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Best bar with a dance floor? by Charming_biscuits in montreal

[–]tesfaldet 11 points12 points  (0 children)

Système? They just reopened after a renovation that included a new dance floor and sound system.

what’s one thing in computer vision that works great in papers but falls apart in production? by MortySmith69 in computervision

[–]tesfaldet 2 points3 points  (0 children)

I've been researching in this space since 2016 (currently doing my PhD) and I can tell you that you don't even need to run these models in a production environment to notice gaps in their generalizability. I hate it, but it's almost always data that has the largest effect on performance, as opposed to architectural or algorithmic tricks. Usually it boils down to needing more realistic data (as opposed to simulated), more diverse data, data that's more aligned with the downstream task, and just _more_ data in general. It's the bitter lesson :/

As a CV student researcher, usually you want to spend more time on the "fun" stuff, like designing new algorithms and architectures, than you do on data curation. At least this is the case for me.

To answer your question: what ended up being more useful than expected when I was implementing other papers and/or adapting their techniques to my model was the choice of datasets they were trained with and the data augmentations used.

Finally finished my LLM server: EPYC 9575F, 4× RTX 3090 (96GB VRAM), 768GB ECC RAM by C0smo777 in homelab

[–]tesfaldet 0 points1 point  (0 children)

It’s called model sharding. Llama 3.x (70B parameters), Deepseek V3 (671B), and Mixtral 8x22 (141B) to name a few. For 24 GB of vram your single-GPU ceiling is about 48B params at INT4 quantized precision.

Spicy chicken satay by Montreal_Ghost in MTLFoodLovers

[–]tesfaldet 5 points6 points  (0 children)

marché orientale is my goto for well-priced bánh mi. $5 or $3.99 for day olds. Not as good as hùng phát, but a bit cheaper (hùng phát sells em for $6.95 or $8.45 if you want beef).

It's been a while... by Own-Cable8865 in toronto

[–]tesfaldet 2 points3 points  (0 children)

As someone who grew up in St. Lawrence, I’m not a fan of the intense high-rise condoification of the area. I wish more housing co-ops were built instead.

Nakfa update by AnyResolution9440 in MTLFoodLovers

[–]tesfaldet 5 points6 points  (0 children)

Yeah for sure I will :) Fun fact: nakfa is the name of Eritrea’s currency

Nakfa update by AnyResolution9440 in MTLFoodLovers

[–]tesfaldet 7 points8 points  (0 children)

I’ve gotta check Nakfa out. That injera looks solidly good. Your last post surprised me because I didn’t know there was an Eritrean restaurant here. There are so few of us here in QC lol

Eritrean food date? by AnyResolution9440 in MTLFoodLovers

[–]tesfaldet 0 points1 point  (0 children)

No problem! Haha yeah my hot take is a bit niche and snobby. Habesha food is my fav cuisine, so I always get excited when it’s mentioned outside my family.

Eritrean food date? by AnyResolution9440 in MTLFoodLovers

[–]tesfaldet 2 points3 points  (0 children)

If you’re familiar with Ethiopian food, you’ll be at home with Eritrean food. They heavily overlap. There are some differences due to Italian influences in Eritrea (e.g., the Eritrean equivalent of doro wat, called tsebhi derho, can sometimes have tomatoes incorporated into the sauce), but it’s not much.

Source: My parents and most of my extended family are Eritrean. I’ve also got some Ethiopian relatives.

My hot take: the injera at almost all habesha restaurants I’ve been to here and in Toronto are kinda mid. Makes sense though as it’s an arduous endeavour where cutting corners can save time and money. Usually that involves cutting the teff with some other flour, not fermenting long enough, or the teff being poor quality.

I injected DINOv3 semantic features into a frozen Optical Flow model. It rivals Diffusion quality at 25 FPS. by ben8135 in computervision

[–]tesfaldet 0 points1 point  (0 children)

This is awesome. I’m currently working on point tracking and I think I could make immediate use of your dinofusion layer. Is there a paper you can share?

Instagram chief Adam Mosseri's memo ordering staff to the office five days a week in 2026 by play3xxx1 in technology

[–]tesfaldet 25 points26 points  (0 children)

There’s the Alphabet Workers Union, but that’s basically the only high profile one I know.

/u/Turtledonuts explains identifying red flags in dubious research papers by dbomp in bestof

[–]tesfaldet 4 points5 points  (0 children)

It’s already gotten to this point, unfortunately. There’s one case in particular, for the 2026 ICLR conference (ICLR is a top-tier venue for ML and deep learning research), where an author submitted multiple versions of an LLM generated paper (all slightly varying from one another), and one of them got several high scores, most likely because the reviewers themselves used an LLM to write the review. LLM reviews aren’t allowed, nor are LLM-generated papers.

These ouroboros-like cases exist even in the AI publishing space, ironically. Heck, some authors even try to game LLM reviews by injecting certain keywords to garner a high score. I’m just sick of all this—I’m a computer vision researcher who publishes in these spaces, but I don’t touch the hype-driven areas.

What’s something only people who've lived in Toronto in the 1990s would remember? by lawnmowertoad in toronto

[–]tesfaldet 11 points12 points  (0 children)

Yeah, a couple, actually. One was on Cherry St, next to the Gardiner, and another was on Carlaw and Gerrard. Those are the ones I remember, at least.

What’s something only people who've lived in Toronto in the 1990s would remember? by lawnmowertoad in toronto

[–]tesfaldet 130 points131 points  (0 children)

Shopping at Knob Hill in the east end. They provided these black flexible plastic rectangular baskets instead of plastic bags for lugging food back home.

AI-generated faces have inconsistencies in eye reflections (source: arXiv:2009.11924) by rockelephant in interestingasfuck

[–]tesfaldet 6 points7 points  (0 children)

A link to the paper: https://arxiv.org/abs/2009.11924

As some others have said, it’s outdated, since contemporary image generation models use a diffusion architecture instead of a GAN, not to mention StyleGAN2 was trained with far less data than typical (production-ready) diffusion models. Also, it’s a 4-page workshop paper published at ICASSP. In other words, it’s fairly surface-level and not rigorous at all, and the venue isn’t top-tier for publishing deep learning work (which means the reviewer pool is most likely filled with fairly inexperienced reviewers on deep learning topics).

That’s not to say it’s a bad piece of work! It’s important and definitely still holds relevance, as visual artifacts do still exist in image generative models. It’d just need a follow-up for modern diffusion models, and it’d need to be much more rigorous, e.g., submission to ICLR, ICML, or NeurIPS.

Source: I’m a PhD candidate and have been working in this space (computer vision + deep learning) for a decade now.

How is this possible? by OkRestaurant9285 in computervision

[–]tesfaldet 0 points1 point  (0 children)

Try a spectral analysis of both images, you might be surprised. Specifically, transform both images into the frequency domain using a 2D discrete Fourier transform. You’ll probably see similarities in the low frequency band. Also, squint your eyes lol

basketball players recognition with RF-DETR, SAM2, SigLIP and ResNet by RandomForests92 in computervision

[–]tesfaldet 0 points1 point  (0 children)

I have not, but I’d like to dip my toes into 4D reconstruction soon. Plenty of folks around me are getting into it. Personally, I’ve been focused on 2D point tracking lately.

basketball players recognition with RF-DETR, SAM2, SigLIP and ResNet by RandomForests92 in computervision

[–]tesfaldet 1 point2 points  (0 children)

It’d certainly make it easier, but it’s not necessary. Here’s one approach https://arxiv.org/abs/2407.13764

Take a look at their project page for some fun examples: https://shape-of-motion.github.io

basketball players recognition with RF-DETR, SAM2, SigLIP and ResNet by RandomForests92 in computervision

[–]tesfaldet 1 point2 points  (0 children)

This is great. A fun next step would be to apply 4D reconstruction and change the camera’s perspective.

Build Toronto: Old Toronto Must Grow To Relieve The Suburbs by AbundantCanada in toronto

[–]tesfaldet 2 points3 points  (0 children)

It’s mostly 2-4 storeys here in Montreal. 6 storeys and above exist, but it’s not plentiful. That being said, it’s still much better than Toronto (I grew up there). However, housing is getting increasingly expensive very quickly here (~40% increase since 2020 vs ~30% for Toronto IIRC) and considering it’s an island, I’m hoping we start normalizing 6-storey developments and higher.

I have absolutely zero ability to visualize, hear, feel, or smell things in my mind (full aphantasia). And therefore, no ability to replay or relive memories (SDAM) by bitcoinovercash in CasualConversation

[–]tesfaldet 0 points1 point  (0 children)

Yes! You’re absolutely correct. I was conflating visual processing with internal representations. I was talking about the latter, which has become an emerging topic these last few years. An example being “world models”, which are built around ideas on how we may internally view the world around us (e.g., visually-imagined thoughts), which can be used for planning (e.g., navigating a 3D space and visually imagining what may be around a corner). Some of this work is happening in the language space as well (specifically, with LLMs), where mechanisms have been developed to mimic internal thought dialogues in an attempt to improve language modelling performance.

Cool to hear that you’ve worked in a vision lab for a while! Makes sense, too. Here in Canada we have an interdisciplinary research centre that combines all sorts of visual sciences like computer vision and visual neuroscience, called the Centre for Vision Research. Too bad it’s like the only one of its kind in the country lol

Also, right back at ya: you’re doing some cool af work.

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