SenBen: Sensitive Scene Graphs for Explainable Content Moderation
Paper • 2604.08819 • Published • 1
AI & ML interests
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fcakyon
authored a
paper 7 days ago
Post
95
Let me introduce you to our CVPR 2026 paper!
Today's content moderation systems give you a label: safe or unsafe. They don't tell you what triggered the decision, who is involved, or where in the image it happens. That opacity hurts auditing, breaks adaptation across platforms, and frustrates the human review that responsible deployment demands.
We built SenBen to fix this: the first large-scale scene graph benchmark designed specifically for sensitive content moderation:
- 13,999 annotated frames from 157 movies
- Visual Genome style scene graphs with bounding boxes, attributes, and predicates
- Affective state attributes (pain, fear, aggression, distress) so the model captures not just what is in the frame, but what it means
- 16 safety tags across 5 categories, the broadest taxonomy of any dataset of this kind
A small model that beats much bigger ones:
We distilled a frontier VLM into a compact 241M parameter student built on Florence-2.
On grounded scene graph metrics, the 241M student beats every evaluated VLM except Gemini, and every commercial safety API. It also wins on object detection and captioning across the entire model zoo. It runs at 733 ms per frame on 1.2 GB VRAM, which is 7.6 times faster than the next-best local VLM at zero per-frame cost. The whole benchmark, from dataset creation through all baseline evaluations, is reproducible for under $350.
Project: https://senben.kim/
Paper: SenBen: Sensitive Scene Graphs for Explainable Content Moderation (2604.08819)
Dataset: fcakyon/senben
Code (soon): https://github.com/fcakyon/senben
Today's content moderation systems give you a label: safe or unsafe. They don't tell you what triggered the decision, who is involved, or where in the image it happens. That opacity hurts auditing, breaks adaptation across platforms, and frustrates the human review that responsible deployment demands.
We built SenBen to fix this: the first large-scale scene graph benchmark designed specifically for sensitive content moderation:
- 13,999 annotated frames from 157 movies
- Visual Genome style scene graphs with bounding boxes, attributes, and predicates
- Affective state attributes (pain, fear, aggression, distress) so the model captures not just what is in the frame, but what it means
- 16 safety tags across 5 categories, the broadest taxonomy of any dataset of this kind
A small model that beats much bigger ones:
We distilled a frontier VLM into a compact 241M parameter student built on Florence-2.
On grounded scene graph metrics, the 241M student beats every evaluated VLM except Gemini, and every commercial safety API. It also wins on object detection and captioning across the entire model zoo. It runs at 733 ms per frame on 1.2 GB VRAM, which is 7.6 times faster than the next-best local VLM at zero per-frame cost. The whole benchmark, from dataset creation through all baseline evaluations, is reproducible for under $350.
Project: https://senben.kim/
Paper: SenBen: Sensitive Scene Graphs for Explainable Content Moderation (2604.08819)
Dataset: fcakyon/senben
Code (soon): https://github.com/fcakyon/senben
selvatas
authored a
paper 19 days ago
adirik
authored 2
papers about 2 months ago
umuthopeyildirim
authored a
paper 4 months ago
selvatas
authored a
paper 6 months ago
Post
12132
deepseek-ai/DeepSeek-OCR is out! 🔥 my take ⤵️
> pretty insane it can parse and re-render charts in HTML
> it uses CLIP and SAM features concatenated, so better grounding
> very efficient per vision tokens/performance ratio
> covers 100 languages
> pretty insane it can parse and re-render charts in HTML
> it uses CLIP and SAM features concatenated, so better grounding
> very efficient per vision tokens/performance ratio
> covers 100 languages
- 4 replies
selvatas
authored a
paper 8 months ago
Post
7008
large AI labs open-sourced a ton of models last week 🔥
here's few picks, find even more here merve/sep-16-releases-68d13ea4c547f02f95842f05 🤝
> IBM released a new Docling model with 258M params based on Granite (A2.0) 📝 ibm-granite/granite-docling-258M
> Xiaomi released 7B audio LM with base and instruct variants (MIT) XiaomiMiMo/mimo-audio-68cc7202692c27dae881cce0
> DecartAI released Lucy Edit, open Nano Banana 🍌 (NC) decart-ai/Lucy-Edit-Dev
> OpenGVLab released a family of agentic computer use models (3B/7B/32B) with the dataset 💻 OpenGVLab/scalecua-68c912cf56f7ff4c8e034003
> Meituan Longcat released thinking version of LongCat-Flash 💭 meituan-longcat/LongCat-Flash-Thinking
here's few picks, find even more here merve/sep-16-releases-68d13ea4c547f02f95842f05 🤝
> IBM released a new Docling model with 258M params based on Granite (A2.0) 📝 ibm-granite/granite-docling-258M
> Xiaomi released 7B audio LM with base and instruct variants (MIT) XiaomiMiMo/mimo-audio-68cc7202692c27dae881cce0
> DecartAI released Lucy Edit, open Nano Banana 🍌 (NC) decart-ai/Lucy-Edit-Dev
> OpenGVLab released a family of agentic computer use models (3B/7B/32B) with the dataset 💻 OpenGVLab/scalecua-68c912cf56f7ff4c8e034003
> Meituan Longcat released thinking version of LongCat-Flash 💭 meituan-longcat/LongCat-Flash-Thinking
- 2 replies
Post
3535
IBM just released small swiss army knife for the document models: granite-docling-258M on Hugging Face 🔥
> not only a document converter but also can do document question answering, understand multiple languages 🤯
> best part: released with Apache 2.0 license 👏 use it with your commercial projects!
> it supports transformers, vLLM and MLX from the get-go! 🤗
> built on SigLIP2 & granite-165M
model: ibm-granite/granite-docling-258M
demo: ibm-granite/granite-docling-258m-demo 💗
> not only a document converter but also can do document question answering, understand multiple languages 🤯
> best part: released with Apache 2.0 license 👏 use it with your commercial projects!
> it supports transformers, vLLM and MLX from the get-go! 🤗
> built on SigLIP2 & granite-165M
model: ibm-granite/granite-docling-258M
demo: ibm-granite/granite-docling-258m-demo 💗
Post
1288
a ton of image/video generation models and LLMs from big labs 🔥
> Meta released facebook/mobilellm-r1-68c4597b104fac45f28f448e, smol LLMs for on-device use 💬
> Tencent released tencent/SRPO, high res image generation model and tencent/POINTS-Reader, cutting edge OCR 📝
> ByteDance released bytedance-research/HuMo, video generation from any input ⏯️
find more models, datasets, demos here merve/sep-11-releases-68c7dbfa26bea8cd921fa0ac
> Meta released facebook/mobilellm-r1-68c4597b104fac45f28f448e, smol LLMs for on-device use 💬
> Tencent released tencent/SRPO, high res image generation model and tencent/POINTS-Reader, cutting edge OCR 📝
> ByteDance released bytedance-research/HuMo, video generation from any input ⏯️
find more models, datasets, demos here merve/sep-11-releases-68c7dbfa26bea8cd921fa0ac
selvatas
authored a
paper 8 months ago
Post
1079
fan-favorite vision LM Florence-2 is now officially supported in transformers 🤗
find all the models in
florence-community org 🫡
find all the models in
Post
1876
past week was great for open LLMs 🔥 merve/sep-1-releases-68bede0e729c12597eefd050
> Google released google/embeddinggemma-300m, new embedding model with 300M params
> new update to Kimi-K2 just landed moonshotai/Kimi-K2-Instruct-0905 😍
> OpenBMB released a new version to MiniCPM with 8B params openbmb/MiniCPM4.1-8B
also soooo many Qwen-Image & Kontext LoRAs dropped!
> Google released google/embeddinggemma-300m, new embedding model with 300M params
> new update to Kimi-K2 just landed moonshotai/Kimi-K2-Instruct-0905 😍
> OpenBMB released a new version to MiniCPM with 8B params openbmb/MiniCPM4.1-8B
also soooo many Qwen-Image & Kontext LoRAs dropped!
Post
3766
upgrade your transformers 🔥
it comes with insanely capable models like merve/sam2-66ac9deac6fca3bc5482fe30, microsoft/kosmos-2.5, and more 🫡
I built a notebook you can run with free Colab T4 to walk through the API for new models 🙋🏻♀️ merve/smol-vision
fine-tuning will follow-up soon!
it comes with insanely capable models like merve/sam2-66ac9deac6fca3bc5482fe30, microsoft/kosmos-2.5, and more 🫡
I built a notebook you can run with free Colab T4 to walk through the API for new models 🙋🏻♀️ merve/smol-vision
fine-tuning will follow-up soon!
Post
6327
large AI labs have dropped so many open models last week 🔥 don't miss out on them
→ Apple released on-device vision LMs apple/fastvlm-68ac97b9cd5cacefdd04872e & apple/mobileclip2-68ac947dcb035c54bcd20c47
→ OpenGVLab released InternVL3.5, 32 new vision LMs with one based on gpt-oss! (OS) OpenGVLab/internvl35-68ac87bd52ebe953485927fb
→ MSFT released a killer small TTS model (OS) microsoft/VibeVoice-1.5B
find more herehttps://huggingface.co/collections/merve/august-29-releases-68b5a3754cfb8abf59e2b486
→ Apple released on-device vision LMs apple/fastvlm-68ac97b9cd5cacefdd04872e & apple/mobileclip2-68ac947dcb035c54bcd20c47
→ OpenGVLab released InternVL3.5, 32 new vision LMs with one based on gpt-oss! (OS) OpenGVLab/internvl35-68ac87bd52ebe953485927fb
→ MSFT released a killer small TTS model (OS) microsoft/VibeVoice-1.5B
find more herehttps://huggingface.co/collections/merve/august-29-releases-68b5a3754cfb8abf59e2b486
- 1 reply
Post
6116
first vision language model built off openai/gpt-oss-20b just dropped! 🔥
InternVL3.5 comes with 32 models 🤯 pre-trained, fine-tuned, aligned in various sizes OpenGVLab/internvl35-68ac87bd52ebe953485927fb
comes with gpt-oss or Qwen3 for LLM part ⤵️
InternVL3.5 comes with 32 models 🤯 pre-trained, fine-tuned, aligned in various sizes OpenGVLab/internvl35-68ac87bd52ebe953485927fb
comes with gpt-oss or Qwen3 for LLM part ⤵️
- 1 reply
Post
2986
What can you do with the VyvoTTS library?
- You can train a model in a language it has never been trained in using the PT model. There’s no need for large datasets.
- With the PT model, you can easily replicate the voice of any character you want. Just 1k samples are enough.
- You can add emotion support with a small dataset.
Github: https://github.com/Vyvo-Labs/VyvoTTS
HuggingFace:
Vyvo
- You can train a model in a language it has never been trained in using the PT model. There’s no need for large datasets.
- With the PT model, you can easily replicate the voice of any character you want. Just 1k samples are enough.
- You can add emotion support with a small dataset.
Github: https://github.com/Vyvo-Labs/VyvoTTS
HuggingFace:
Post
3352
GPT-4.1-mini level model right in your iPhone 🤯
openbmb/MiniCPM-V-4 is only 4B while surpassing GPT-4.1-mini in vision benchmarks 🔥
allows commercial use as well!
openbmb/MiniCPM-V-4 is only 4B while surpassing GPT-4.1-mini in vision benchmarks 🔥
allows commercial use as well!