5 ms·
Qwen3-VL can scan two-hour videos and pinpoint nearly every detail
- thot_experiment 11mo agoanyone have a tl;dr for me on what the best way to get the video comprehension stuff going is? i use qwen-30b-vl all the time locally as my goto model because it's just so insanely fast, curious to mess with the video stuff, the vision comprehension works great and i use it for OCR and classification all the time
- xrd 11mo agoHow much VRAM do you need for local usage may I ask?
- moralestapia 11mo agoTo me, this qualifies as some sort ASI already.
- visioninmyblood 11mo agoI was using this for video understanding with inference form vlm.run infra. It definitely has outperformed Gemini which generally is much better than openai or Claude on videos. The detailed extraction is pretty good. With agents you can also crop into a segment and do more operations on it. have to see how the multi modal space progresses: link to results: https://chat.vlm.run/c/82a33ebb-65f9-40f3-9691-bc674ef28b52 https://chat.vlm.run/c/82a33ebb-65f9-40f3-9691-bc674ef28b52 Quick demo: https://www.youtube.com/watch?v=78ErDBuqBEo https://www.youtube.com/watch?v=78ErDBuqBEo
- colechristensen 11mo agoI 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
- visioninmyblood 11mo agoI agree claude and chatgpt and even gemini does a poor job in detecting and cropping into a region. Some of the simplest tasks, Qwen also is great at summerization but not into solving simple vision tasks like cropping, segmentetation and detection. Here is an examples where we compared claude, gemini, chatgpt and other frontier models for simple(and complicated) visual tasks https://chat.vlm.run/showdown#:~:text=Crop%20into%20the%20clock%20in%20the%20image%20and%20extract%20the%20time%20shown https://chat.vlm.run/showdown#:~:text=Crop%20into%20the%20cl...
- colechristensen 11mo agoThe part that was funny to me is I would respond "is that right?" and it would tell me exactly how it was wrong and proceed to do it incorrectly again in a very similar but different way. It was like a Monty Python sketch. I might have also been very tired and easily amused.
- djmips 11mo agoDoes anyone else worry about this technology used for Big Brother type surveillance?
- reactordev 11mo agoWhere 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 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 https://www.revir.ai
- mptest 11mo agoor if you prefer your depression in book format: surveillance capitalism by zuboff pegasus: a spy in your pocket laurent richard
- eurekin 11mo agoNo mention of palantir?
- bilbo0s 11mo ago>It’s already in use, or models like it, by companies selling access to The State Doesn't that pretty much cover Palantir as well?
- bigyabai 11mo agoPalantir's just the new guy on the block: https://en.wikipedia.org/wiki/Sentient_(intelligence_analysis_system) https://en.wikipedia.org/wiki/Sentient_(intelligence_analysi...
- fragmede 11mo agoor even https://en.wikipedia.org/wiki/IBM_and_the_Holocaust https://en.wikipedia.org/wiki/IBM_and_the_Holocaust
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- spwa4 11mo agoIt's so weird how that works with transformers. Finetuning an LLM "backbone" (if I understand correctly: a fully trained but not instruction tuned LLM, usually small because students) with OCR tokens bests just about every OCR network out there. And it's not just OCR. Describing images. Bounding boxes. Audio, both ASR and TTS, all works better that way. Now many research papers are only really about how to encode image/audio/video to feed it into a Llama or Qwen model.
- zmmmmm 11mo agoIt 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.
- jepj57 11mo agoNow apply that thinking to human-based neural nets...
- kgeist 11mo ago>People still talk about fine tuning dedicated models being effective >it's still always better to use a larger generalist model than a smaller fine tuned one Smaller fine-tuned models are still a good fit if they need to run on-premises cheaply and are already good enough. Isn't it their main use case?
- bangaladore 11mo agoLatency and size. Otherwise pretty much useless.
- eurekin 11mo agoInsane if true... now I wonder, if I use it to go through some old dance routing video catalogue to recognize and write individual move lists
- iib 11mo agoI have been looking for the same thing, either from Meta's SAM 3[1] model, either from things like the OP. There has been some research specifically in this area with what appears to be classic ML models [2], but it's unclear to me if it can generalize to dances it has not been trained on. [1] https://ai.meta.com/blog/segment-anything-model-3/ https://ai.meta.com/blog/segment-anything-model-3/ [2] https://arxiv.org/html/2405.19727v1 https://arxiv.org/html/2405.19727v1
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- mikae1 11mo agoHope this on day will be used for auto-tagging all video assets with time codes. The dream of being able to search for running horse and find a clip containing a running horse at 4m42s in one of thousands of clips.
- laidoffamazon 11mo agoIt’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
- dynode 11mo agoWould you be willing to share more details of what you did?
- laidoffamazon 11mo agoSure. I had a lot of help from Claude Opus 4.5, but it was roughly: - Using pyscenedetect to split each video on a per scene level - Using the decord library https://github.com/dmlc/decord https://github.com/dmlc/decord to pull frames from each scene at a particular sample rate (specific rate I don't have handy right now, but it was 1-2 per scene) - Aggregating frames in batches of around 256 frames to be normalized for CLIP embedding on GPU (had to re-write the normalization process for this because the default library does it on CPU) - Uploading the frames along with metadata (timestamp, etc) into a vector DB, in my case Qdrant running locally along with a screenclip of the frame itself for debugging. I'm bottlenecked by GPU compute so I also started experimenting with using Modal for the embedding work too, but then vacation ended :) Might pick it up again in a few weeks. I'd like to be able to have a temporal-aware and potentially enriched search so that I can say "Seek to the scene in Oppenheimer where Rami Malek testifies" and be able to get a timestamped clip from the movie.
- vhcr 11mo agoI'm guessing you're not storing the CLIP for every single frame, instead of every second or so? Also, are you using the cosine similarity? How are you finding the nearest vector?
- clusterhacks 11mo agoI was playing around with Qwen3-VL to parse PDFs - meaning, do some OCR data extraction from a reasonably well-formated PDF report. Failed miserably, although I was using the 30B-A3B model instead of the larger one. I like the Qwen models and use them for other tasks successfully. It is so interesting how LLMs will do quite well in one situation and quite badly in another.
- totetsu 11mo agoThe opus models seems pretty adept and extracting structured data from ocr https://www.ocrarena.ai/battle https://www.ocrarena.ai/battle
- coppsilgold 11mo ago> The test works by inserting a semantically important "needle" frame at random positions in long videos, which the system must then find and analyze. This seems to be somewhat unwise. Such an insertion would qualify as an anomaly. And if it's also trained that way, would you not train the model to find artificial frames where they don't belong? Would it not have been better to find a set of videos where something specific (common, rare, surprising, etc) happens at some time and ask the model about that?
- bigmadshoe 11mo agoYeah the needle in a haystack tests are so stupid. It seems clear with LLMs that performance degrades massively with context size, yet those tests claim the model performs perfectly.
- patates 11mo agoAs someone who abuses gemini regularly with a 90% full context, the model performance does degrade for sure but I wouldn't call it massively. I can't show any evidence as I don't have such tests, but it's like coding normally vs coding after a beer or two. For the massive effect, fill it 95% and we're talking vodka shots. 99%? A zombie who can code. But perhaps that's not fair when you have 1M token context size.
- oceansweep 11mo agohttps://fiction.live/stories/Fiction-liveBench-Mar-25-2025/oQdzQvKHw8JyXbN87 https://fiction.live/stories/Fiction-liveBench-Mar-25-2025/o...
- IanCal 11mo agoThat rather depends on exactly how this is done, although it's a useful upper bound for many tasks either way. You could say the same for images and yet due to the way some work they straight up cannot see in certain ways. This could describe adding a frame of nonsense into an existing video. It also could describe finding a semantically useful thing in an actual video, where the exact location is randomised by looking at different time crops of the video. For example, finding a book on a desk in a video that's only there in a panning shot, and you then see if it can find it in a 10s cut, 20s cut, 10 minute cut, etc, and near the start/middle/end. Here's the paper: https://arxiv.org/pdf/2511.21631 https://arxiv.org/pdf/2511.21631 > To evaluate the model’s capability in processing long-context inputs, we construct a video “Needle-ina-Haystack” evaluation on Qwen3-VL-235B-A22B-Instruct. In this task, a semantically salient “needle” frame—containing critical visual evidence—is inserted at varying temporal positions within a long video. The model is then tasked with accurately locating the target frame from the long video and answering the corresponding question. During evaluation, videos are uniformly sampled at 1 FPS, and frame resolution is dynamically adjusted to maintain a constant visual token budget. This potentially sounds more like the former, but I can't find more accurate information on how this works. Regardless I'd say again that while not the whole story things like this really are useful to know, and can be very important to test - it's really not a given that models can always find anything in their context window, perhaps even more so for video.
- chhxdjsj 11mo agoNot so relevant to the thread but ive been uploading screenshots from citrix guis and asking qwen3-vl for the appropriate next action eg Mouseclick, and while it knows what to click it struggles to accurately return which pixel coordinates to click. Anyone know a way to get accurate pixel coordinates returned?
- 8f2ab37a-ed6c 11mo agoAlso curious about this. I tried https://moondream.ai/ https://moondream.ai/ as well for this task and it felt still far from being bulletproof.
- jazzyjackson 11mo agoCould you combine it with a classic OCR segmentation process, so that along with the image you also provide box coordinates of each string?
- logankeenan 11mo agoIt’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://huggingface.co/allenai/Molmo-7B-D-0924 https://github.com/logankeenan/george https://github.com/logankeenan/george https://github.com/microsoft/OmniParser https://github.com/microsoft/OmniParser
- chhxdjsj 11mo agoThanks ill try this!
- visioninmyblood 11mo agoyou 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 https://chat.vlm.run/c/e12f0153-7121-4599-9eb9-cd8c60bbbd69
- CSMastermind 11mo agoStill not great at the use cases I tested it for but Gemini isn't either. I think we're still very early on video comprehension.
- re5i5tor 11mo agoFor anyone using Qwen3-VL: where are you running it? I had tons of reliability problems with Qwen3-VL inference providers on OpenRouter — based on uptime graphs I wasn’t alone. But when it worked, Qwen3-VL was pack-leading good at AI Vision stuff.
- m00dy 11mo agoI run it on ollama
- lreeves 11mo agoI run the larger version of it on a Threadripper with 512GB RAM and a 32GB GPU for the non-expert layers and context, using llama.cpp. Performs great, however god forbid you try to get that much memory these days.
- sosodev 11mo agoI’ve noticed that the open weight models have a lot of issues on OpenRouter. You get a lot of inconsistency in quality due to varying quants at least. I’ve had some seriously nonsensical responses from models that I can’t replicate at all when I switch providers. Lots that just randomly fail to handle requests too. I would recommend finding a provider that works best for your needs and pinning it.
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- m00dy 11mo agoIve used qwen3-VL on deepwalker lately. All I can stay is that this model is so underrated. [0]: https://deepwalker.xyz https://deepwalker.xyz
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- Alifatisk 11mo agoMany of the models we have today seem to only perform OCR on the images you send and use the text retrieved for context when answering. However, Qwen-VL, and I guess Gemini now? Are different, they seem to "understanding" the image I send with my prompt. They manage to capture spatial relationships, objects, and semantics from the image, it's very impressive. I’ve been telling my friends about the Qwen3-VL model option in Qwen Chat for a while because I feel like it’s underrated.
- neves 11mo agoMy favorite AI feature is to put a YouTube link in Gemini and ask it to summarize. Or even better: put the link of a 20min video "5 ways to" and ask "what are the 5 ways?" I think Gemini analyzes the transcription. Can I do the same for free with Qwen3?
- seidleroni 11mo agoI don't believe that it just analyzes the transcription. I asked Gemini to look at the youtube video referenced on the site below and "build" something that duplicates that device. It did a pretty good approximation that it could not have done without going through the full video. https://bitsnpieces.dev/posts/a-synth-for-my-daughter/ https://bitsnpieces.dev/posts/a-synth-for-my-daughter/
- IncreasePosts 11mo agoSame - I see a lot of "vaguely interesting but no way I'm spending 40 minutes on that" kind of videos, and it usually works. However, I have noticed it occasionally will just summarize the wrong video for me. It might be if the video is very new, or something? I'm not sure.
- crispyambulance 11mo agoI didn't understand what is meant by "pinpoint nearly every detail". The article is titled with that but then firehoses a bunch of technical details. The github spells it out much better: https://github.com/QwenLM/Qwen3-VL?tab=readme-ov-file#cookbooks https://github.com/QwenLM/Qwen3-VL?tab=readme-ov-file#cookbo...
- ta12653421 11mo agoso, a great politician once said: "the internet is for p0rn" - and if we accept this, what will this thing pinpoint in p0rn videos? :-D LOL
- spider-mario 11mo agoThere it is! Oh, it disappeared. There it is again! Oh, it disappeared. It’s back! Wait, no.
- DrAwdeOccarim 11mo agoDoes anyone know how this actually was done? Like, did they export every frame as a PNG and then run them each one by one through the model? Or did they somehow "load" the video into the model directly (which then internally somehow steps through each frame?)