Makes you wonder what intelligence is lurking in a 10T parameter model like Gemini 3 that we may not discover for some years yet…
I've got a new potential LoRA implementation that I've been testing locally (using a transformed S matrix with frozen U and V weights from an SVD decomposition of the base matrix) that seems to work really well, and I've been playing with both changes to the forward-noising schedule and the loss functions which seem to yield empirically superior results of the standard way of doing things. Epsilon prediction may be old and busted (and working on it makes me really appreciate flow matching!) but there's some really cool stuff happening in its training dynamics that are a lot of fun to explore.
It's just a lot of fun. Great playground for both learning how these things work and for trying out new ideas.
https://gist.github.com/cheald/7d9a436b3f23f27b8d543d805b77f... - here's a quick dump of my SVDLora module though. I wrote it for use in OneTrainer though it should be adaptable to other frameworks easily enough. If you want to try it out, I'd love to hear what you find.
It seems conceptually related to ddpm/ancestral sampling, no? Except they're just adding noise to the intermediate latent to simulate a "trajectory jump". How does your method compare?
A large part of my 2017 phd thesis [0] is dedicated in exploring the formulation and utility of soft validation operators, including this soft IoU, and the extent to which they are "better" / "more reliable" than thresholding (whether this occurs in isolation, or even when marginalised out, as in with the AUC). Long story short, soft operators are at least an order of magnitude more reliable than their thresholding counterparts [1], despite the fact that thresholding still seems to be the industry/academia standard. This is the case for any set-operation-based operator, such as the Dice coefficient (a.k.a. F1-score), not just for the IoU. Recently, influential groups have proposed the matthews correlation coefficient as a "better operator", but still treat it in binary / thresholding terms, which means it's still unreliable to an order of magnitude. I suspect this insight goes beyond images (e.g. the F1-score is often used in ML problems more generally, in situations where probabilistic outputs are thresholded to compare against binary ground truth labels), but I haven't tested that hypothesis explicitly beyond the image domain (yet).
In this work you effectively used the "goedel" (i.e. min/max) fuzzy operator to define fuzzy intersection and union, for the purposes of using it in an IoU operator. There are other fuzzy norms with interesting properties that you can also explore. Other classical ones include product and lukasiewicz. I show in [0] and [1] that these have "best case scenario sub-pixel overlap", "average case" and "worst-case scenario" underlying semantics. (In other words, min/max should not be a random choice of T-norm, but a conscious choice which should match your problem, and what the operator is intended to validate specifically). In my own work, I then proceeded show that if you take gradient direction at the boundary into account, you can come up with a fuzzy intersection/union pair which has directional semantics, and is even more reliable an operator when used to define a soft IoU.
Having said that, in your case you're comparing against a binary ground truth. This collapses all the different T-norms to the same value. I wonder if this is the reason you chose a binary ground truth. If yes, you might want to consider my work, and use original 'soft' ground truths instead, for higher reliability, as well as ability to define intersection semantics.
I hope the above is of interest / use to you :) (and, if you were to decide to cite my work, it wouldn't be the eeeeeend of the world, I gueeeeesss xD )
[0] https://ora.ox.ac.uk/objects/uuid:dc352697-c804-4257-8aec-08...
[1] https://repository.essex.ac.uk/24856/1/Papastylianou.etal201...
onesandofgrain•2mo ago
Kalabint•2mo ago
I think you can find the answer under point 3:
> In this work, our primary goal is to show that pretrained text-to-image diffusion models can be repurposed as object trackers without task-specific finetuning.
Meaning that you can track Objects in Videos without using specialised ML Models for Video Object Tracking.
echelon•2mo ago
The more complex that an eye gets, the more the brain evolves not just the physics and chemistry of optics, but also rich feature sets about predator/prey labels, tracking, movement, self-localization, distance, etc.
These might not be separate things. These things might just come "for free".
fxtentacle•2mo ago
If you train a system to memorize A-B pairs and then you normally use it to find B when given A, then it's not surprising that finding A when given B also works, because you trained it in an almost symmetrical fashion on A-B pairs, which are, obviously, also B-A pairs.
jacquesm•2mo ago
https://en.wikipedia.org/wiki/Lateral_geniculate_nucleus
So the brain does not necessarily receive 'raw' images to process to begin with, there is already a lot of high level data extracted at that point such as optical flow to detect moving objects.
Mkengin•2mo ago
DrierCycle•2mo ago
magicalhippo•2mo ago
Someone previously found that that the cross-attention layers in text-to-image diffusion models captures correlation between the input text tokens and corresponding image regions, so that one can use this to segment the image, pixels containing "cat" for example. However this segmentation was rather coarse. The authors of this paper found that also using the self-attention layers leads to a much more detailed segmentation.
They then extend this to video by using the self-attention between two consecutive frames to determine how the segmentation changes from one frame to the next.
Now, text-to-image diffusion models require a text input to generate the image to begin with. From what I can gather they limit themselves to semi-supervised video segmentation, so that the first frame is already segmented by say a human or some other process.
They then run a "inversion" procedure which tries to generate text that causes the text-to-image diffusion model to segment the first frame as closely as possible to the provided segmentation.
With the text in hand, they can then run the earlier segmentation propagation steps to track the segmented object throughout the video.
The key here is that the text-to-image diffusion model is pretrained, and not fine-tuned for this task.
That said, I'm no expert.
jacquesm•2mo ago
nicolailolansen•2mo ago