2 citations · 2 across the 4 of their papers we have counts for
5 papers
ClaimDiff-RL: Fine-Grained Caption Reinforcement Learning through Visual Claim Comparison
Tianle Li, Xuyang Shen, Yan Ma +7
Long-form image captioning exposes a reward granularity problem in RL: captions are judged as whole sequences, while the important errors occur at the level of individual visual cl…
Towards Scalable Pre-training of Visual Tokenizers for Generation
Jingfeng Yao, Yuda Song, Yucong Zhou +1
The quality of the latent space in visual tokenizers (e.g., VAEs) is crucial for modern generative models. However, the standard reconstruction-based training paradigm produces a l…
Learning Low-Rank Representations for Model Compression
Zezhou Zhu, Yucong Zhou, Zhao Zhong
Vector Quantization (VQ) is an appealing model compression method to obtain a tiny model with less accuracy loss. While methods to obtain better codebooks and codes under fixed clu…
Learning specialized activation functions with the Piecewise Linear Unit
Yucong Zhou, Zezhou Zhu, Zhao Zhong
The choice of activation functions is crucial for modern deep neural networks. Popular hand-designed activation functions like Rectified Linear Unit(ReLU) and its variants show pro…
FixNorm: Dissecting Weight Decay for Training Deep Neural Networks
Yucong Zhou, Yunxiao Sun, Zhao Zhong
Weight decay is a widely used technique for training Deep Neural Networks(DNN). It greatly affects generalization performance but the underlying mechanisms are not fully understood…