1 citations · 1 across the 4 of their papers we have counts for
10 papers · 1 filter
Improved Baselines with Representation Autoencoders
Jaskirat Singh, Boyang Zheng, Zongze Wu +3
Representation Autoencoders (RAE) replace traditional VAE with pretrained vision encoders. In this paper, we systematically investigate several design choices and find three insigh…
RepFusion: Leveraging Multimodal Priors for Denoising in Representation Space
Xichen Pan, Aashu Singh, Satya Narayan Shukla +3
Large language models (LLMs) are widely used in text-to-image (T2I) systems, but they are typically limited to text encoding, while denoising is handled by newly trained generative…
SIMS-V: Simulated Instruction-Tuning for Spatial Video Understanding
Ellis Brown, Arijit Ray, Ranjay Krishna +3
Despite impressive high-level video comprehension, multimodal language models struggle with spatial reasoning across time and space. While current spatial training approaches rely…
Cambrian-S: Towards Spatial Supersensing in Video
Shusheng Yang, Jihan Yang, Pinzhi Huang +12
We argue that progress in true multimodal intelligence calls for a shift from reactive, task-driven systems and brute-force long context towards a broader paradigm of supersensing.…
Benchmark Designers Should "Train on the Test Set" to Expose Exploitable Non-Visual Shortcuts
Ellis Brown, Jihan Yang, Shusheng Yang +2
Robust benchmarks are crucial for evaluating Multimodal Large Language Models (MLLMs). Yet we find that models can ace many multimodal benchmarks without strong visual understandin…
Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis
Bingda Tang, Boyang Zheng, Xichen Pan +2
This paper does not describe a new method; instead, it provides a thorough exploration of an important yet understudied design space related to recent advances in text-to-image syn…