🚨 Introducing our @tmlrorg.bsky.social paper “Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation”
We present UnLOK-VQA, a benchmark to evaluate unlearning in vision-and-language models, where both images and text may encode sensitive or private information.

Flying to SG 🇸🇬 to attend #ICLR2025.

Check out our 3 papers:

☕️CREMA: Video-language + any modality reasoning

🛡️SAFREE: A training-free concept guard for any visual diffusion models

🧭SRDF: Human-level VL-navigation via self-refined data loop

feel free to DM me to grab a coffee&citywalk together 😉

🚨Real-world retrieval is messy: queries are ambiguous or docs conflict & have incorrect/irrelevant info. How can we jointly address these problems?

➡️RAMDocs: challenging dataset w/ ambiguity, misinformation & noise
➡️MADAM-RAG: multi-agent framework, debates & aggregates evidence across sources

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🚨Announcing TaCQ 🚨 a new mixed-precision quantization method that identifies critical weights to preserve. We integrate key ideas from circuit discovery, model editing, and input attribution to improve low-bit quant., w/ 96% 16-bit acc. at 3.1 avg bits (~6x compression)

📃 arxiv.org/abs/2504.07389

Thanks to all of my amazing co-authors from Adobe Research, UMichi, UNC

Difan Liu (co-lead), @marstin.bsky.social (co-led)
Yicong Hong, Yang Zhou, Hao Tan, Joyce Chai, @mohitbansal.bsky.social

Check out more details on our homepage/paper.
Website: veggie-gen.github.io

We further find that VEGGIE shows emergent zero-shot multimodal instruction following and in-context video editing ability, which may facilitate a broader range of future applications.

We project grounded queries into 2D spaces with PCA & t-SNE. We found Reasoning and Grounding cluster together, while Color, Env, and Change are closely grouped. Addition aligns with Reasoning and Grounding, suggesting addition involves semantic processes, while Removal is a more independent task.

we evaluate 7 different models on VEG-Bench across 8 distinct editing skills. Overall, VEGGIE demonstrates the best performance among instructional video editing models.

To further support our training, we also introduce a novel automatic instructional video data generation pipeline that lifts high-quality instructional image editing data into the video domain using image-to-video and video evaluation tools.

VEGGIE first leverages an MLLM to interpret complex instructions, generating frame-wise conditions, and then a video diffusion model is applied to reflect these conditions at the pixel space. Such continuous, learnable task query embeddings enable end-to-end training & capture task representations.

Existing video editing methods fall short of the goal of a simple, versatile video editor, requiring multiple models, complex pipelines, or extra caption/layout/human guidance. We introduce VEGGIE which formulates diverse editing tasks as end-to-end grounded generation in pixel space.

Introducing VEGGIE 🥦—a unified, end-to-end, and versatile instructional video generative model.

VEGGIE supports 8 skills, from object addition/removal/changing, and stylization to concept grounding/reasoning. It exceeds SoTA and shows 0-shot multimodal instructional & in-context video editing.

🎉 Congrats to the awesome students, postdocs, & collaborators for this exciting batch of #ICLR2025 and #NAACL2025 accepted papers (FYI some are on the academic/industry job market and a great catch 🙂), on diverse, important topics such as:

-- adaptive data generation environments/policies
...
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🚨 We have postdoc openings at UNC 🙂

Exciting+diverse NLP/CV/ML topics**, freedom to create research agenda, competitive funding, very strong students, mentorship for grant writing, collabs w/ many faculty+universities+companies, superb quality of life/weather.

Please apply + help spread the word 🙏

I was so lucky to work with Jaemin in my 1st year and learned a lot from him. I can confidently say he's not only a top mind in multimodal AI but also an incredible mentor&collaborator. He is insightful, hands-on, and genuinely knows how to guide and inspire junior students👇👏

🚨 I am on the faculty job market this year 🚨
I will be presenting at #NeurIPS2024 and am happy to chat in-person or digitally!

I work on developing AI agents that can collaborate and communicate robustly with us and each other.

More at: esteng.github.io and in thread below

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Looking forward to giving this Distinguished Lecture at StonyBrook next week & meeting the several awesome NLP + CV folks there - thanks Niranjan‬ + all for the kind invitation 🙂

PS. Excited to give a new talk on "Planning Agents for Collaborative Reasoning and Multimodal Generation" ➡️➡️

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🚨 Reverse Thinking Makes LLMs Stronger Reasoners

We can often reason from a problem to a solution and also in reverse to enhance our overall reasoning. RevThink shows that LLMs can also benefit from reverse thinking 👉 13.53% gains + sample efficiency + strong generalization (on 4 OOD datasets)!