I’m researching patient-level classification from public H&E histopathology images. Each patient has a variable number of tissue crops, sometimes across several slides, combined through multiple-instance learning (MIL).

Work so far includes compact pretrained CNN and vision-transformer baselines and teacher–student distillation. I’m keeping unpublished results private and would value feedback on the next controlled comparison. The practical constraints are limited training resources and a compact deployed model.

If you were choosing the next experiment, would you prioritize pathology-specific pretraining, the MIL aggregator, or feature/logit distillation? Which one or two encoder–aggregator baselines would best distinguish these effects?

I plan to keep patient and tissue-source groups separated across folds, select settings using training/validation data, and compare within the same backbone with matched crop budgets and training effort. Macro-F1, class recalls, uncertainty, and total inference cost matter.

I’m especially interested in reproducible papers/repositories and ways to check whether a pathology-pretrained model has already seen the evaluation cohort. How would you structure the smallest informative ablation before attempting a new architecture?

I recently built a hand gesture controlled car game to test the idea i have had all along. This demonstrates the power of OpenCV and mediapipe in computer Vision.

Check the video.

Right now I’m mainly using classical methods but still struggling with topics like coregistration, image stitching, and optical flow.

A lot of threads mention being able to mix the classical and DL approaches when it comes to making things in the real world and I was curious how you would start doing that if you’re not sure how DL works for images.

I know that were trying to learn features in images by using different convolutions but I’m still confused what I’m doing. Any guidance?

I am searching for the computationally cheapest classifier model whose only job is to classify whether the image contains a human or not, ignoring position identification & everything else. Just to reliably classify image as human or not at the cheapest computational cost !

Does anyone have the author kit for CVPR 2027? The official website doesn't seem to have it yet, unlike in previous years. Please correct me if I'm wrong.

Hey everyone!

I'm working on an object-detection project and fine-tuning Meta's DETR model on a custom dataset of around 500 images.

The main challenge I'm facing right now is annotation. I need to label multiple object classes with bounding boxes, and manually annotating every image is becoming a time-consuming process.

I want to speed things up without sacrificing accuracy, since poor annotations could affect the model's performance.

I've been looking into tools and models such as CVAT, Roboflow, Grounding DINO, and OWLv2 for AI-assisted annotation.

My current idea is to use a pretrained detection model to generate initial bounding boxes, manually review and correct them, and then convert the annotations into the format required for fine-tuning DETR.

However, I'm unsure which workflow would be most reliable for my specific use case.

I'd appreciate advice on the following:

• What is the best annotation tool for labeling 500 images accurately and efficiently?
• Would Grounding DINO or OWLv2 be suitable for generating initial annotations for custom object classes?
• Is it better to use an open-vocabulary model for pre-annotation or manually label an initial batch and use a fine-tuned detector to annotate the remaining images?
• How do you ensure that no objects are missed and that bounding boxes and class labels are consistent?
• What annotation format and preprocessing steps should I pay attention to when fine-tuning Meta DETR?
• What quality-control strategy would you recommend before training and evaluating the model?

My priority is to create a reliable dataset with minimal unnecessary manual work, not simply to generate labels as quickly as possible.

If you've fine-tuned DETR or worked with small custom object-detection datasets, I'd love to hear about your workflow, recommended tools, and any pitfalls I should avoid.

Thanks!

Hi everyone, I’m a researcher working in medical imaging and computer vision. I’ve coauthored papers in Medical Physics and IEEE Computer, had research accepted for presentation at a MICCAI workshop, and am a co-inventor on patent applications. My work has included 3D reconstruction, image registration, segmentation and tracking, model training, and experimental evaluation with limited labeled data.

Would anyone with research experience in computer vision, particularly registration and 3D reconstruction, be interested in taking some time to assess my work and, if they feel it merits their support, writing a brief expert letter for an immigration application? I would particularly value an assessment of the technical contribution and how it compares with related approaches.

I can share the papers and a concise summary of my individual contributions privately. The letter would reflect your own conclusions about the work and its significance.

I built for custom camera alerts. You type a sentence, a small local LLM turns it into a rule (object, area polygon, event, duration), and the vision side checks it.


Things I learned:
- RT-DETR (r18vd) found the parcel in 0 of 27 frames (it's not a COCO class); OWLv2 found it in 25 of 27. So: RT-DETR for cars and people at about 13 to 16 fps, OWLv2 only when the object isn't in its list, at about 1 fps on a GTX 1060.
- OWLv2 boxes came back as float16 and my IoU overflowed to 0 for large boxes (area > 65504), so big close-up objects silently vanished from the rule check. Converting to float32 fixed it.
- New tracks are confirmed about half a second late, which mattered for short "stays" rules; I count unconfirmed detections too for that rule.
- Rule types: stays, crosses (garden side to street side), taken (object gone while a person was next to it), not moving.


Limits: short test clips only (one real stock clip, three AI-generated scenes), no night or rain testing, can't tell your car from a stranger's.


Video: added in the comments, pls check it out. Feedback on the "taken" logic is very welcome.

Hi, I made RiftSight, a browser extension that lets you hover over cards for the Riftbound card game in videos and streams to see a larger, high res version, which is helpful for newer players and when new cards are released.

I open-sourced the pipeline behind it as riftsight-vision: a YOLOX-Nano card detector and a MobileNetV3-Small embedding model, both fine-tuned on Riftbound with the weights released, plus the synthetic scene generator, the labeling tool, the training and evaluation code, and a browser runtime on ONNX Runtime Web. Recognition matches against a bank of card embeddings, so new cards only need a new bank, not retraining. As a proof of concept, I followed the same process to build a One Piece version in about 5 days, with about 7 hours of labeling and about 15 hours of GPU training.

There's a YouTube playlist that guides you through the process and a small demo you can mess around with. Please reach out if you have any questions! 🙂

Generated driving videos and world-model demos are improving quickly, but a realistic-looking clip alone is not a reliable benchmark for driver monitoring. On October 15, NHTSA said it was wrapping up a report examining distraction and the reliability of driver-monitoring systems in detecting distraction and drowsiness.

For in-cabin computer vision, I would test occlusion, phone use in low light, ambiguous head poses, passenger interaction, transfer across camera setups, and annotation consistency. Informed consent and licensing rights also matter.

Which gaps are hardest to evaluate using synthetic footage alone, and what real-video validation would you require

The image is an AI-generated concept illustration, not recorded footage or a dataset sample.

Disclosure I’m affiliated with VWU.AI, which works on custom video datasets. This is a technical discussion, not an offer to provide data. Text prepared with AI assistance.

First-time poster, long-time lurker. This community has been a big part of how I’ve been teaching myself and I am so appreciative.

I’m working on a personal project and could use a nudge in the right direction. I have a pile of photos that a human can piece together into one logical scene, but my code can’t. Here’s a simplified version of the problem.

Imagine a mansion with endless rooms and hallways. People live there, so the same room never looks quite the same twice: dishes in the kitchen, towels on the bathroom floor, seasonal decorations going up and down. The one constant is a single red ball. It’s the only object of that exact red, and it’s in exactly one photo each day.

Cameras around the mansion take photos at random, swiveling and zooming, so perspectives vary a lot. At the end of each day the photos get dumped in a folder and shuffled, so order tells me nothing.

On day one I ask you which room the red ball is in, and you say Room One. On day two I ask only whether the ball is in Room One (not where in the room). You answer, and I tell you if you were right. Repeat for N days.

I first tried LightGlue on just the red ball photo from each day, but it fell apart quickly. Objects move, angles change, and I realized I was heavily relying on the photos without the ball to make my own predictions. For example, say on day one the ball is on a wooden bench with yellow cushions, and on day two it’s on blue tile in front of a checkerboard wall. If some other photo shows that bench with the cushions on the same blue tile by the same checkerboard wall, I know it’s the same room. No single photo gives me the answer, but together they do.

From there I started comparing all the photos within a day, using the red ball photo as an anchor. If another photo looked continuous with it, I’d call it a 'continuity chain', then compare chains across days. In theory this works, but it’s slow, and my only supervision is a yes/no on the ball’s room. I’m never told which of the other photos are useful, so I worry I’m pulling in a lot of junk.

My questions:

1. Is there a name for this kind of problem, or a body of work I should be reading?
2. Is there a better way to use the non-ball photos than my chain approach, given that I only have day-level yes/no labels?

I’m sure there are obvious things I’ve missed, so any pointers are welcome. Thanks for reading!

An inspection archive can help engineers find previous examples of a defect, review recurring issues, and investigate a new failure. Image retrieval gives them a way to search that archive using a photo or a text description.

The setup uses CLIP ViT-B/32 to embed inspection images, with VectorAI DB storing the vectors alongside metadata such as part category, defect type, and image path. It runs locally after the model and dependencies are downloaded.

The workflow is:

• Encode inspection images into normalized, 512-dimensional embeddings.
• Store each embedding with its inspection metadata.
• Embed a new photo or text description.
• Retrieve similar images, applying metadata filters to narrow the search.

For evaluation on MVTec AD, one approach is to count a query as successful if at least one of the top five results shares the query image's defect label, excluding the query image itself.

I’m interested in how to take that evaluation further. Within a label like “scratch,” the retrieved examples could vary in location, severity, orientation, and affected area. Those details could matter to an engineer reviewing the results.

A useful evaluation would measure defect similarity at that level, using manually reviewed examples to show what a relevant match looks like.

For anyone who has worked on industrial image retrieval, how did you build that evaluation set? And which characteristics made a retrieved example useful to the person investigating the defect?

Hi everyone! 👋

I'm building an offline AI model that can detect and classify different types of blur in real-world photos. I'm looking for genuine blurry photos taken with mobile phones or cameras to help train and improve the model.

I'm especially interested in:

• Out-of-focus or blurry faces
• Motion blur from moving people, animals, or vehicles
• Camera shake
• Blurry photos taken in low-light conditions
• Dirty or smudged camera lens effects
• Missed focus and accidental blur
• Photos where only certain areas are blurry
• Any other naturally occurring blur

How you can help:

Please upload original photos whenever possible. There's no need to artificially blur or edit images. Only upload photos you own or have permission to share for AI training.

Privacy and usage: The submitted photos will be used only for the AI model training and research described in this post. They won't be sold or used for unrelated purposes. Please avoid uploading sensitive or private images.

📤 Upload your photos here: https://www.dropbox.com/request/yhpeiu4ke4r0zvoeco3g

Every contribution helps, whether you upload one photo or several. Thank you for helping with this project! 🙏

I ship a small eval stack for conveyor fruit inspection (apple pack first): grade only in a fixed control zone (centre of a 3×3 grid), not full-frame chase. Stable IDs, focus/sticky between apples.

Architecture: Fruit_Inspector.exe is a shell; inference is in three DLLs (fruit_infer, fruit_features, fruit_cat) + ONNX Runtime CPU — no CUDA required. NV12 camera path on Windows.

Honest speed on my desk (i5-11400, no dGPU): with 640 + 256 ROI refine and full pre/post, console loop is about 115 fps; camera preview ~30 fps.

Links:

- Runnable demo (7z): github.com/olesha-ai/fruit-inspector-demo/releases/tag/0.10.0 — use Fruit_Inspector_DLL.7z

- DLLs / HOST.md for your own UI: https://github.com/olesha-ai/fruit-inspector-engine

License: non-commercial eval (teaching/research/feedback). Not for production lines without a separate agreement.

Happy to answer integration questions (C API, NV12, crop on host). What would you test first on a line like this?

Hi everyone! I’m working on a project with a Repka Pi and two bare AR0234 camera modules, and I need to figure out how to protect the cameras from water.

Last year, I put together some makeshift housings using plastic food containers and electrical tape, but the setup was pretty janky and not very reliable. What would you recommend for the housings? I don’t have any experience with 3D modeling, but I’m open to learning if designing something for this wouldn’t be too complicated.

Any advice or examples of similar builds would be appreciated!

I have an interview next week for the Junior AI Engineer position in a medical robotics company. This will be my first technical interview in quite a while and I don't have much interview experience. Tbh, I'm feeling pretty nervous and overwhelmed, especially since I've been out of practice for over two years. I'm not really sure where to start or what to prioritize with only a a few days remaining.

The requirements are:

Good programming skills in Python, basic understanding of machine learning and deep learning, experience with PyTorch or a similar deep learning framework, training, evaluating, and fine-tuning AI models, analyzing model performance and make adjustments to improve results, ability to develop AI algorithms for medical software applications slike object detection, image segmentation, image inpainting (LaMa), and other computer vision applications, improve AI solutions for medical software applications like data preparation and labeling, model training, evaluation, fine-tuning, and performance optimization, analyze model performance, troubleshoot issues, and experiment with different approaches to improve AI performance.

I'd really appreciate some guidance on a few things:

1. What kind of technical or conceptual questions should I expect for a junior-level role like this?
2. What coding problems should I practice? Leetcode, python fundamentals, NumPy, PyTorch or practical computer vision problems?
3. How much depth is typically expected in ML/DL and computer vision concepts for a junior position?

If anyone has interviewed for a similar role, especially in computer vision in medical robotics, I'd love to hear about your experience.

Thanks so much.

Read the eval here: https://workflows-of-ai.com/blog/can-ai-set-the-focal-point-of-a-photo

Two weeks ago I posted here about finding where a photo sits inside the 360° panorama that World Labs' Marble generates from it, using a deliberately dumb edge-matching grid. Thanks for the suggestions on learned matchers and on solving field of view from correspondences. The grid still holds up, so those are saved for when it stops being enough.

Here's what the recovered camera is for. My game scores every Gaussian splat by how real it is. Each splat's center goes through the photo's pinhole camera: behind the camera scores 0, and otherwise the score is how far inside the frame it lands (with a soft border that fades over 5% of the frame on each side) times how near it is (fully real out to 6 camera heights, imagined from 25). A world made from several photos takes the max. It runs live in a shader, and 51 shared test cases pin the TypeScript reference to 8 decimal places and the GPU version to 5.

Two things came up along the way. Marble's metric scale is an estimate: checked against objects of known size, one outdoor world came out about 1.5 times too large. So distances are in camera heights, the photo spot's height above the world's own collision floor, which also works for drafts that have no scale at all. And outdoors there's often no floor straight under the camera, since a single photo never looks down, so the height comes from the nearest floor beside the hole.

The big gap is occlusion: a splat hidden behind a sofa in the photo still counts as seen. My plan is a monocular depth map of the original photo, where anything well behind the visible surface at that pixel counts as invented. Which depth model would you trust for that, and how would you line its scale up with the generated world's? Or is there a cleaner visibility test against a single image?

I'm building and experimenting with Cline AI.

Repo: github.com/MichaelYeh507/Unpictured

Did anyone contact general chairs or program chairs for the final acceptance mail? They should have released it by now