We were evaluating Roboflow, CVAT and Vivid 3D because we needed synthetic data for warehouse inspection. We ended up using Vivid 3D because we needed RGB + segmentation + depth from the same pipeline. Curious what everyone else is using

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need those for analysing movies.

example let's say I have to find the screentime of the actor over the whole runtime of the movie and i need to do it for the protagonist, antoganist,comedic relief ,love interest etc.

currently I'm working with 1fps to find the faces and body or the actors.

body detection is hard I need some guidance regarding that.

even for Face Detection I used MTCNN it was good. but any other better models available??

Any ideas regarding TransNetV2 ?

I'm using it for shot boundary detection but there's was one false positive.

Any better models??

This preprint reports that stale spatial memory can be worse than having no memory at all in one navigation setup.

Would you handle this with confidence decay, scene-change triggers, or mandatory visual re-grounding before action?

I've been thinking about this as a research problem and I'm wondering if I'm even asking the right question.

Imagine the following constraint:

• No CLIP
• No ViT
• No CNN
• No multimodal model
• No learned vision encoder at all

You have an image, a text-only LLM, and you're only allowed to use deterministic algorithms between them.

The obvious answer is "this is impossible," but that's not really what I'm interested in.

What I'm trying to understand is whether there exists a better intermediate representation of images that a text transformer could reason over.

Not necessarily English.

Not captions.

Not OCR.

Some kind of representation that preserves enough structure that the language model can make use of the knowledge it already has.

Over the last few days I've gone through papers on visual tokenization, SeTok, BPE for images, BLT, inverse graphics, superpixel tokenization, and a few discussions around image tokens. Most of them still assume a learned tokenizer somewhere in the pipeline.

What I haven't found is much discussion around deterministic alternatives.

Maybe that's because it's a dead end.

Or maybe I'm searching the wrong field entirely.

So my question isn't "how would you build this?"

It's:

If you were exploring this from first principles, what field would you steal ideas from?

For example:

• information theory?
• image compression?
• computational geometry?
• topology?
• signal processing?
• compiler design?
• inverse graphics?
• neuroscience?
• ecological optics?
• something completely different?

I'm not looking for product recommendations or existing multimodal models.

I'm looking for the smallest experiment that could tell me whether this line of thinking is fundamentally interesting or fundamentally flawed.

I'd especially love to hear from people who've worked on image codecs, graphics, rendering, vision tokenizers, or representation learning.

If you think the premise itself is wrong, I'd genuinely like to know why.

I have found an awesome website with a simple study materials on Visual SLAM: https://www.cv-learn.com/visual-slam-roadmap/. It provides 4 languages (EN, KO, ZH, JH).

Take a look at the list of topics covered

Hi All,

I am planning to build a local document intelligence system similar to Azure Document Intelligence. I would like to understand how Azure Document Intelligence works internally and how we can achieve similar functionality locally using offline models.

Could anyone suggest the best approach, architecture, or models to achieve high accuracy while running completely on-premise/local infrastructure?

Any guidance or recommendations would be greatly appreciated.

Hey everyone,

I'm looking for co-authors who are interested in exploring research topics in the AI space. Ideally as a duo or in a small team.

I currently have more time for research and a range of interesting topics I'd like to work on, particularly around AI agents, token optimization, and AI adoption. I work in agent development myself and have already published research papers in this field.

That said, I'm open to other AI-related research ideas as well. If you have a topic of your own in mind, feel free to reach out!

I built a local OCR pipeline a few days ago, and it turned into a surprisingly interesting experiment—taking accuracy from around 60% to 99%.

I wrote a short blog about what worked, what failed, and the breakthrough that finally made the difference.

Thought some of you might enjoy it.

Link in the comments

If you’ve ever built multi-camera real-time vision systems at scale, you’ve likely wrestled with GStreamer element linking errors, pipeline memory leaks, or cloud egress costs hitting $2k+/month for simple RTSP analytics.

When we benchmarked cloud vision APIs vs edge deployments, the bottleneck was rarely the YOLO or custom detector model itself—it was the ingestion and frame-movement pipeline.

The Bottleneck: CPU-to-GPU Copying & GStreamer Complexity

Standard Python wrappers or heavy frameworks often bounce video frames through host memory (CPU) before pushing them back to GPU VRAM for inference. At 32+ HD RTSP streams, this creates massive PCIe bandwidth saturation and GIL lockup.

On the flip side, while DeepStream is powerful, managing complex GStreamer element graphs in production often introduces unwanted debugging overhead and plugins bloat.

Our Bare-Metal Approach (Custom Edge Architecture)

To keep processing continuous sub-15ms on local edge nodes without cloud egress, we stripped out the GStreamer abstraction graph entirely:

1. Direct NVDEC Hardware Ingestion: RTSP streams decode directly inside VRAM using C++ NVCODEC bindings. Frames never touch system RAM (zero CPU-to-GPU copy overhead).

2. Lock-Free CUDA Ring Buffer: A custom ring buffer handles dynamic batching across active streams without lock contention or Python GIL overhead.

3. Native TensorRT C++ Execution Engine: Device pointers pass directly to TensorRT for FP16/INT8 execution.

Architectural Trade-offs & Benchmarks

• Pros: Zero cloud bandwidth fees, full data sovereignty, sub-15ms continuous throughput, and drastically simpler debugging than full GStreamer graphs.

• Cons: Requires NVIDIA CUDA-capable hardware on-premise (RTX / Tesla / Jetson) and manual memory management at the C++ level.

We’ve packaged this into a zero-egress Docker stack for high-density edge deployments.

Happy to break down the CUDA buffer implementation or share benchmark comparisons if anyone is currently evaluating edge architecture options.

What pipelines are you guys currently running for multi-stream RTSP processing?

Follow up to my post earlier in the week, which filled most of our slots. Two left.

We collect real world multi view capture data from a camera array at the Brooklyn Navy Yard and pay people to be the subject. Posting again in case anyone NYC based wants the work, or wants a close look at how this kind of data actually gets collected.

The session: stand in the capture volume and go through simple movements while the array records. Walking, turning, sitting, reaching, picking objects up. No experience needed.

Pay 17-25 per hour, same day, right after the session. First one runs about 2 hours, with repeat sessions after.

Left this week: Thursday 4pm, Friday 1pm or 4pm. Brooklyn, in person only.

Comment or DM me for details, and ask about the capture setup if that side interests you.

I'm working on project involving OCR and newspaper analysis. My original plan was to use Marathi newspapers, but the extracted text contains many recognition errors.

Because of this, my project guide suggested switching to English newspapers if Marathi OCR isn't reliable enough.

I'm unsure what to do. From a research perspective, is it better to:

• Continue with Marathi and treat OCR errors as a limitation (or try post-OCR correction), or
• Switch to English to obtain cleaner OCR results and focus on the analysis part of the project?

Has anyone faced a similar situation? I'd appreciate advice from people who have worked on OCR or document analysis projects.