7 citations · 16 across the 7 of their papers we have counts for
7 papers
AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation
Shengkun Tang, Yaqing Wang, Caiwen Ding +3
Diffusion models achieve great success in generating diverse and high-fidelity images, yet their widespread application, especially in real-time scenarios, is hampered by their inh…
Teach LLMs to Personalize -- An Approach inspired by Writing Education
Cheng Li, Mingyang Zhang, Qiaozhu Mei +4
Personalized text generation is an emerging research area that has attracted much attention in recent years. Most studies in this direction focus on a particular domain by designin…
Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization
Junru Wu, Yi Liang, Feng Han +3
Self-supervised pre-training recently demonstrates success on large-scale multimodal data, and state-of-the-art contrastive learning methods often enforce the feature consistency f…
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model
Shengkun Tang, Yaqing Wang, Zhenglun Kong +6
Large-scale Transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with incre…
Self-supervised Domain Adaptation in Crowd Counting
Pha Nguyen, Thanh-Dat Truong, Miaoqing Huang +3
Self-training crowd counting has not been attentively explored though it is one of the important challenges in computer vision. In practice, the fully supervised methods usually re…
Transition-based Abstract Meaning Representation Parsing with Contextual Embeddings
Yichao Liang
The ability to understand and generate languages sets human cognition apart from other known life forms'. We study a way of combing two of the most successful routes to meaning of…
Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)
YI Liang, Shuai Zhao, Bo Cheng +2
Few-shot relation learning refers to infer facts for relations with a limited number of observed triples. Existing metric-learning methods for this problem mostly neglect entity in…