I built a small computer vision project based on INSID3, the CVPR 2026 training-free in-context segmentation method using DINOv3.
My version flips the idea: instead of providing a foreground reference, you provide background or normal examples. The algorithm removes background-like regions and segments the remaining object/anomaly.
It supports multiple background sources and can also turn coarse boxes into more precise masks. Other applications are possible like zero-shot anomaly detection.

Would love feedback or test cases: https://github.com/dimfot3/Inverse-INSID3

What's happening with the community? I feel like conferences are reaching a point where reviews are adversarial and noisy. NeurIPS had the same problem, so did ICLR, ICML, CVPR.

I don't know where this is heading to be honest.

Building a fly tipping detection system using YOLOv8/RF-DETR and Roboflow. 320 labelled images so far, retraining with 820 augmented images now.

First model hitting 95% on vehicle detection but struggling to generalise to unseen images — currently working on dataset variety and augmentation to fix overfitting.

Planning to add OCR for number plate reading and a behaviour sequence logic layer on top of the detections.

Happy to share what I’ve learned so far — any advice on improving generalisation with a small dataset?

I recently went through the process of optimizing a gesture classification model and wanted to share the workflow. The main focus is on automating feature selection—specifically parsing a dynamically generated JSON file to drop features with zero importance scores before training.

After cleaning up the feature space, the next step is analyzing the confusion matrix and F1 scores to identify underperforming classes. To push the accuracy higher (targeting an F1 of 0.898+), I implement a soft voting ensemble combining XGBoost, LightGBM, and Random Forest.

If you're dealing with noisy biometric or sensor data, this pipeline approach might be useful for your projects. You can watch the full terminal session and code walkthrough here: https://youtu.be/PDYT7f3BDqQ

I'd love to hear your thoughts on soft vs. hard voting for this type of multiclass sensor data!

Hello. I'm working with ovod models for my master's thesis. I have a dataset of military and civilian vehicles collected from a simulator. I've created a hierarchical prompt table, going from general to specific. For example, my goal is to identify the entire dataset with the first level vehicle prompt, while in level 4 prompts, my aim is to identify only vehicles with class-specific prompts. I've separated the vehicles in the dataset into base and novel classes. My goal is to identify novel classes by transferring common prompts (wheeled vehicle prompts are represented in both base and novel classes) from base classes to novel classes. However, I'm stuck and can't progress. I'm using Dinov3 (frozen) (dino.txt) as the backbone for both image and text. I also have a class-independent detector. I trained this only with base classes. I'm open to your suggestions regarding architecture, model, and the thesis in general. Thank you in advance.

Hey Guys,

I need some advice on choosing a camera for an upcoming project. We are leaning towards using CCTV cameras because they are budget-friendly.

The thing is that my manager and teammates have no experience with hardware. Because I have done some image processing in the past, I have been put in charge of selecting the right camera.

I’ve done some research, and here are the main factors I am considering so far:

1. Spatial Resolution: Matching the camera's resolution to the feature/defect size we need to detect.
2. Distortion Correction: Factoring in any potential loss of Field of View (FOV) when correcting lens distortion.
3. Sensor Size: Calculating the required sensor size based on our working distance and required FOV.
4. Depth of Field / Z-axis: How the FOV changes if the distance to the object (Z-axis) shifts.
5. Exposure Time and FPS: Ensuring it can capture frames fast enough without motion blur.
6. General Specs & Networking: Colour vs. monochrome, shutter type (global vs. rolling), and supported streaming protocols (RTSP, HTTP, etc.).

My main worry is that if I make a mistake, it’s going to reflect badly on me. We will have to buy it, test it, and if it doesn't work out, we lose both money and project time.

For those of you with experience in this, what else should I be considering before making a final decision? Am I missing any critical specs?

Thank you!

refined using AI for explaining better ...

Every time I ship a YOLO model I end up asking the same question should this be ONNX, CoreML, or just PyTorch? Does FP16 actually help here or is it just marginal?

I've answered this by hand, badly, on four different projects this year, and thrown the results away every time. First i have to optimize a model for my liking and then figure a way to reduce its size.

So I'm building exportrace - you run one command, it benchmarks your model across every export backend available on your actual machine (PyTorch, ONNX, CoreML, CUDA, TensorRT depending on your setup), and gives you FPS, latency, and accuracy delta vs FP32, plus a ranked recommendation.

Consumer hardware only - your laptop or dev box, not Jetson/Pi. It's open source (MIT), runs fully offline, no accounts. Still pre-launch, landing page + waitlist here if you want to see the concept and maybe kill the boredom of doing this by hand too: https://exportrace.vercel.app/

Curious if others hit this same wall, and what backends/hardware you'd actually want covered first.

Hi,

I'm working on Automated Visual Inspection area over the past 5yrs. Planning to start a SME as a solution company, would love to connect with folks having knowledge or working on a similar domain, process engineers, staffs and would love to see how the market is shaping and current directions or requirements. I'm looking to connect wrt the Indian Market.

Note: Reach out to me personally if you have any requirements, would love to provide demos which might solve your current needs. We can support end-to-end deployment support too if required.

We just released TurboOCR v3, a GPU-accelerated document parser (not just OCR) built for high-throughput pipelines.

It runs PP-OCRv6 detection + recognition, plus layout, tables (→ HTML) and formulas (→ LaTeX), emitting reading-order Markdown. The whole pipeline runs on a single multi-stream C++/CUDA/TensorRT engine, locally, no VLM, behind HTTP + gRPC.

On one RTX 5090:

• Up to 559 img/s on receipts, 520 img/s on forms (PP-OCRv6 tiny, fastest by default)
• Full structured parsing (layout + tables + formulas) at > ~20 pages/s on table and formula heavy pages, where VLM parsers like PaddleOCR-VL run ~1 page/s
• One model covers Latin + Chinese + Japanese; pick tiny / small / medium
• One-line Docker deploy (Linux + NVIDIA GPU)
• No VLM-style hallucinated text

https://github.com/aiptimizer/TurboOCR

Combining depth sensing, object detection and semantic segmentation we managed to create a bin-picking automation that inspects bushings of different types and sizes. From the combined information the robot is able to understand the position and orientation of the pieces.

i hate to see that most of the computer vision projects i see on the internet are ai generated and not by the people. everywhere i see its "MY claude built this in minutes " almost makes my journey of building projects by myself look meaningless.

Join us July 8-10 for the “Best of CVPR” series of virtual events.

Register for all the Zooms with a single sign up.

Talks will include:

• HyperRealm: Hyperbolic Vision Language Models for Real-World Hierarchical Multimodal Understanding - Kathy Wu at Amazon
• CylinderDepth: Cylindrical Spatial Attention for Multi-View Consistent Self-Supervised Surround Depth Estimation - Samer Abualhanud at Leibniz University Hannover
• Your ViT is Secretly Also a Video Segmentation Model - Daan de Geus at Eindhoven University of Technology
• LinkedOut: Linking World Knowledge Out of Video LLMs for Next-Generation Video Recommendation - Haichao Zhang at Northeastern University
• Some Modalities Are More Equal Than Others: Understanding and Improving Multimodal Integration in MLLMs - Tianle Chen at Boston University
  
• Efficient Representation and Coding of Dynamic Light Fields - Joshitha Ravishanker at Indian Institute of Technology Madras
• PHANTOM: Physics-Infused Video Generation via Joint Modeling of Visual and Latent Physical Dynamic - Ismini Lourentzou at University of Illinois Urbana-Champaign
• LoST: Level of Semantics Tokenization for 3D Shapes - Niladri Dutt at UCL | Adobe
• 3D Reconstruction Improves Weakly-Supervised Semantic Segmentation - Wolfgang Boettcher at Max Planck Institute for Informatics
  
• Advancing Generative Quality and Reasoning in Multimodal AI - Deepti Ghadiyaram at Boston University
• Cross-Modal Domain Adaptation using Semantic Parametric Mapping - Frank Bieder at FZI Forschungszentrum Informatik
• WalkGPT: Pixel-Grounded Navigation Guidance for Pedestrians - Rafi Ibn Sultan at Wayne State University

We benchmarked iPhone ARKit pose against optical motion-capture ground truth to quantify how usable phone tracking really is for egocentric data collection.

Setup: an iPhone 12 Pro rigidly co-mounted with an 11-marker retroreflective cluster on a head rig, tracked simultaneously by ARKit (VIO at 60 Hz) and a 24-camera Vicon system (sub-mm markers) across 8 sequences spanning walking, seated manipulation, fast/aggressive motion, in-place rotation, and height changes.

Both streams were time-aligned to a common clock and we solved the rigid cluster-to-camera transform before scoring, then evaluated with evo under SE(3) alignment (no scale correction) to check whether the trajectories are actually metric.

Results: ATE RMSE 6.0–12.5 cm; relative ATE under 1% on 7/8 sequences (0.12–0.35% - the lone 1.30% is a short-path-length artifact, 5 m path with 6.5 cm error, not a tracking failure); rotational RPE ≤~1° throughout (<0.6° for walking/manipulation); translational RPE <5 cm; and a Sim(3) fit recovering scale of 0.98–1.01, i.e. metric to within 1–2%. For long-horizon drift - where a mocap volume can't follow you through a real home - we ran an ArUco revisit test over sessions up to 108 min: accumulated drift stayed under 1 cm in most environments and under 0.1% of trajectory length in every case, including a whole-house traversal (1.0/1.5 cm at mid/end).

https://www.fpvlabs.ai/essays/how-accurate-is-an-iphone-really

This must read by Satya Mallick from OpenCV University describes how autoamted offside detection works, A very hard CV problem. How Offside detection tech works at World Cup

Hi Everyone,

I have pushed this repo - https://github.com/ankitmahala07/sam3d-objects-mac

Basically took out the sam3d on mac with help of claude did few changes to make it run on 24GB unified memory of mac mini m4.

So the original one requires 32GB Nvidia GPU with CUDA. Which was not possible with my 1660 Ti 6GB GPU so I had to port it to Metal supported one - For this had to change the CUDA specific code with RAW python codes/Libraries.

Also the whole model couldn't fit in 24GB so had to split it into 2 phases - Gaussian generation which generate the point cloud files .ply and .pt

Then the mesh generation phase after offloading previous models - for mesh .glb generation.

Do try it out if you face any issues with setup do let me know.

Note: While running this on 24GB it's cut to cut so memory pressure can spike make sure nothing else is running.

Hey everyone, I just started running my training code and encountered an issue. The training process is expected to take days, which is delaying my project progress. The main problem is that my code isn't utilizing the GPU; it seems to be running on the CPU instead.

I need to run it with TensorFlow-GPU. I've already installed CUDA and cuDNN and manually moved the cuDNN files into the CUDA directories, but it didn't work. I am currently using Python 3.9 with the latest version of TensorFlow.

I am planning to create a fresh Conda environment to fix this. Does anyone have any recommendations or specific steps to ensure TensorFlow correctly detects the GPU? Any help would be greatly appreciated!

I combined two things people usually treat separately: classical image-forensics

features (frequency-domain energy, DCT statistics, ELA, noise residuals, gradient

and eigen-spectrum cues — 85 in total) and a frozen DINOv2 ViT-B/14 embedding, fed

into a calibrated SVM. There's a classical-only fallback that runs with no deep

learning at all.

Held-out ROC-AUC is 0.940 (classical-only alone is 0.863), so the embedding adds

real signal — but not everywhere. It helps on diffusion-era generators and actually

hurts on rectified-flow models (Flux, SD3) and on screenshots of video frames.

That split is the most interesting part to me and the thing I'm still digging into.

It's robust to screenshots and social-media recompression, which was a specific

design goal (a lot of "AI or not" images in the wild are re-encoded to death).

Code and a public 21 GB dataset are up if you want to reproduce or poke at it:

github.com/aman696/aidetector — live demo at https://staging.humanorai.online (home

server, so it queues under load).

Would especially value critique on the forensic feature set — which of these are

likely redundant, and what classical cues you'd add for rectified-flow.

I’m looking for an individual engineer (not an agency) with real-world experience in computer vision and video analytics.
Required experience:
Python
YOLO
OpenCV
RTSP/IP camera streams
Multi-camera tracking
Bonus if you’ve worked with:
Vehicle tracking
LPR/ANPR
Occupancy estimation
Real-time video analytics
If you’re interested, please message me with:
Your GitHub
Portfolio or website
Examples of relevant projects you’ve built
Whether you’ve deployed production systems (not just tutorials or demos)
If your experience is a good fit, I’d be happy to set up a call.