TLDR Index
TLDR AI

TLDR AI 2023-04-26

5 sections14 entriesinline urlcomplete parse

Parse notes

  • Subscription/account footer truncated before parsefooter_truncated

01HEADLINES & LAUNCHES

02RESEARCH & INNOVATION

SELF SUPERVISED LEARNING COOKBOOK

It could be argued that a lot of the recent success with deep learning has come on the back of self-supervised tasks. There's not a clear boundary between unsupervised and self-supervised, but where it does exist this paper is a really nice exploration of the methods and all of the various hyperparameters that need to be taken into consideration. Self supervised learning is a really powerful technique and this is a cool way to learn more

arxiv.org55 min read

FULLY SPARSE FUSION FOR 3D OBJECT DETECTION

This paper investigates the integration of image modality into fully sparse architecture for efficient long-range 3D detection, using instance queries to fuse 2D instance segmentation with LiDAR-based 3D segmentation. The resulting framework achieves state-of-the-art results on nuScenes and Argoverse 2 datasets, while being 2.7 times faster than other multimodal 3D detection methods.

arxiv.org6 min read

INTERACTIONS FROM SINGLE MONOCULAR VIDEOS

HOSNeRF is a 360° free-viewpoint rendering method that reconstructs neural radiance fields for dynamic human-object-scene interactions from a single monocular video, addressing challenges like complex object motions and varying interactions. The method significantly outperforms state-of-the-art approaches on two datasets, with a 40%-50% improvement in LPIPS, and provides compelling 360° renderings from single videos.

showlab.github.io8 min read

03ENGINEERING & RESOURCES

TRACK ANYTHING

Based on segment anything and XMem, track anything is a flexible and scalable way to track objects and videos. You can then use the masks for automatic video editing, style transfer, or video recognition tasks.

github.comGitHub repository

SEGMENT ANYTHING IN 3D

SA3D is a novel framework for one-shot 3D segmentation using neural radiance fields, enabling users to segment target objects in rendered views with minimal manual prompting. The iterative process uses 2D masks, density-guided inverse rendering, and cross-view self-prompts for accurate 3D mask learning, completing segmentation in about 2 minutes without optimization.

jumpat.github.io3 min read

HEALTHGPT

HealthGPT is an experimental iOS app that allows users to interact with their health data stored in the Apple Health app using natural language.

github.comGitHub repository

04MISCELLANEOUS

GPT-4 COST $43 MILLION TO TRAIN?

While we don’t know exactly how much it cost to train the newest flagship model from OpenAI - this report breaks down a few potential pieces of information to try and guess how much it might have cost. The amount is mind-blowing (it’s not a lot in absolute terms, but an expensive model nonetheless). There are other nice tidbits in the report around compute usage and an estimate of when we’ll run out of data!

epochai.org7 min read