15 citations · 135 across the 41 of their papers we have counts for
41 papers
NoVo: Norm Voting off Hallucinations with Attention Heads in Large Language Models
Zheng Yi Ho, Siyuan Liang, Sen Zhang +2
Hallucinations in Large Language Models (LLMs) remain a major obstacle, particularly in high-stakes applications where factual accuracy is critical. While representation editing an…
Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases
Ziyi Zhang, Sen Zhang, Yibing Zhan +3
Bridging the gap between diffusion models and human preferences is crucial for their integration into practical generative workflows. While optimizing downstream reward models has…
TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation
Xin Lin, Chong Shi, Yibing Zhan +3
Dynamic scene graph generation (SGG) focuses on detecting objects in a video and determining their pairwise relationships. Existing dynamic SGG methods usually suffer from several…
Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation
Changtong Zan, Liang Ding, Li Shen +4
Zero-shot translation (ZST), which is generally based on a multilingual neural machine translation model, aims to translate between unseen language pairs in training data. The comm…
Free-Form Composition Networks for Egocentric Action Recognition
Haoran Wang, Qinghua Cheng, Baosheng Yu +4
Egocentric action recognition is gaining significant attention in the field of human action recognition. In this paper, we address data scarcity issue in egocentric action recognit…
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…
Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking
Qingyue Wang, Liang Ding, Yanan Cao +5
Zero-shot transfer learning for Dialogue State Tracking (DST) helps to handle a variety of task-oriented dialogue domains without the cost of collecting in-domain data. Existing wo…
Noise-Resistant Multimodal Transformer for Emotion Recognition
Yuanyuan Liu, Haoyu Zhang, Yibing Zhan +4
Multimodal emotion recognition identifies human emotions from various data modalities like video, text, and audio. However, we found that this task can be easily affected by noisy…
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…
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Chao Xue, Wei Liu, Shuai Xie +25
Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to t…
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…
DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes
Shenghao Hao, Peiyuan Liu, Yibing Zhan +5
Cross-view multi-object tracking aims to link objects between frames and camera views with substantial overlaps. Although cross-view multi-object tracking has received increased at…
Original or Translated? On the Use of Parallel Data for Translation Quality Estimation
Baopu Qiu, Liang Ding, Di Wu +3
Machine Translation Quality Estimation (QE) is the task of evaluating translation output in the absence of human-written references. Due to the scarcity of human-labeled QE data, p…
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…
Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
Yuanyuan Liu, Wenbin Wang, Yibing Zhan +3
Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets…
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
Yu Cao, Dianqi Li, Meng Fang +4
We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while main…
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…
Vega-MT: The JD Explore Academy Translation System for WMT22
Changtong Zan, Keqin Peng, Liang Ding +9
We describe the JD Explore Academy's submission of the WMT 2022 shared general translation task. We participated in all high-resource tracks and one medium-resource track, includin…
Improving Adversarial Robustness via Mutual Information Estimation
Dawei Zhou, Nannan Wang, Xinbo Gao +4
Deep neural networks (DNNs) are found to be vulnerable to adversarial noise. They are typically misled by adversarial samples to make wrong predictions. To alleviate this negative…
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection
Gaoang Wang, Yibing Zhan, Xinchao Wang +2
Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables ef…
Learning Graph Neural Networks for Image Style Transfer
Yongcheng Jing, Yining Mao, Yiding Yang +4
State-of-the-art parametric and non-parametric style transfer approaches are prone to either distorted local style patterns due to global statistics alignment, or unpleasing artifa…
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.…
Pluralistic Image Completion with Probabilistic Mixture-of-Experts
Xiaobo Xia, Wenhao Yang, Jie Ren +4
Pluralistic image completion focuses on generating both visually realistic and diverse results for image completion. Prior methods enjoy the empirical successes of this task. Howev…
HL-Net: Heterophily Learning Network for Scene Graph Generation
Xin Lin, Changxing Ding, Yibing Zhan +2
Scene graph generation (SGG) aims to detect objects and predict their pairwise relationships within an image. Current SGG methods typically utilize graph neural networks (GNNs) to…