35 citations · 50 across the 5 of their papers we have counts for
6 papers
CodePMP: Scalable Preference Model Pretraining for Large Language Model Reasoning
Huimu Yu, Xing Wu, Haotian Xu +2
Large language models (LLMs) have made significant progress in natural language understanding and generation, driven by scalable pretraining and advanced finetuning. However, enhan…
Contrastive Vision-Language Pre-training with Limited Resources
Quan Cui, Boyan Zhou, Yu Guo +4
Pioneering dual-encoder pre-training works (e.g., CLIP and ALIGN) have revealed the potential of aligning multi-modal representations with contrastive learning. However, these work…
Person-in-Context Synthesiswith Compositional Structural Space
Weidong Yin, Ziwei Liu, Leonid Sigal
Despite significant progress, controlled generation of complex images with interacting people remains difficult. Existing layout generation methods fall short of synthesizing reali…
Instance-level Facial Attributes Transfer with Geometry-Aware Flow
Weidong Yin, Ziwei Liu, Chen Change Loy
We address the problem of instance-level facial attribute transfer without paired training data, e.g. faithfully transferring the exact mustache from a source face to a target face…
Image Generation from Layout
Bo Zhao, Lili Meng, Weidong Yin +1
Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core…
Semi-Latent GAN: Learning to generate and modify facial images from attributes
Weidong Yin, Yanwei Fu, Leonid Sigal +1
Generating and manipulating human facial images using high-level attributal controls are important and interesting problems. The models proposed in previous work can solve one of t…