arXiv subjects

Yunjie Xu

Publications and source records attributed to Yunjie Xu.

2 recordsLinked to original sources

arXiv2026

RT-NeRV: Rethinking Hybrid Neural Representations for Video via Residual Tokenization

Neural Representations for Videos(NeRV) have emerged as a promising paradigm for video compression by representing videos as compact neural networks with efficient decoding. Hybrid NeRV methods further improve reconstruction quality through content adaptive embeddings, but still struggle to preserve fine details at low bitrates. A key limitation is that shallow residual support in formation, although highly beneficial for reconstruction, is costly to transmit in its continuous form and is therefore underutilized. In this paper, we rethink hybrid NeRV and present RT-NeRV, a residual tokenization framework for hybrid neural video representations. The core idea is to discretize shallow residual features and inter-frame residual cues into compact residual tokens, allowing informative reconstruction support to be transmitted efficiently and exploited by the decoder. To this end, we design a residual tokenizer together with a residual-aware codebook learning strategy that improves token utilization and stabilizes training. RT-NeRV can be readily integrated into modern hybrid NeRV hosts, consistently enhancing detail preservation, reconstruction quality, and bitrate quality trade-offs. Extensive experiments on video regression and related restoration tasks show that RT-NeRV outperforms strong hybrid NeRV baselines and remains competitive with recent INR based video compression methods. These results demonstrate that residual tokenization is an effective and complementary direction for advancing hybrid neural video representations

cs.CV↗

arXiv2021

TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion

The prosperity of mobile and financial technologies has bred and expanded various kinds of financial products to a broader scope of people, which contributes to advocating financial inclusion. It has non-trivial social benefits of diminishing financial inequality. However, the technical challenges in individual financial risk evaluation caused by the distinct characteristic distribution and limited credit history of new users, as well as the inexperience of newly-entered companies in handling complex data and obtaining accurate labels, impede further promoting financial inclusion. To tackle these challenges, this paper develops a novel transfer learning algorithm (i.e., TransBoost) that combines the merits of tree-based models and kernel methods. The TransBoost is designed with a parallel tree structure and efficient weights updating mechanism with theoretical guarantee, which enables it to excel in tackling real-world data with high dimensional features and sparsity in $O(n)$ time complexity. We conduct extensive experiments on two public datasets and a unique large-scale dataset from Tencent Mobile Payment. The results show that the TransBoost outperforms other state-of-the-art benchmark transfer learning algorithms in terms of prediction accuracy with superior efficiency, shows stronger robustness to data sparsity, and provides meaningful model interpretation. Besides, given a financial risk level, the TransBoost enables financial service providers to serve the largest number of users including those who would otherwise be excluded by other algorithms. That is, the TransBoost improves financial inclusion.

cs.LG↗