1 citations · 1 across the 5 of their papers we have counts for
5 papers
Scaling Up Temporal Domain Generalization via Temporal Experts Averaging
Aoming Liu, Kevin Miller, Venkatesh Saligrama +4
Temporal Domain Generalization (TDG) aims to generalize across temporal distribution shifts, e.g., lexical change over time. Prior work often addresses this by predicting future mo…
Fine-grained Token Allocation Via Operation Pruning for Efficient MLLMs
Aoming Liu, Reuben Tan, Boqing Gong +1
Token reduction accelerates Multimodal Large Language Models (MLLMs) by reducing excessive tokens, but overlooks structural redundancy differences, where critical and redundant mod…
BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning
Shengao Wang, Arjun Chandra, Aoming Liu +2
Human infants rapidly develop visual reasoning skills from minimal input, suggesting that developmentally inspired pretraining could significantly enhance the efficiency of vision-…
Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization
Siqi Wang, Aoming Liu, Bryan A. Plummer
Methods addressing Learning with Noisy Labels (LNL) and multi-source Domain Generalization (DG) use training techniques to improve downstream task performance in the presence of la…
PanoFree: Tuning-Free Holistic Multi-view Image Generation with Cross-view Self-Guidance
Aoming Liu, Zhong Li, Zhang Chen +3
Immersive scene generation, notably panorama creation, benefits significantly from the adaptation of large pre-trained text-to-image (T2I) models for multi-view image generation. D…