Vincent Sitzmann ✓

@vincesitzmann

Building AI that learns by interacting with the world. Associate Professor @ MIT, leading the Scene Representation Group (https://t.co/h5gvhLYZj4).

20 095Followers

331Following

965Posts total

451.9KViews on collected posts

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Vincent Sitzmann ✓@vincesitzmann17 Feb

@vincesitzmann This is the only point of yours that I disagree with, I think. Much of the "computer vision" community is primarily concerned with graphics and generation, and those don't seem to be reducible to the "map an image to an action" robotics task. 794 views · 8 likes · 1 reposts · 2 replies Open on X →

Vincent Sitzmann ✓@vincesitzmann16 Feb

@vincesitzmann What about a radiologist who just want to know if this MRI had a tumor or no, no action is needed just wanting to know the truth , does this count as a computer vision problem ? 5.2K views · 23 likes · 0 reposts · 3 replies Open on X →

Vincent Sitzmann ✓@vincesitzmann16 Feb

I believe that the differentiation between "robot learning" and "computer vision" does not make sense anymore in our present time. We as computer vision researchers should proactively tackle the question of decision-making, and should not shy away from learning policies :) 23.6K views · 82 likes · 1 reposts · 2 replies Open on X →

Vincent Sitzmann ✓@vincesitzmann16 Feb

Would love to discuss - these are all *opinions*, and I only seek to share my own thinking and the "bitter" lessons I have learned, having spent several years working on 3D computer vision-even though folks smarter than me confronted me with some of these same questions early on! 9.4K views · 36 likes · 0 reposts · 2 replies Open on X →

Vincent Sitzmann ✓@vincesitzmann16 Feb

In my recent blog post, I argue that "vision" is only well-defined as part of perception-action loops, and that the conventional view of computer vision - mapping imagery to intermediate representations (3D, flow, segmentation...) is about to go away. https://t.co/aFmE9CHHau 397.1K views · 1.1K likes · 162 reposts · 43 replies Open on X →

Vincent Sitzmann ✓@vincesitzmann16 Feb

I present this as a corollary of the bitter lesson, which folks readily apply to *model architectures*, purging models of inductive biases, but less so to *intermediate representations* - yet, point clouds, NeRFs, camera poses, etc. are clever, task-specific tricks just the same. 15.8K views · 55 likes · 1 reposts · 1 replies Open on X →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

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Last 6 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

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Reactions — likes, reposts, replies and quotes — divided by views.

What the audience does

Likes53.2%1 257 in total

Reposts7.0%165 in total

Replies2.2%53 in total

Quotes3.4%80 in total

Bookmarks34.2%807 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Posts recentes

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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