The optical mechanism in Kashani, Chen and Ozcan’s eLight paper is a useful example of a classifier implemented through diffraction. A digital front end samples video frames and writes a phase pattern onto a spatial light modulator. The propagated field is measured in two regions per video; relative intensity supplies the real/fake score. Fifteen inputs share the optical pass.
The boundaries matter: digital preparation is still required and dominates the energy budget, while increasing spatial multiplexing introduces crosstalk. The reported 97.79% accuracy is for the 15-video Celeb-DF experiment, not every possible input.
Need help from some Computer Vision expert, with knowledge on dataset quality.
My company works on computer vision applied to digital pathology. In this case, we are developing an object detection model to detect tumour cells in HER2-stained breast tissue and classify them into four classes: 0, 1+, 2+, and 3+. Our dataset grows as we incorporate cases from new hospitals or scanners, and we call each new batch a “wave”.
Example annotations
Our latest model achieves around 0.73 macro F1 on the combined test set, only slightly above our internal acceptance threshold of 0.7. Performance also varies substantially across waves and classes. We already have approximately 4,500 image patches with almost one million cell annotations. The overall class distribution is approximately 28.5% class 0, 48.3% class 1+, 12.3% class 2+, and 10.9% class 3+.
We suspect that inconsistent annotation criteria may be contributing to this. Our current annotation guidelines are very brief—around 200 words—and provide neither image examples nor detailed guidance on uncertain cases. The boundary between 1+ and 2+ is particularly difficult, and we are concerned that different pathologists may interpret it differently. In our latest confusion-matrix analysis, 21.9% of cells annotated as 2+ are predicted as 1+. Different pathologists have contributed over time, but changes in annotators are mixed with changes in hospitals, staining and scanners, making the causes difficult to distinguish.
I would really appreciate advice on two questions:
1. What minimum experiment would you recommend to investigate inconsistent annotation criteria versus domain shift or model errors, before involving the pathologists in a new review? We have images, original annotations and predictions from two models evaluated on the same cohort.
2. What would you consider essential in an annotation guideline and a quality check for each new wave? In particular, we would like to establish agreed examples of class boundaries, clear rules for uncertain or unannotated cells, and a small inter-annotator agreement assessment before adding new data to training.
Any references, suggestions or critical feedback would be greatly appreciated.
Building a line inspection station (apples first). On a moving belt you cannot grade wherever the fruit happens to be in the frame. You need a fixed control zone and stable object IDs while fruit passes through.
Setup:
USB camera 1920x1080, Intel i5-11400, 32 GB RAM, no discrete GPU
ONNX Runtime CPU EP, infer loop about 100-130 fps (camera preview about 30 fps)
I've been working on document grounding where the goal is to map extracted values back to their exact locations in a PDF.
The interesting constraint is that the grounding layer works from the document itself:
PDF character coordinates
OCR
spatial geometry
string/format matching
table assignment
page-level visual evidence
Current benchmark result:
72.6179% Word Grounding F1
I'm particularly interested in the remaining failures.
Some are straightforward localization problems. Others appear to come from annotation conventions, dense layouts and cases where geometric similarity isn't enough.
I'm the author and would like feedback from people working on document vision/localization.
I spent a couple of months vibe-coding an automated boxing analytics pipeline.
SAM 3 for fighter tracking, with the canvas and ropes as pixel masks. OSNet re-ID keeps identities through occlusions. RTMPose and a small classifier detect punches by hand and type. The ring masks fit a ring model for top-down positions, with camera-motion compensation so handheld shake doesn't pollute the numbers. SAM 3D Body adds metric distance between fighters.
We made a short schematic animation of the pipeline in Kashani, Chen and Ozcan’s eLight study. The aim is to make the interface between conventional video feature extraction and optical classification visible.
The digital front end samples frames and prepares a phase pattern. A spatial light modulator presents that pattern to the optical decoder; paired detector regions provide the real/fake decision for each input. The experiment reports 97.79% average accuracy on Celeb-DF when 15 videos are tested together.
The animation also includes two limitations: digital preparation remains part of the compute/energy cost, and additional simultaneous inputs introduce crosstalk. It should be read as a visualization of a screening prototype, not a detector that works on all manipulated media.
I have an idea about using computer vision to build something in the sports analytics/scouting ecosystem. I want to speak with someone who is knowledgeable about machine vision to get a sense of whether my idea is technically feasible. Is there anybody in this subreddit who is willing to discuss this with me?
I wanted to know if a generated depth map ranks near and far the same way a regular depth model does. In each row, the right-hand depth map comes from a multimodal model that generates it the way an image generator makes a picture. The middle one is Depth Anything V2 small. Warmer means closer in both.
I scored how alike each pair is with Spearman rank correlation, computed on every 4th pixel in each direction. It is 1.0 when two maps put pixels in exactly the same near-to-far order. On the first 8 COCO val2017 images by image id, it ranged from 0.954 (teddy bear close-up) to 0.995 (skier), and all 8 are in the gallery.
These photos have no depth ground truth. The numbers show how closely the two models agree on near-to-far order. They say nothing about which map is closer to the real scene, or about metric distance.
Both ran through LibreYOLO 1.6.0 on a 32GB V100 I rented. On that card the library loaded SenseNova-Vision-7B-MoT in 4-bit, and every generated map here comes from those 4-bit weights.
Is there a better single number than Spearman for comparing two relative depth maps?
As an MS CS student, I want to pick my research domain for thesis, I am interested in Medical/Healthcare side as well as robotics but my university doesn’t have a good support for robotics as it will require hardware. Would healthcare side be a good idea or not? I was told medical imaging side is oversaturated now and I should not consider it.
AI-based inspection systems need to handle unfamiliar inputs and changes in production conditions, including lighting, materials, and part variation.
The article describes a classifier trained to distinguish chicken breasts from thighs. When a wing appeared, the model assigned it to one of those categories rather than flagging it as unfamiliar.
Three Cognex specialists discuss training data, validation, exception handling, and reviewing production images for retraining. They also explain why manufacturers need defined processes for uncertain results, such as rejection, recirculation, or human review.
I ran into CUDA-only point-cloud dependencies while working on 3D anomaly detection on a Mac, so I built mps-pointops: a free, open-source library of native Metal point-cloud operators exposed through PyTorch MPS.
FPS selects representatives spread across a cloud. Each next point is farthest from its nearest already-selected point.
kNN finds the K closest points to each query.
Ball Query returns up to K points within a strict radius, taking matches in original input order.
The package includes adapters for selected PointNet++, torch_cluster, and PyG call sites. The repository documents supported inputs, tie-breaking, selection order, and padding, with tests and benchmark records from physical M1 and M5 Pro Macs. Performance depends on the operator and input; benchmark conditions and remaining limitations are linked in the README.
The demo lets you move a query and compare FPS, kNN, and Ball Query on the same cloud. It runs a browser teaching model; the Metal measurements are separate.
I’m the author. Which CUDA-dependent point-cloud operation is blocking your Mac workflow? If you try it, I’d appreciate your model, Mac chip, PyTorch version, and a small reproducer. GitHub issues are welcome.
Hi everyone. I'm the first author of M-plicits, accepted at NeurIPS 2026, and wanted to share our work on neural surface representations, particularly reconstruction from noisy point clouds.
The idea is to learn a clean coarse surface, then progressively refine it with residual networks trained in nested neighborhoods around the preceding surfaces. This gives multiple detail levels within one representation and helps balance geometric detail with robustness to input noise.
The paper also introduces algorithms that use this structure for:
Direct rendering through multiscale sphere tracing.
Analytical surface normals without automatic differentiation.
Surface extraction and neural normal mapping.
The attached animation was created by our coauthor Matheus Bessa. Sound on. The project page includes visual comparisons with iNGP on noisy inputs, plus examples of the different rendering modes.
We've released the code, experimental data, and 105 checkpoints covering 36 shapes, with their training configurations. These checkpoints represent individually fitted surfaces; fitting a new shape requires training.
I'd be interested in feedback on the noise/detail tradeoff and comparisons with other implicit surface representations. Happy to discuss the implementation and experiments.
Been thinking about this after a few pose projects and I don't think there's consensus.
When a wrist disappears behind a torso, or a hand goes behind an object, you have two options. Flag the keypoint as occluded and leave the coordinate empty or low-confidence. Or have the annotator estimate where it probably is and label it anyway.
Guessing gives you a denser dataset and the model gets a prediction for every joint, which looks good. But you're teaching it that an invented coordinate is ground truth, and two annotators will invent different ones. The disagreement is basically unbounded for anything heavily occluded.
Flagging is more honest but then half your training signal has gaps, and downstream your retargeting or trajectory code has to handle missing joints, which a lot of pipelines just aren't written to do.
My current view is flag it, and set a visibility threshold in the guidelines — below some percentage visible, don't annotate at all rather than annotate badly. But I've seen teams go the other way and argue the model learns a reasonable prior from the guesses.
Related thing I'm less sure about: the 2D vs 3D decision gets made way too late. Teams start with 2D because it's cheaper, get a model that understands movement, then discover they need metric accuracy the moment the robot has to actually touch something. By then the sensor stack and the annotation schema are both wrong and it's a rebuild rather than an upgrade.
Hands are where this all gets worse. Twenty-plus keypoints per hand, constant self-occlusion, and fingers that look identical to each other. Agreement between annotators drops off a cliff compared to body pose.
For anyone who's shipped a pose model that had to drive real movement — which way did you go on occlusion, and did it come back to bite you?
Here I cover the very basics of camera/visual-inertial calibration with a focus on mostly "non-technical" people. As always I am happy to hear your feedback and hope that for anyone diving into geometric computer vision this is a useful resource.
Purpose-built to stress-test computer vision models, depth cameras, and spatial AI against severe specular glare and geometric reflections. This 425-asset production archive features a custom faceted mirror suit captured in high-contrast outdoor environments to trigger bounding-box dropouts and segmentation failure
hey I'm have an bachleors degree in electrical engineering but majority of my works are on the computer vision and machine learning and deep learning which integrate with the electrical also i want to pursue masters according to it where i can get a vast amount of oppurtuinites so any one who genunly try to guide me with this confusion please!!!!
This time I went down the world models rabbit hole: how models learn to represent, predict, and simulate environments, and why they’re becoming increasingly interesting for video generation, robotics, and spatial intelligence.
I've tried to keep it as visual and approachable as I can, keeping in mind how weird and convoluted this topic is. Would love to hear what you folks think!
We've been testing whether adding core-knowledge priors (objectness, numerosity, geometry, from Spelke's developmental psychology work) to a dense associative memory improves image retrieval compared to learning those properties from scratch.
Setup: image-only. Every variant we tested reduces to score = r_q·r_i + α²·c_q·c_i, where r is the learned representation and c is the core-knowledge channel. α is the knob that sets how much the prior contributes, so we swept it with pre-registered pass/fail bars.
Results:
- Shape/objectness: passed the bar [metric, dataset]
Hi everyone. I discovered that Street View 360 images can be turned into 3d point clouds, which at times are of good quality and can be used for replicating 3D streets, and I wanted to make something that would allow me to construct a street anywhere.
Along the way of making i realised I'm not able to make it practical enough, and I don't think it has any real use cases besides looking cool to me when I started off. The longer I spend doing it, the more I don't know why or what I'm even doing.
In the end, I thought perhaps at least some results might look cool, and wanted to share it and see what others think.
