Categories: FAANG

KPConvX: Modernizing Kernel Point Convolution with Kernel Attention

In the field of deep point cloud understanding, KPConv is a unique architecture that uses kernel points to locate convolutional weights in space, instead of relying on Multi-Layer Perceptron (MLP) encodings. While it initially achieved success, it has since been surpassed by recent MLP networks that employ updated designs and training strategies. Building upon the kernel point principle, we present two novel designs: KPConvD (depthwise KPConv), a lighter design that enables the use of deeper architectures, and KPConvX, an innovative design that scales the depthwise convolutional weights of…
AI Generated Robotic Content

Recent Posts

Best guess as to which tools were used for this? VACE v2v?

credit to @ unreelinc submitted by /u/Leading_Primary_8447 [link] [comments]

2 hours ago

Calculating What Your Bank Spends on Marketing Compliance Reviews

By Taylor Mahoney, VP of Solutions ConsultingPicture this. The Federal Reserve has just dropped interest…

2 hours ago

AlphaGenome: AI for better understanding the genome

Introducing a new, unifying DNA sequence model that advances regulatory variant-effect prediction and promises to…

2 hours ago

TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining

This paper was accepted to the ACL 2025 main conference as an oral presentation. This…

2 hours ago

Build an intelligent multi-agent business expert using Amazon Bedrock

In this post, we demonstrate how to build a multi-agent system using multi-agent collaboration in…

2 hours ago

How Schroders built its multi-agent financial analysis research assistant

Financial analysts spend hours grappling with ever-increasing volumes of market and company data to extract…

2 hours ago