9 citations · 24 across the 8 of their papers we have counts for
8 papers
Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?
Cheng-En Wu, Yu Tian, Haichao Yu +4
Vision-language models such as CLIP learn a generic text-image embedding from large-scale training data. A vision-language model can be adapted to a new classification task through…
Exploring the Role of Audio in Video Captioning
Yuhan Shen, Linjie Yang, Longyin Wen +3
Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for…
Boosted Dynamic Neural Networks
Haichao Yu, Haoxiang Li, Gang Hua +2
Early-exiting dynamic neural networks (EDNN), as one type of dynamic neural networks, has been widely studied recently. A typical EDNN has multiple prediction heads at different la…
Dropout Prediction Uncertainty Estimation Using Neuron Activation Strength
Haichao Yu, Zhe Chen, Dong Lin +2
Dropout has been commonly used to quantify prediction uncertainty, i.e, the variations of model predictions on a given input example. However, using dropout in practice can be expe…
Is In-Domain Data Really Needed? A Pilot Study on Cross-Domain Calibration for Network Quantization
Haichao Yu, Linjie Yang, Humphrey Shi
Post-training quantization methods use a set of calibration data to compute quantization ranges for network parameters and activations. The calibration data usually comes from the…
High-Resolution Deep Image Matting
Haichao Yu, Ning Xu, Zilong Huang +2
Image matting is a key technique for image and video editing and composition. Conventionally, deep learning approaches take the whole input image and an associated trimap to infer…
FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation
Hanchao Yu, Shanhui Sun, Haichao Yu +4
Motion estimation of cardiac MRI videos is crucial for the evaluation of human heart anatomy and function. Recent researches show promising results with deep learning-based methods…
Any-Precision Deep Neural Networks
Haichao Yu, Haoxiang Li, Honghui Shi +2
We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference. The s…