380 citations · 848 across the 35 of their papers we have counts for
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Efficient Quantization Strategies for Latent Diffusion Models
Yuewei Yang, Xiaoliang Dai, Jialiang Wang +2
Latent Diffusion Models (LDMs) capture the dynamic evolution of latent variables over time, blending patterns and multimodality in a generative system. Despite the proficiency of L…
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
Yunyang Xiong, Bala Varadarajan, Lemeng Wu +9
Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high…
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
Xiaoliang Dai, Ji Hou, Chih-Yao Ma +23
Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face chal…
Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence
Yonggan Fu, Yuecheng Li, Chenghui Li +4
Real-time and robust photorealistic avatars for telepresence in AR/VR have been highly desired for enabling immersive photorealistic telepresence. However, there still exists one k…
Trainable Projected Gradient Method for Robust Fine-tuning
Junjiao Tian, Xiaoliang Dai, Chih-Yao Ma +3
Recent studies on transfer learning have shown that selectively fine-tuning a subset of layers or customizing different learning rates for each layer can greatly improve robustness…
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors
Ji Hou, Xiaoliang Dai, Zijian He +2
Current popular backbones in computer vision, such as Vision Transformers (ViT) and ResNets are trained to perceive the world from 2D images. However, to more effectively understan…
Pruning Compact ConvNets for Efficient Inference
Sayan Ghosh, Karthik Prasad, Xiaoliang Dai +4
Neural network pruning is frequently used to compress over-parameterized networks by large amounts, while incurring only marginal drops in generalization performance. However, the…