Thanks for covering our work on "Identification of Systematic Errors of Image Classifiers on Rare Subgroups", RSIP Vision (page 22-24)! Joint work with Robin Hutmacher, Grace Hua, Valentyn Boreiko 🇺🇦, Dan Zhang at Bosch Center for Artificial Intelligence. See you today at our #ICCV2023 poster from 2:30pm-4:30pm!

Great opportunity to work on Foundation Models in our group 👇

If you are at BMVC2023, please meet us at our oral talk tomorrow at 16:30 (UK GMT time). We will present a simple solution to help your T2I model better follow your instructions.

Tired of overthinking decisions in your life? I can't help you with that, but come to our NeurIPS poster #514 tomorrow (Tue, 12th Dec) between 5-7pm to see how to prevent your early-exit neural networks from overthinking

👇Check out our work presented at NeurIPs now! Early-exit networks aim at adjusting the computation effort according to the actual complexity of classifying each sample. We further encourage them to become gradually confident. More processing only for better prediction quality!

Our models struggle at generating high-quality uncertainty estimates, because they are not trained for doing that. Our paper at #NeurIPS2023 provided a simple solution. Visit us at the poster session #502 Great hall & Hall B1+B2 (Wed 13, 7:15-9.15 a.m. CST).

Check out our #NeurIPS2023 poster at the Workshop on Distribution Shifts on Friday (Room R06-R09)! The work with Dan Zhang and @eric_nalisnick explores divergence-based disagreement notions for predicting performance under distribution shift. Paper: arxiv.org/abs/2312.08033

👇Checkout our paper just accepted at ICLR2024. Adversarial supervision w. multi-denoising step unrolling makes Layout-to-Image diffusion models thrive. 🥳

Thanks RSIP Vision for featuring our #ICLR2024 paper "Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive"! Check out our project page with code & models 🤗 yumengli007.github.io/ALDM Joint work with Margret Keuper Dan Zhang Anna Khoreva at Bosch Center for Artificial Intelligence

VSTAR Generative Temporal Nursing for Longer Dynamic Video Synthesis Despite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content. They tend to

Check out our new work on Generative Temporal Nursing for Longer Dynamic Video Synthesis 👇 yumengli007.github.io/VSTAR/ Thanks AK for sharing!

Check out our recent work on improving video synthesis, very little extra effort for generating longer and much more dynamic videos using pretrained T2V models.

Check out our recent work on parameter efficient finetuning of LLMs and VLMs - ETHER, which was accepted at ICML Conference ! #ICML2024 github.com/mwbini/ether More info 👇

Things change all the time, and we cannot stop that. Check out our poster if you don’t want to keep retraining your deployed models!

🤗Code is out: github.com/boschresearch/… 🚀TL;DR: VSTAR generates longer videos with dynamic visual evolution in a single pass. No fine-tuning is needed! 🙌Check out our project page: yumengli007.github.io/VSTAR/ Bill Beluch Margret Keuper Dan Zhang Anna Khoreva Bosch Center for Artificial Intelligence ❤️

If this caught your attention, have a look at out paper and come talk to us during the NeurIPS poster session on Thu 12 Dec at 11:00 a.m. PST (East Exhibit Hall A-C #4505) Great collaboration with: @AlexTimans, @eric_nalisnick, Christian A. Naesseth, Dan Zhang, Tin Hadzi Veljkovic and others

🔥 Are you ever dissatisfied with the imprecise names in vision-language datasets? 🚀 At #NeurIPS2024, we introduce 𝐑𝐄𝐍𝐎𝐕𝐀𝐓𝐄, showing how better segmentation dataset names lead to 𝐛𝐞𝐭𝐭𝐞𝐫 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 & 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧. Let’s dive in! 🧵👇

👇checkout our work that greatly improves text annotations of segmentation and object detection datasets. #NeurIPS2024

NEWS: Chinese media tested ADAS in various scenarios, including highways & night driving. Tesla’s vision-based system outperformed emerging Chinese brands like Huawei & Xiaomi, as well as traditional automakers. Even with LiDAR, competitors’ ADAS performance lags behind Tesla.