3 citations · 6 across the 17 of their papers we have counts for
5 papers · 1 filter
Efficient-vDiT: Efficient Video Diffusion Transformers With Attention Tile
Hangliang Ding, Dacheng Li, Runlong Su +4
Despite the promise of synthesizing high-fidelity videos, Diffusion Transformers (DiTs) with 3D full attention suffer from expensive inference due to the complexity of attention co…
SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning
Junkai Chen, Zhijie Deng, Kening Zheng +6
As Multimodal Large Language Models (MLLMs) develop, their potential security issues have become increasingly prominent. Machine Unlearning (MU), as an effective strategy for forge…
Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models
Zijun Chen, Wenbo Hu, Guande He +3
Multimodal large language models (MLLMs) combine visual and textual data for tasks such as image captioning and visual question answering. Proper uncertainty calibration is crucial…
Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads
Siqi Kou, Jiachun Jin, Zhihong Liu +6
We introduce Orthus, an autoregressive (AR) transformer that excels in generating images given textual prompts, answering questions based on visual inputs, and even crafting length…
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference
Siqi Kou, Lei Gan, Dequan Wang +2
Diffusion models have impressive image generation capability, but low-quality generations still exist, and their identification remains challenging due to the lack of a proper samp…