1 code implementation • 1 Apr 2025 • Jie Ma, Zhitao Gao, Qi Chai, Jun Liu, Pinghui Wang, Jing Tao, Zhou Su
The first stage expands the test space with greater diversity, while the second enables a refined robustness evaluation across rare, frequent, and overall question distributions.
Audio-visual Question Answering
Audio-Visual Question Answering (AVQA)
+4
1 code implementation • 5 Sep 2024 • Jie Ma, Zhitao Gao, Qi Chai, Wangchun Sun, Pinghui Wang, Hongbin Pei, Jing Tao, Lingyun Song, Jun Liu, Chen Zhang, Lizhen Cui
Furthermore, the integration experiments with various LLMs on the mentioned datasets highlight the flexibility of DoG.
1 code implementation • 18 Aug 2024 • Nuo Xu, Pinghui Wang, Junzhou Zhao, Feiyang Sun, Lin Lan, Jing Tao, Li Pan, Xiaohong Guan
On the other hand, D-LADAN presents a novel momentum-updated memory mechanism to dynamically sense the posterior similarity between law articles (or charges) and a weighted GDO to adaptively capture the distinctions for revising the inductive bias caused by the data imbalance problem.
1 code implementation • 11 Apr 2024 • Tao Duan, Junzhou Zhao, Shuo Zhang, Jing Tao, Pinghui Wang
To address this problem, we propose a novel method, i. e., Key-Value sequence Early Co-classification (KVEC), which leverages both inner- and inter-correlations of items in a tangled key-value sequence through key correlation and value correlation to learn a better sequence representation.
no code implementations • 2 Mar 2024 • Yuya Sasaki, Jing Tao, Yulong Wang
Motivated by the empirical observation of power-law distributions in the credits (e. g., "likes") of viral social media posts, we introduce a high-dimensional tail index regression model and propose methods for estimation and inference of its parameters.
no code implementations • 27 Oct 2023 • Ziquan Zhu, Jing Tao, Shuihua Wang, Xin Zhang, Yudong Zhang
Five indexes are selected in this paper, which are accuracy, sensitivity, precision, F1-score, and specificity.
no code implementations • 27 Feb 2023 • Shuo Zhang, Junzhou Zhao, Pinghui Wang, Tianxiang Wang, Zi Liang, Jing Tao, Yi Huang, Junlan Feng
To cope with this problem, we explore to improve multi-action dialog policy learning with explicit and implicit turn-level user feedback received for historical predictions (i. e., logged user feedback) that are cost-efficient to collect and faithful to real-world scenarios.
no code implementations • 26 Jan 2023 • Runze Lei, Pinghui Wang, Junzhou Zhao, Lin Lan, Jing Tao, Chao Deng, Junlan Feng, Xidian Wang, Xiaohong Guan
In this work, we propose a novel FL framework for graph data, FedCog, to efficiently handle coupled graphs that are a kind of distributed graph data, but widely exist in a variety of real-world applications such as mobile carriers' communication networks and banks' transaction networks.
no code implementations • 7 Sep 2020 • Yang Ning, Sida Peng, Jing Tao
This paper proposes a doubly robust two-stage semiparametric difference-in-difference estimator for estimating heterogeneous treatment effects with high-dimensional data.
1 code implementation • NeurIPS 2020 • Lin Lan, Pinghui Wang, Xuefeng Du, Kaikai Song, Jing Tao, Xiaohong Guan
We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representative nodes for training a classifier.
2 code implementations • 23 May 2019 • Nuo Xu, Pinghui Wang, Long Chen, Jing Tao, Junzhou Zhao
To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs.
no code implementations • CVPR 2018 • Minghan Li, Qi Xie, Qian Zhao, Wei Wei, Shuhang Gu, Jing Tao, Deyu Meng
Based on such understanding, we specifically formulate both characteristics into a multiscale convolutional sparse coding (MS-CSC) model for the video rain streak removal task.