15 citations · 57 across the 14 of their papers we have counts for
14 papers
Parameter Efficient Multi-task Model Fusion with Partial Linearization
Anke Tang, Li Shen, Yong Luo +5
Large pre-trained models have enabled significant advances in machine learning and served as foundation components. Model fusion methods, such as task arithmetic, have been proven…
On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling
Chuang Liu, Yibing Zhan, Baosheng Yu +4
A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology. However, current…
Improving Heterogeneous Model Reuse by Density Estimation
Anke Tang, Yong Luo, Han Hu +5
This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assum…
Token Contrast for Weakly-Supervised Semantic Segmentation
Lixiang Ru, Heliang Zheng, Yibing Zhan +1
Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the local structure…
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE
Qihuang Zhong, Liang Ding, Keqin Peng +5
This technical report briefly describes our JDExplore d-team's submission Vega v1 on the General Language Understanding Evaluation (GLUE) leaderboard, where GLUE is a collection of…
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
Qihuang Zhong, Liang Ding, Yibing Zhan +11
This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard. SuperGLUE is more challenging than the widely used general language…
Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models
Qihuang Zhong, Liang Ding, Li Shen +4
Fine-tuning large pretrained language models on a limited training corpus usually suffers from poor generalization. Prior works show that the recently-proposed sharpness-aware mini…
Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition
Mengya Han, Yibing Zhan, Yong Luo +4
Few-shot visual recognition refers to recognize novel visual concepts from a few labeled instances. Many few-shot visual recognition methods adopt the metric-based meta-learning pa…
CLNode: Curriculum Learning for Node Classification
Xiaowen Wei, Xiuwen Gong, Yibing Zhan +3
Node classification is a fundamental graph-based task that aims to predict the classes of unlabeled nodes, for which Graph Neural Networks (GNNs) are the state-of-the-art methods.…
Robust Weight Perturbation for Adversarial Training
Chaojian Yu, Bo Han, Mingming Gong +4
Overfitting widely exists in adversarial robust training of deep networks. An effective remedy is adversarial weight perturbation, which injects the worst-case weight perturbation…
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
Chuang Liu, Yibing Zhan, Jia Wu +5
Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation. As an essential component of the archite…
Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers
Lixiang Ru, Yibing Zhan, Baosheng Yu +1
Weakly-supervised semantic segmentation (WSSS) with image-level labels is an important and challenging task. Due to the high training efficiency, end-to-end solutions for WSSS have…
Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling
Lixiang Ru, Bo Du, Yibing Zhan +1
Weakly-Supervised Semantic Segmentation (WSSS) methods with image-level labels generally train a classification network to generate the Class Activation Maps (CAMs) as the initial…
Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation
Chao Chen, Yibing Zhan, Baosheng Yu +3
Scene Graph Generation (SGG) aims to build a structured representation of a scene using objects and pairwise relationships, which benefits downstream tasks. However, current SGG me…