Deep Learning

Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery

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Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery

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

Multi-Token Prediction (MTP) in V3: Inference Acceleration

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Multi-Token Prediction (MTP) in V3: Inference Acceleration

Last time we discussed the structure of the Deepseek model. Today, I will share the inference acceleration tool in V3, the multi-token prediction (MTP) module, which is somewhat similar to speculative decoding, but the specific code has not been open-sourced. Speculative decoding was introduced in a series of articles on inference optimization, which essentially changes … Read more

FPGA: The Next Generation Robot Perception Processor

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FPGA: The Next Generation Robot Perception Processor

Author Introduction: Liu Shaoshan, co-founder of PerceptIn. PhD in Computer Science from the University of California, Irvine, with research interests in intelligent perception computing, system software, architecture, and heterogeneous computing (FPGA, GPU). Currently, PerceptIn focuses on core SLAM technology for augmented reality, virtual reality, and robotics, as well as its implementation and optimization on smart … Read more

Latest Overview of Global Autonomous Driving AI Chips

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Latest Overview of Global Autonomous Driving AI Chips

Click the “Computer Vision Life” above and select “Star”. Quickly obtain the latest insights. 1. Introduction The intelligence of autonomous vehicles depends on algorithms, leading to the emergence and popularity of the concept of software-defined vehicles. However, to realize software-defined vehicles, a hardware computing platform or domain controller capable of supporting highly intelligent and computationally … Read more

Hailo’s Deep Learning Chips: Turbocharging Data Processing for Autonomous Vehicles

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Hailo's Deep Learning Chips: Turbocharging Data Processing for Autonomous Vehicles

By | Dazhuang Lv Reported by AI-Drive AI-Drive Note: The autonomous driving industry is not just about the few leading giants! Recently, Israeli AI startup Hailo Technologies completed a Series A funding round, raising $12.5 million. This funding will be used for the development of deep learning chips to help edge computing devices perform real-time … Read more

Predicting Chromatin Accessibility in Drosophila Using CNN and Multi-Head Attention

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Predicting Chromatin Accessibility in Drosophila Using CNN and Multi-Head Attention

Chromatin accessibility (open chromatin accessibility) has gained increasing attention in the context of gene regulation and evolution, but our understanding of it remains limited. There is particularly little knowledge about how chromatin accessibility develops and evolves. Recently, the Zhao Li laboratory at The Rockefeller University published a research paper titled The evolution and mutational robustness … Read more

Multimix: Semi-Supervised, Explainable Multi-Task Learning from Medical Images

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Multimix: Semi-Supervised, Explainable Multi-Task Learning from Medical Images

Source: DeepHub IMBA This article is about 4000 words long and is recommended for a reading time of over 10 minutes. In this article, I will discuss a new semi-supervised, multi-task medical imaging method. In this article, I will discuss a new semi-supervised, multi-task medical imaging method called Multimix, authored by Ayana Haque (ME), Abdullah-Al-Zubaer … Read more

Multi-Modal Multi-Task Masked Autoencoder: A Simple, Flexible, and Effective ViT Pre-Training Strategy

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Multi-Modal Multi-Task Masked Autoencoder: A Simple, Flexible, and Effective ViT Pre-Training Strategy

Source: Deephub Imba This article is about 1000 words long and is recommended to read in 4 minutes. This article introduces a simple, flexible, and effective pre-training strategy for ViT. MAE is a ViT that uses a self-supervised pre-training strategy, masking patches in the input image and then predicting the missing areas for sub-supervision and … Read more

Understanding Multi-Head Attention in NLP

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Understanding Multi-Head Attention in NLP

1. Multi-Head Attention Multi-Head Attention is a widely adopted extension of the attention mechanism in the Transformer model. It captures different attention distributions in various subspaces of the input sequence by running multiple independent attention mechanisms in parallel, thereby comprehensively capturing the various semantic associations present in the sequence. In Multi-Head Attention, the input sequence … Read more

Understanding Pathways: Single-controller vs Multi-controller

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Understanding Pathways: Single-controller vs Multi-controller

Source: OneFlow This article is approximately 7732 words long and is recommended to be read in 12 minutes. This article introduces the background of Pathways and provides an in-depth analysis. 01. Why Discuss Pathways? In the past two years, TensorFlow has been caught off guard by the rise of PyTorch, and the entire industry is … Read more