Search Results for author: Yibing Zhan

Found 46 papers, 21 papers with code

SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification

1 code implementation26 Nov 2023 Lei Wang, Yibing Zhan, Leilei Ma, Dapeng Tao, Liang Ding, Chen Gong

The "splice" in our method is two-fold: 1) Each mixed image is a splice of several downsampled images in the form of a grid, where the semantics of images attending to mixing are blended without object deficiencies for alleviating co-occurred bias; 2) We splice mixed images and the original mini-batch to form a new SpliceMixed mini-batch, which allows an image with different scales to contribute to training together.

Data Augmentation Multi-Label Image Classification

Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes

no code implementations21 Nov 2023 Chuang Liu, Wenhang Yu, Kuang Gao, Xueqi Ma, Yibing Zhan, Jia Wu, Bo Du, Wenbin Hu

Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning.

Graph Representation Learning

Parameter Efficient Multi-task Model Fusion with Partial Linearization

no code implementations7 Oct 2023 Anke Tang, Li Shen, Yong Luo, Yibing Zhan, Han Hu, Bo Du, Yixin Chen, DaCheng Tao

We demonstrate that our partial linearization technique enables a more effective fusion of multiple tasks into a single model, outperforming standard adapter tuning and task arithmetic alone.

Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation

1 code implementation28 Sep 2023 Changtong Zan, Liang Ding, Li Shen, Yibin Lei, Yibing Zhan, Weifeng Liu, DaCheng Tao

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.

Machine Translation Navigate +1

Chasing Consistency in Text-to-3D Generation from a Single Image

no code implementations7 Sep 2023 Yichen Ouyang, Wenhao Chai, Jiayi Ye, Dapeng Tao, Yibing Zhan, Gaoang Wang

In light of the above issues, we present Consist3D, a three-stage framework Chasing for semantic-, geometric-, and saturation-Consistent Text-to-3D generation from a single image, in which the first two stages aim to learn parameterized consistency tokens, and the last stage is for optimization.

Text to 3D

Free-Form Composition Networks for Egocentric Action Recognition

no code implementations13 Jul 2023 Haoran Wang, Qinghua Cheng, Baosheng Yu, Yibing Zhan, Dapeng Tao, Liang Ding, Haibin Ling

We evaluated our method on three popular egocentric action recognition datasets, Something-Something V2, H2O, and EPIC-KITCHENS-100, and the experimental results demonstrate the effectiveness of the proposed method for handling data scarcity problems, including long-tailed and few-shot egocentric action recognition.

Action Recognition Temporal Action Localization

On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling

1 code implementation22 Jun 2023 Chuang Liu, Yibing Zhan, Baosheng Yu, Liu Liu, Bo Du, Wenbin Hu, Tongliang Liu

A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology.

Graph Classification Representation Learning

Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking

no code implementations1 Jun 2023 Qingyue Wang, Liang Ding, Yanan Cao, Yibing Zhan, Zheng Lin, Shi Wang, DaCheng Tao, Li Guo

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.

Dialogue State Tracking Transfer Learning

Noise-Resistant Multimodal Transformer for Emotion Recognition

no code implementations4 May 2023 Yuanyuan Liu, Haoyu Zhang, Yibing Zhan, Zijing Chen, Guanghao Yin, Lin Wei, Zhe Chen

To this end, we present a novel paradigm that attempts to extract noise-resistant features in its pipeline and introduces a noise-aware learning scheme to effectively improve the robustness of multimodal emotion understanding.

Multimodal Emotion Recognition

Token Contrast for Weakly-Supervised Semantic Segmentation

1 code implementation CVPR 2023 Lixiang Ru, Heliang Zheng, Yibing Zhan, Bo Du

Secondly, to further differentiate the low-confidence regions in CAM, we devised a Class Token Contrast module (CTC) inspired by the fact that class tokens in ViT can capture high-level semantics.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE

no code implementations18 Feb 2023 Qihuang Zhong, Liang Ding, Keqin Peng, Juhua Liu, Bo Du, Li Shen, Yibing Zhan, DaCheng Tao

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 nine natural language understanding tasks, including question answering, linguistic acceptability, sentiment analysis, text similarity, paraphrase detection, and natural language inference.

Contrastive Learning Denoising +11

DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes

1 code implementation15 Feb 2023 Shenghao Hao, Peiyuan Liu, Yibing Zhan, Kaixun Jin, Zuozhu Liu, Mingli Song, Jenq-Neng Hwang, Gaoang Wang

Although cross-view multi-object tracking has received increased attention in recent years, existing datasets still have several issues, including 1) missing real-world scenarios, 2) lacking diverse scenes, 3) owning a limited number of tracks, 4) comprising only static cameras, and 5) lacking standard benchmarks, which hinder the investigation and comparison of cross-view tracking methods.

Multi-Object Tracking object-detection +1

Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples

no code implementations ICCV 2023 Xiaobo Xia, Bo Han, Yibing Zhan, Jun Yu, Mingming Gong, Chen Gong, Tongliang Liu

As selected data have high discrepancies in probabilities, the divergence of two networks can be maintained by training on such data.

Learning with noisy labels

Original or Translated? On the Use of Parallel Data for Translation Quality Estimation

no code implementations20 Dec 2022 Baopu Qiu, Liang Ding, Di wu, Lin Shang, Yibing Zhan, DaCheng Tao

Machine Translation Quality Estimation (QE) is the task of evaluating translation output in the absence of human-written references.

Data Augmentation Machine Translation +1

Pose-disentangled Contrastive Learning for Self-supervised Facial Representation

1 code implementation CVPR 2023 Yuanyuan Liu, Wenbin Wang, Yibing Zhan, Shaoze Feng, Kejun Liu, Zhe Chen

Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets heavily.

Contrastive Learning Data Augmentation +5

TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack

1 code implementation27 Oct 2022 Yu Cao, Dianqi Li, Meng Fang, Tianyi Zhou, Jun Gao, Yibing Zhan, DaCheng Tao

We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while maintaining gold answers.

Adversarial Attack Question Answering

Vega-MT: The JD Explore Academy Translation System for WMT22

1 code implementation20 Sep 2022 Changtong Zan, Keqin Peng, Liang Ding, Baopu Qiu, Boan Liu, Shwai He, Qingyu Lu, Zheng Zhang, Chuang Liu, Weifeng Liu, Yibing Zhan, DaCheng Tao

As for model sizes, we scale the Transformer-Big up to the extremely large model that owns nearly 4. 7 Billion parameters, to fully enhance the model capacity for our Vega-MT.

Data Augmentation Machine Translation +1

Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition

no code implementations7 Sep 2022 Mengya Han, Yibing Zhan, Yong Luo, Bo Du, Han Hu, Yonggang Wen, DaCheng Tao

To address the above issues, we propose a novel metric-based meta-learning framework termed instance-adaptive class representation learning network (ICRL-Net) for few-shot visual recognition.

Meta-Learning Representation Learning

Improving Adversarial Robustness via Mutual Information Estimation

1 code implementation25 Jul 2022 Dawei Zhou, Nannan Wang, Xinbo Gao, Bo Han, Xiaoyu Wang, Yibing Zhan, Tongliang Liu

To alleviate this negative effect, in this paper, we investigate the dependence between outputs of the target model and input adversarial samples from the perspective of information theory, and propose an adversarial defense method.

Adversarial Defense Adversarial Robustness +1

Learning Graph Neural Networks for Image Style Transfer

no code implementations24 Jul 2022 Yongcheng Jing, Yining Mao, Yiding Yang, Yibing Zhan, Mingli Song, Xinchao Wang, DaCheng Tao

To this end, we develop an elaborated GNN model with content and style local patches as the graph vertices.

Image Stylization

Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks

no code implementations18 Jul 2022 Chuang Liu, Xueqi Ma, Yibing Zhan, Liang Ding, Dapeng Tao, Bo Du, Wenbin Hu, Danilo Mandic

However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where significant redundancy exists.

Node Classification

CLNode: Curriculum Learning for Node Classification

1 code implementation15 Jun 2022 Xiaowen Wei, Xiuwen Gong, Yibing Zhan, Bo Du, Yong Luo, Wenbin Hu

Experimental results on real-world networks demonstrate that CLNode is a general framework that can be combined with various GNNs to improve their accuracy and robustness.

Classification Node Classification

Pluralistic Image Completion with Probabilistic Mixture-of-Experts

no code implementations18 May 2022 Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, Tongliang Liu

Second, the constraints for diversity are designed to be task-agnostic, which causes the constraints to not work well.

Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities

1 code implementation15 Apr 2022 Chuang Liu, Yibing Zhan, Jia Wu, Chang Li, Bo Du, Wenbin Hu, Tongliang Liu, DaCheng Tao

Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation.

Graph Classification Graph Generation

BatchFormerV2: Exploring Sample Relationships for Dense Representation Learning

1 code implementation4 Apr 2022 Zhi Hou, Baosheng Yu, Chaoyue Wang, Yibing Zhan, DaCheng Tao

Specifically, when applying the proposed module, it employs a two-stream pipeline during training, i. e., either with or without a BatchFormerV2 module, where the batchformer stream can be removed for testing.

Image Classification object-detection +3

Exploring High-Order Structure for Robust Graph Structure Learning

no code implementations22 Mar 2022 Guangqian Yang, Yibing Zhan, Jinlong Li, Baosheng Yu, Liu Liu, Fengxiang He

In this paper, we analyze the adversarial attack on graphs from the perspective of feature smoothness which further contributes to an efficient new adversarial defensive algorithm for GNNs.

Adversarial Attack Graph structure learning +1

Where Does the Performance Improvement Come From? -- A Reproducibility Concern about Image-Text Retrieval

1 code implementation8 Mar 2022 Jun Rao, Fei Wang, Liang Ding, Shuhan Qi, Yibing Zhan, Weifeng Liu, DaCheng Tao

In contrast to previous works, we focus on the reproducibility of the approaches and the examination of the elements that lead to improved performance by pretrained and nonpretrained models in retrieving images and text.

Information Retrieval Retrieval +1

Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers

1 code implementation CVPR 2022 Lixiang Ru, Yibing Zhan, Baosheng Yu, Bo Du

Motivated by the inherent consistency between the self-attention in Transformers and the semantic affinity, we propose an Affinity from Attention (AFA) module to learn semantic affinity from the multi-head self-attention (MHSA) in Transformers.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

A Review of Affective Generation Models

no code implementations22 Feb 2022 Guangtao Nie, Yibing Zhan

Therefore, we propose to provide a comprehensive review of affective generation models, as models are most commonly leveraged to affect others' emotional states.

Hyper-relationship Learning Network for Scene Graph Generation

no code implementations15 Feb 2022 Yibing Zhan, Zhi Chen, Jun Yu, Baosheng Yu, DaCheng Tao, Yong Luo

As a result, HLN significantly improves the performance of scene graph generation by integrating and reasoning from object interactions, relationship interactions, and transitive inference of hyper-relationships.

Graph Attention Graph Generation +1

Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Pooling

1 code implementation10 Feb 2022 Lixiang Ru, Bo Du, Yibing Zhan, Chen Wu

In the visual words learning module, we counter the first problem by enforcing the classification network to learn fine-grained visual word labels so that more object extents could be discovered.

Classification Weakly supervised Semantic Segmentation +1

Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation

1 code implementation18 Jan 2022 Chao Chen, Yibing Zhan, Baosheng Yu, Liu Liu, Yong Luo, Bo Du

To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation.

Graph Generation Unbiased Scene Graph Generation

SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks

no code implementations22 Dec 2021 Weigang Lu, Yibing Zhan, Binbin Lin, Ziyu Guan, Liu Liu, Baosheng Yu, Wei Zhao, Yaming Yang, DaCheng Tao

In this paper, we conduct theoretical and experimental analysis to explore the fundamental causes of performance degradation in deep GCNs: over-smoothing and gradient vanishing have a mutually reinforcing effect that causes the performance to deteriorate more quickly in deep GCNs.

Link Prediction Node Classification

Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels

no code implementations NeurIPS 2021 Sheng Wan, Yibing Zhan, Liu Liu, Baosheng Yu, Shirui Pan, Chen Gong

Essentially, our CGPN can enhance the learning performance of GNNs under extremely limited labels by contrastively propagating the limited labels to the entire graph.

Graph Attention Node Classification +1

Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples

no code implementations29 Sep 2021 Xiaobo Xia, Bo Han, Yibing Zhan, Jun Yu, Mingming Gong, Chen Gong, Tongliang Liu

The sample selection approach is popular in learning with noisy labels, which tends to select potentially clean data out of noisy data for robust training.

Learning with noisy labels

Expression Snippet Transformer for Robust Video-based Facial Expression Recognition

no code implementations17 Sep 2021 Yuanyuan Liu, Wenbin Wang, Chuanxu Feng, Haoyu Zhang, Zhe Chen, Yibing Zhan

To this end, we propose to decompose each video into a series of expression snippets, each of which contains a small number of facial movements, and attempt to augment the Transformer's ability for modeling intra-snippet and inter-snippet visual relations, respectively, obtaining the Expression snippet Transformer (EST).

Dynamic Facial Expression Recognition Facial Expression Recognition +1

Graph Pattern Loss based Diversified Attention Network for Cross-Modal Retrieval

no code implementations25 Jun 2021 Xueying Chen, Rong Zhang, Yibing Zhan

In this paper, we propose a Graph Pattern Loss based Diversified Attention Network(GPLDAN) for unsupervised cross-modal retrieval to deeply analyze correlations among representations.

Cross-Modal Retrieval Retrieval

Not All Operations Contribute Equally: Hierarchical Operation-Adaptive Predictor for Neural Architecture Search

no code implementations ICCV 2021 Ziye Chen, Yibing Zhan, Baosheng Yu, Mingming Gong, Bo Du

Despite their efficiency, current graph-based predictors treat all operations equally, resulting in biased topological knowledge of cell architectures.

Neural Architecture Search

Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal Hashing

1 code implementation25 Dec 2020 Jun Yu, Hao Zhou, Yibing Zhan, DaCheng Tao

Essentially, DGCPN addresses the inaccurate similarity problem by exploring and exploiting the data's intrinsic relationships in a graph.

Quantization Retrieval

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