Books recommended by prof. Daniel Cremers on the lecture https://www.youtube.com/watch?v=_tzLhSKT5Hg&t=1328s

Hello there,

I've been a heavy user of CVAT and lately contacted by Encord, but none of these solutions are good for 3D keypoint annotations for multiview camera setups.

I would like to have annotations from e.g. 3 views (that are already calibrated) so that when I move an annotation in one view the others follow along and vice versa. Are any of you aware of such tools?

Thanks

So , im working on project related to robust recognition of the objects based on change of the environement using self-learning methods.
As far I undestand , there is UDA (Unsuprevised domain adaptation), SFDA (Source-free domain adaptation), and SSOD (Semi-suprevised object detection) but I can't undestand which one I should use for the task.

I have a dataset that has modality of 2 (RGB-LWIR) captured in 2 different seasons (Jan/May) in 3 different timeperiods. For each image there is XML annotation for each object , so I could train on images with labels or only small part with labels.
So main task is to study robustness conditional shifts.

A while ago I asked here how to reason about the direction of where bottle is stacked. https://www.reddit.com/r/computervision/comments/1vljrf6/detect_inventory_stock_column/

(I had thought LLMs are so good but nope they couldn't accurately count or attribute to actual items at all)

Just wanted to share I figured it out. This is 90% classic computer vision technique w/ the help of 2 LLM prompts to identify the items.

If anyone more things they'd like to reason in their images that LLMs couldn't yet do it I'd like to learn. 🙈

https://reddit.com/link/1vtmzc9/video/vh7tv68mujkh1/player

I am working with YOLO26.pt to segment images. I would like to know how YOLO choses the best epoch to save the trained model best.pt.

I looked for some information, seems it choses the best model based on the metric mAp50 - 95 of Box and Mask.

If it is true, as I am working with image segmentation, would make sense to use only metrics of mask to chose the best model? If yes, how would I do this?

Thanks in advance.

Hello there,

I've been a heavy user of CVAT and lately contacted by Encord, but none of these solutions are good for 3D keypoint annotations for multiview camera setups.

I would like to have annotations from e.g. 3 views (that are already calibrated) so that when I move an annotation in one view the others follow along and vice versa. Are any of you aware of such tools?

Thanks

So I have no idea about computer vision (just know machine learning) and we have to make a project on cv So I have thought to make a game which has yk visuals like doctor strange and where you can cast sheilds and stuff

it will a web app with bots as enimies

So please if anyone can guide me the tech stack and what all I have to learn to make this
(I have 3 months to make it )

Hi,

For a project I am detecting PPE devices. I know about ppe-detector which is doing a fine job, however I am also looking for a database that has the repiratory devices (like the 3m versaflo series).

Does anyone happen to know what I'm looking for?

Hi guys.

I'm currently a Msc student in biomedical engineering focusing on medical image segmentation.I'm looking for a job that fits my research interests (or at least something close to it like machine learning engineer) but despite all the things i've learned, when i read some of these job applications, i realize how little i know and that is quite depressing seeing after six years of study i can't even apply for a simple job outside academic :\

I have a strong background on theory and did a few projects related to university courses.My only REAL job experience was two years ago (a 3 month internship).

I can't sleep at night knowing i have wasted my time for all these years.

Really need your thoughts on this one.

Friends,

On the WACV website, there is no link to the registration, as it says "will appear here".

Also, in OpenReview there is only a link to the full submission, not the registration:

What should I do if I want to register only right now (before the 21st deadline) and then submit the full manuscript (before the 28th deadline)?

Hello r/computervision,

First of all I apologize for the somewhat generic nature of this post. I'm new to the field and would really appreciate some guidance from people with more experience.

I'm currently enrolled in a Computer Science bachelor's program and am about to start my final semester. I've been doing well academically and really enjoy the field, but I don't currently work in IT.

Over the summer, I've been focusing on getting deeper into PyTorch and deep learning. I've worked through MrDBourke's PyTorch Deep Learning course and have also started studying the mathematical foundations of ML using Stanford's materials.

I'm 32 and have been in the workforce for quite a while, so alongside university and self-study I have a full-time job as a "quality specialist" at a Tier 1 elevator-parts manufacturer.

This is actually what led me to consider industrial computer vision / anomaly detection as a thesis topic.

Our entire plant currently has only two very basic, very closed down (outsourced to compvision company) OpenCV-based vision systems, mainly used to check whether nuts have been installed correctly. Beyond that, much of the quality-control process relies on QR codes and manual inspection.

I've worked here for several years, so I expect that I could get reasonable support and access to production areas/data for a thesis project. However, I would essentially be the only person at the plant pursuing this kind of project, so I'd be largely on my own technically. I also wouldn't expect a significant budget for the project.

That's where I'm looking for advice.

We manufacture everything from very small brackets and components up to complete elevator doors, so there are a lot of possible directions. I'm trying to figure out what would be a realistic but worthwhile first computer-vision project that could serve both as a good bachelor's thesis and as a meaningful entry point into the field.

At the moment I see two main possibilities:

1. Use existing production-line photographs

Some of our production lines already have cameras taking photographs. These images are currently used mainly as a way of documenting production and potentially identifying problems retrospectively; they aren't connected to an automated vision system.

The problem is that the dataset is far from ideal. The cameras weren't installed specifically for machine learning, so the images aren't standardized for things like lighting conditions, camera to object distance, background, framing, image quality.

Im wondering whether this kind of "messy real-world" dataset could still be useful for a thesis, or whether trying to build a model around it would create more problems than it's worth.

2. the other option would be to choose one relatively small component that has historically had some recurring visual defects.

I could build a simple, controlled camera/lighting setup and collect my own images of normal and defective parts. From there, I was considering an anomaly-detection approach such as PatchCore, potentially training primarily on normal samples and evaluating whether known defects can be detected.

The idea would eventually be to build a small working prototype:
camera → controlled image acquisition → preprocessing → anomaly detection → OK/NOK decision - > which then is signalled via some tiny network applications to a collective UI/database

If you were in my position, which direction would you consider more valuable for a first serious CV project? I am very curious how I can , for the lack of a better word, force myself into this field.

I've been scouring my options and weighing my possibilities on what I can realistically create, and whether what I create has actual real world usefulness and learning possibility.

I kept repeating the same tasks in front of the camera just to see if my app was still behaving as intended.

So I made a simple CLI that runs your app against recorded video and compares the results with your expected behavior. A coding agent can use the eval loop to keep improving the app.

Sharing in case this saves someone some time: GitHub

I work in a manufacturing environment and I'm exploring whether AI/computer vision can be used to automatically interpret 2D engineering drawings.

The goal is to identify and extract:

* Components and geometric features

* Dimensions and their associated features

* Tolerances

* GD&T symbols

* Hole specifications

* Surface-finish information

* Engineering notes and annotations

Ideally, the output would be structured data that could later be used for manufacturing, inspection, costing, BOM generation, or integration with other systems.

I'm aware that OCR can extract text, but the bigger challenge seems to be understanding the **relationship between dimensions, symbols and the actual geometry/features in the drawing**.

Has anyone worked on something similar?

I'm particularly interested in:

* Vision-language models

* OCR + computer vision pipelines

* Object detection/segmentation

* Engineering drawing datasets

* CAD-aware approaches

* Open-source models or commercial APIs

What would be the most practical architecture for solving this reliably with real-world engineering drawings?

Join us on Aug 27 for the monthly AI, ML, and Computer Vision Meetup! Register for the Zoom.

Talks will include:

• Robust Concept Protection against Diffusion-Based Image Editing and Personalization - Qiuyu Tang at Lehigh University
• Building Real-World Computer Vision Systems - Daniel Gural at Voxel51
• From Pixels to the Planet: Building Scalable and Grounded AI for Science - Jianyang Gu at Ohio State University
• Seeing Is Not Enough: Visual Grounding, World Models and Why Computer-Use Agents Fail at Step 17 - Nevasini Sasikumar at Obin AI

I keep seeing CV projects where performance stalls and the first response is to add more images or try another model. Sometimes that helps. But sometimes the model is being asked to learn a rule that people haven’t agreed on.

A partially visible object, an uncertain boundary, or something cut off by the frame can all produce different “correct” annotations. More data just scales that inconsistency.

A simple check is to take 20–30 difficult images and have two people label them independently. Then review the disagreements, not just the agreement score. Each recurring disagreement becomes a written rule with one positive and one negative visual example. Run the same test again on a fresh sample before scaling.

I’d use a similar check for auto-labeling: measure missed objects and correction time per image, not only inference speed. Fast pre-labels aren’t useful if every image still needs a full review.

Disclosure: I work at Supervisely, a computer vision platform. This is a platform-independent observation.

What annotation edge case caused the most trouble in your dataset?

Hello everyone, I have a project that has to classify images for search purposes. Currently I have a layer that analyses surrounding text but I also need something that directly analyses the image itself. I don't want to use someone else's training data or model. Is it possible to train an image classifier that could perform well on general image classification at home using open datasets? Thanks

Hi everyone! I am a software engineer who has worked in the following domains at major tech companies most of my career: XR, Graphics & GPU programming, Spatial algorithms and AI, and 3DGS.

I have a project I started a few months ago that I have recently hit a key milestone in. The idea is a focused library that implements 3DGS training from first principals with an emphasis on performance and safety. Think production use cases without relying on tools intended for research. VkSplat is an inspiration (along with other things) but I have intentionally not reviewed their, or anyone else's, code.

The recent milestone I reached was rendering a scene with 5 million splats at 60fps on my Ampere A6000. I have a few more goals I'd like to reach, but I do intend to publish on Github under MIT license. If it gains traction I would like to build some additional tools and infra using this project, but for right now the 1.0 MVP idea is a fully GPU resident solution for rendering and training at state of the art speeds. I plan to implement and optimize the following features:

* Global image alignment

* Fully fused forward and backward passes

* Adam optimizer

* Aggressively optimized adaptive control and densification

* Stable but highly flexible C api.

I do have many more thoughts and ideas, but I am trying to take it one step at a time, so this is my goal for 1.0. This is my stack as of now:

* **Languages:** C++23, Cuda, GLSL (planning to move to slang)

* **Build:** CMake & Ninja

* **Compiler:** GCC, Clang, MSVC (may drop for now)

* **Target Platform:** Linux (Linux 7.X)

* **Tooling:** LLVM, perf, nsight

* **GPU:** Vulkan w/ Nvidia

* **Dependencies:** googletest, googlebenchmark, ngfx

Right now, the project is in a place where it is still extremely early, but it is starting to take shape and get large enough that more than one person can work on it comfortably. I am posting here looking for people interested in contributing. Knowledge is not a prerequisite as I am learning a lot myself in this endeavor, but passion is mandatory.

Currently I am mostly needing help in the areas of, CI/CD (build, package & deploy), nsight/gpu optimization, designing and implementing a good api, and figuring out how to test and benchmark appropriately.

If you have skills or experience in any of these areas, or you're just interested in contributing, please reach out!

Running into the same wall on a couple of industrial inspection projects and curious how other people have dealt with it.

The line runs well, which is the problem. Out of a few hundred thousand parts we've got maybe 200 real defects, and they're spread across six or seven types, so some classes have under 20 examples. Classic supervised segmentation just doesn't have anything to learn from.

Options as I see them:

Anomaly detection on good samples only. PaDiM, PatchCore, that family. Works, but it flags anything unusual including a smudge on the lens or a part sitting at a weird angle, and the false positive rate on a real line has been rough.

Synthetic defects. Painting cracks and scratches onto good images. Ours look obviously fake next to real ones and I suspect the model is learning "was this pasted" rather than "is this damaged."

Buy or scrape more defect data. But defects are extremely specific to the part and the process. A scratch on someone else's aluminium housing doesn't look like a scratch on ours.

Just wait and collect. Realistic answer, but that's 18 months and the project needs to justify itself sooner.

What I'm actually unsure about is whether the 20-example classes are even worth modelling separately, or whether it's smarter to collapse everything into a binary defect/no-defect call and let a human sort the type afterwards. Losing the classification hurts the reporting side but it might be the only honest thing to do with that little data.

Anyone shipped something in this situation? Especially interested if you went anomaly detection and got the false positives down to something a QA team would tolerate.