing the SOTA models based on CNN/Transformer at the same computational … Read more
The popularity of large-scale language models like ChatGPT and GPT-4 has quickly ignited public enthusiasm for artificial intelligence, drawing strong attention from the industry towards AI chips. Compared to general-purpose chips like CPUs and GPUs, NPUs (Neural Processing Units) can handle AI workloads with simpler control flows, higher efficiency, and lower power consumption. With the … Read more
NPU and heterogeneous computing take the lead? Author| Zhou Ya Image| Midjourney Today’s technological era resembles the .com world over 20 years ago. When the internet emerged, there were voices proclaiming that “every computer would connect to the internet,” and now, the same voices are emerging around personal computers (PC), with the keyword being AI. … Read more
Introduction This article is an interpretation of the paper Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot by VCC student Yu Tao. This work comes from the European NAVER laboratory and has been published at the top computer vision conference ECCV 2024. Project homepage: https://europe.naverlabs.com/blog/whole-body-human-mesh-recovery-of-multiple-persons-from-a-single-image/This work proposes a method called Multi-HMR to recover … Read more
