Cheng-Yu Hsieh @cydhsieh
PhD student @UWcse chengyuhsieh.github.io Seattle, USA Joined September 2022-
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🔥We are excited to present our work Synthetic Visual Genome (SVG) at #CVPR25 tomorrow! 🕸️ Dense scene graph with diverse relationship types. 🎯 Generate scene graphs with SAM segmentation masks! 🔗Project link: bit.ly/4e1uMDm 📍 Poster: #32689, Fri 2-4 PM 👇🧵
Agentic AI will transform every enterprise–but only if agents are trusted experts. The key: Evaluation & tuning on specialized, expert data. I’m excited to announce two new products to support this–@SnorkelAI Evaluate & Expert Data-as-a-Service–along w/ our $100M Series D! ---…
1/8🧵 Thrilled to announce RealEdit (to appear in CVPR 2025)! We introduce a real-world image-editing dataset sourced from Reddit. Along with the training and evaluation datasets, we release our model that achieves SOTA performances on a variety of real-world editing tasks.
Stop by poster #596 at 10A-1230P tomorrow (Fri 25 April) at #ICLR2025 to hear more about Sigmoid Attention! We just pushed 8 trajectory checkpoints each for two 7B LLMs for Sigmoid Attention and a 1:1 Softmax Attention (trained with a deterministic dataloader for 1T tokens): -…
Stop by poster #596 at 10A-1230P tomorrow (Fri 25 April) at #ICLR2025 to hear more about Sigmoid Attention! We just pushed 8 trajectory checkpoints each for two 7B LLMs for Sigmoid Attention and a 1:1 Softmax Attention (trained with a deterministic dataloader for 1T tokens): -… https://t.co/ClLadGIs7I
The 2nd Synthetic Data for Computer Vision workshop at @CVPR! We had a wonderful time last year, and we want to build on that success by fostering fresh insights into synthetic data for CV. Join us! We welcome submissions! Please consider submitting your work! (deadline: March…
I'm exited to announce that our work (AURORA) got accepted into #CVPR2025🎉! Special thanks to my coauthors: @ch1m1m0ry0, @cydhsieh, @ethnlshn, @Dongping0612, Linda Shapiro and @RanjayKrishna, This work wouldn’t have been possible without them! See you all in Nashville 🎸!
I'm exited to announce that our work (AURORA) got accepted into #CVPR2025🎉! Special thanks to my coauthors: @ch1m1m0ry0, @cydhsieh, @ethnlshn, @Dongping0612, Linda Shapiro and @RanjayKrishna, This work wouldn’t have been possible without them! See you all in Nashville 🎸!
(1/5)🚨LLMs can now self-improve to generate better citations✅ 📝We design automatic rewards to assess citation quality 🤖Enable BoN/SimPO w/o external supervision 📈Perform close to “Claude Citations” API w/ only 8B model 📄arxiv.org/abs/2502.09604 🧑💻github.com/voidism/SelfCi…
Introducing AURORA 🌟: Our new training framework to enhance multimodal language models with Perception Tokens; a game-changer for tasks requiring deep visual reasoning like relative depth estimation and object counting. Let’s take a closer look at how it works.🧵[1/8]
I will be presenting our Lookback Lens paper at #EMNLP2024 in Miami! 📆 Nov 13 (Wed) 4:00-5:30 at Tuttle (Oral session: ML for NLP 1) 🔗 arxiv.org/abs/2407.07071 Happy to chat about LLMs and hallucinations! See you soon in Miami! ✈️ @linluqiu @cydhsieh @RanjayKrishna @yoonrkim
I will be presenting our Lookback Lens paper at #EMNLP2024 in Miami! 📆 Nov 13 (Wed) 4:00-5:30 at Tuttle (Oral session: ML for NLP 1) 🔗 arxiv.org/abs/2407.07071 Happy to chat about LLMs and hallucinations! See you soon in Miami! ✈️ @linluqiu @cydhsieh @RanjayKrishna @yoonrkim
Hard negative finetuning can actually HURT compositionality, because it teaches VLMs THAT caption perturbations change meaning, not WHEN they change meaning! 📢 A new benchmark+VLM at #ECCV2024 in The Hard Positive Truth arxiv.org/abs/2409.17958 @cydhsieh @RanjayKrishna @uclanlp
🤔 In training vision models, what value do AI-generated synthetic images provide compared to the upstream (real) data used in training the generative models in the first place? 💡 We find using "relevant" upstream real data still leads to much stronger results compared to using…
🤔 In training vision models, what value do AI-generated synthetic images provide compared to the upstream (real) data used in training the generative models in the first place? 💡 We find using "relevant" upstream real data still leads to much stronger results compared to using…
‼️ LLMs hallucinate facts even if provided with correct/relevant contexts 💡 We find models' attention weight distribution on input context versus their own generated tokens serves as a strong detector for such hallucinations 🚀 The detector transfers across models/tasks, and can…
‼️ LLMs hallucinate facts even if provided with correct/relevant contexts 💡 We find models' attention weight distribution on input context versus their own generated tokens serves as a strong detector for such hallucinations 🚀 The detector transfers across models/tasks, and can…
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