I think @Publons is a criminally underrated / underutilized tool. It collects verified reviews into a public record. If more people used it, we could have a better conversation about review duty. Thoughts?
@ducha_aiki@_krishna_murthy@Publons Interesting, thanks! A priori it seems to me like w/o alternatives advantages outweigh disadvantages (& they need some business model). Peer review anonymity is not 100% watertight anyway. Worried about "malicious" leak or f*ck-up? What's wrong with publishers motivating you?
@pesarlin@Publons I would guess so. So far I have not encountered a review that wasn't approved. It's super simple, you just need to forward a review confirmation e-mail (make sure one is sent, in CMT this is an option afaik) to an e-mail address they provide.
Very nice interview with @SattlerTorsten about Computer Vision between Geometry & Learning, and research trends. Personal takeaways from first ½: AR/VR as stepping stone for robotics; visualization is key to understanding; simulations allow agents to make and test hypotheses.
Here's the 125th!!! @ctdsshow Episode 🙏
I had an amazing chance to interview @SattlerTorsten about 🍵
- His journey in CV
- Mixed Reality
- Localisation
- Traditional CV in 2020 & Robotics
Audio: https://t.co/QakXYk6SL1
Video: https://t.co/V5MhVCfVuD
Hey @todoist , it would be really nice to have "negative priority" / p5 and maybe p6. So that in my "Today" view, I can move "less important than default" tasks back without having to mark all default tasks as higher priority. Should be trivial to implement.
@fdellaert Yes! Recently switched from paper to digital annotations inspired by @wuningxi 's slides and this is so tedious! Though I think some kind of built-in split-screen would be even more important for this particular problem. Please @paperpile 😬?
Time and attention are precious, and we should treat each others’ time and attention with respect. Here is a proposal for some 21st-century rules that should help us do that with modern communication methods: https://t.co/y0lHnz4OB2 Thanks Carmen and @foehnph for reviewing!
@mikko_lauri Thanks! Reliable and fully connected (e.g. multi-hop, see work by @af_robotyk for connectivity. maint.). Single failure can be handled w/ redundancy @ extra cost. Temp. failure should be ok. Assumption only affects work on O(n) dec. place rec., irrelevant for other contribs.
Ph.D. Defense: Multi-Agent Visual SLAM with minimal data exchange: a) O(n²) → O(n) decentr. place recognition, b) minimal feature set with SIPs, c) no more feature descriptors with IMPs, d) optimization is not needed for navigation, nor exploration: https://t.co/mMoLHrODel
@ducha_aiki@jatentaki Good idea! When using cross-entropy, the detector score can even make halfways decent predictions about whether detected points will be correct matches 😉: https://t.co/HL3SyrUT9W
@fdellaert As an aside, I remember that you once said, when visiting ETH, that the contributions that stick around are the simple ones that everybody can understand. This inspired a lot of my "problem picking" choices!
@fdellaert Thank you, Prof. Dellaert! I will upload the thesis after addressing the reviewers' comments. The core papers are linked in the YouTube description: https://t.co/mMoLHrODel
@DesaiDjd Thanks! I will upload the thesis after addressing the reviewers' comments! In the meantime, a lot is already in the core papers (see YouTube description).