🚗✨ I’m happy to share that I’m starting a new position as Research Engineer at @Waymo today!
🎓✨ This comes shortly after defending my PhD at @umdcs, where I had the privilege of working with and learning from amazing collaborators at @gammaumd. A heartfelt thank you to my advisor, Prof. Dinesh Manocha @dmanocha, for his constant support and guidance throughout this journey.
🥳 Excited to join the incredible team with John Kang and Khaled S. Refaat, and contribute to building the future of autonomous driving. Looking forward to the road ahead at Waymo! 🙌
#Waymo #AutonomousDriving #AI #Robotics #ComputerVision #UMD #UMDCS
Introducing Mochi 1 preview. A new SOTA in open-source video generation. Apache 2.0.
magnet:?xt=urn:btih:441da1af7a16bcaa4f556964f8028d7113d21cbb&dn=weights&tr=udp://tracker.opentrackr.org:1337/announce
✅ @terryguan97 and @kongkong from @umdcs will discuss three separate works: (1) HallusionBench -- manual hallucination benchmark creation, (2) AUTOHALLUSION -- automatic hallucination benchmark curation, and (3) Eagle -- hallucination alleviation and mitigation.
https://t.co/Ol3E8lpnh3
Vision-Language Models (VLMs) suffer from hallucinations👀🙈: their reasoning may ignore some objects or create non-existing ones. This is usually caused by their language model bias dominating the facts in the input visual signals, just like the human brain relying on memory🧠 and past experiences🌎 (called "schema" in cognitive science).
But building a benchmark to evaluate and diagnose VLM hallucinations need a lot of human efforts #HallusionBench.
Can we automatically generate (image, prompt) pairs that can induce hallucinations of VLMs? 🤔
The answer is Yes.
We introduce #AutoHallusion🚀, an automatic hallucination pipeline that scales up the benchmark generation process. It probes LLMs to determine image content that contradicts with the LLM prior, and then generate the image by manipulating objects in the image using image-editing models. (1/n)👇
In addition to creating benchmarks📏, our proposed method allows us to easily scale up the number of synthetic data that induce hallucinations. This can be used for tailored hallucination mitigation in different Vision-Language Models. (7/n)👇
Just spotted an incredible robot 🤖 from Disney Research at @CVPR! It's amazing to see robot in animation come to life in the real world. 🌟 #Robotics#CVPR2024
🔥🔥🔥Excited to present #HallusionBench at the @MMFMWorkshop and @CVPR! 🎉 Catch me 👀 at poster #97 on Tuesday, June 18, from 9:40-11:00 AM and at poster session #457 on Thursday, June 20, from 10:30 AM to noon.
Come by and let's chat! 🥳