Qwen3-VL can scan two-hour videos and pinpoint nearly every detail

you can do that with Morphik already :)

We use an embedding model that processes videos and allows you to perform RAG on them.

It’s not difficult to hack this together with CLIP. I did this with about a tenth of my movie collection last week with a GTX 1080 - though it lacks temporal understanding so you have to do the scene analysis yourself

I run it on ollama

Where have you been the last decade? It’s already in use, or models like it, by companies selling access to The State

https://deflock.me

Not to mention cloud platforms that collect evidence and process it with all the models and store that information for searching…

https://www.revir.ai

It was already used before current AI explosion.

This is why keeping our governments from eating that tasty apple of "if you can record AND analyse everything there will be so much less crime" and "just give us keys to all private communication, we swear we will just use it to find bad guys". Because someone will, and someone will use it to hit on people they don't like

This tech would be a massive waste of computational resources to do that. Technology for what you said is way more efficient and has been working well for years now.

How do you think this tech was developed in the first place? It's probably trained and used in the surveillance bid for a decade before it comes to consumers, and this probably isn't the SoA stuff that governments have access to, we're probably 5-10 years behind what's on the cutting edge.

We got Facial Rec and LPR first, those are more dangerous for surveillance.

warmly encourage you avoid reading the header files of the dahua camera SDK

In surveillance and police states like The Netherlands it has been used since forever:

https://www.theguardian.com/cities/2018/mar/01/smart-cities-...

Now people will say again that this project has been abandoned, which just isn't true (2024):

https://www.dutchnews.nl/2024/06/smart-street-surveillance-o...

2009, you rang?

Big Brother is a reference to George Orwell's critique of Communism in Nineteen Eighty-Four.

Qwen is a video model trained by a Communist government, or technically by a company with very close ties to the Chinese government. The Chinese government also has laws requiring AI be used to further the political goals of China in particular and authoritarian socialism in general.

In the light of all this, I think it's reasonable to conclude that this technology will be used for Big Brother type surveillance and quite possible that it was created explicitly for that purpose.

How do you prompt the model? In my experience, Qwen3-VL models have very accurate grounding capabilities (I’ve tested Qwen3-VL-30B-A3B-Instruct, Qwen3-VL-30B-A3B-Thinking, and Qwen3-VL-235B-A22B-Thinking-FP8).

Note that the returned values are not direct pixel coordinates. Instead, they are normalized to a 0–1000 range. For example, if you ask for a bounding box, the model might output:

```json [ {"bbox_2d": [217, 112, 920, 956], "label": "cat"} ] ```

Here, the values represent [x_min, y_min, x_max, y_max]. To convert these to pixel coordinates, use:

[x_min / 1000 * image_width, y_min / 1000 * image_height, x_max / 1000 * image_width, y_max / 1000 * image_height]

Also, if you’re running the model with vLLM > 0.11.0, you might be hitting this bug: https://github.com/vllm-project/vllm/issues/29595

It’s been about a year since I looked into this sort of thing, but molmo will give you x,y coordinates. I hacked together a project about it. I also think Microsoft’s omniparser is good at finding coordinates too.

https://huggingface.co/allenai/Molmo-7B-D-0924

https://github.com/logankeenan/george

https://github.com/microsoft/OmniParser

Could you combine it with a classic OCR segmentation process, so that along with the image you also provide box coordinates of each string?

It's very not accurate, but sometimes instructing to return pyautogui code works.

  prompt: I attach a screenshot (1920x1080). Write code to click the submit button using pyautogui.
  attachment: <screenshot>
  reply:
    import pyautogui
    pyautogui.click(100, 200)

Also curious about this. I tried https://moondream.ai/ as well for this task and it felt still far from being bulletproof.

you want get the exact coordinated by running a key point network to pinpoint which coordinates does the next click point is you can. here I show a example simple prompt which returns the keypoint location of the next botton to click and visually localize the point with a keypoint in the image

https://chat.vlm.run/c/e12f0153-7121-4599-9eb9-cd8c60bbbd69

The opus models seems pretty adept and extracting structured data from ocr https://www.ocrarena.ai/battle

I found it pretty funny how bad Claude was at cropping an image. It was a cute little character with some text off to the side on a white background, all very clean cartoon vibes and it COULD NOT just select the character. I pursued it for 20 minutes because I thought it was funny. Of course it was 45 seconds to do it myself.

A lot of my side projects involve UIs and almost all of my problems with getting LLMs to write them for me involve "The UI isn't doing what you say it's doing" and struggling to get A) a reliable way to get it to look at the UI so it can continue its loop and B) getting it to understand what it's looking at well enough to do something about it

How much VRAM do you need for local usage may I ask?

It is fascinating. Vision language models are unreasonably good compared to dedicated OCR and even the language tasks to some extent.

My take is it fits into the general concept that generalist models have significant advantages because so much more latent structure maps across domains than we expect. People still talk about fine tuning dedicated models being effective but my personal experience is it's still always better to use a larger generalist model than a smaller fine tuned one.