Search Results for author: Zhong Ji

Found 33 papers, 10 papers with code

VIP: Versatile Image Outpainting Empowered by Multimodal Large Language Model

no code implementations3 Jun 2024 Jinze Yang, Haoran Wang, Zining Zhu, Chenglong Liu, Meng Wymond Wu, Zeke Xie, Zhong Ji, Jungong Han, Mingming Sun

In this paper, we focus on resolving the problem of image outpainting, which aims to extrapolate the surrounding parts given the center contents of an image.

Image Outpainting Language Modelling +1

Raformer: Redundancy-Aware Transformer for Video Wire Inpainting

1 code implementation24 Apr 2024 Zhong Ji, Yimu Su, Yan Zhang, Jiacheng Hou, Yanwei Pang, Jungong Han

Video Wire Inpainting (VWI) is a prominent application in video inpainting, aimed at flawlessly removing wires in films or TV series, offering significant time and labor savings compared to manual frame-by-frame removal.

Video Inpainting

SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior

no code implementations29 Mar 2024 Zhongrui Yu, Haoran Wang, Jinze Yang, Hanzhang Wang, Zeke Xie, Yunfeng Cai, Jiale Cao, Zhong Ji, Mingming Sun

To tackle this problem, we propose a novel approach that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model along with complementary multi-modal data.

Autonomous Driving Neural Rendering +1

NTK-Guided Few-Shot Class Incremental Learning

no code implementations19 Mar 2024 Jingren Liu, Zhong Ji, Yanwei Pang, Yunlong Yu

While anti-amnesia FSCIL learners often excel in incremental sessions, they tend to prioritize mitigating knowledge attrition over harnessing the model's potential for knowledge acquisition.

Few-Shot Class-Incremental Learning Incremental Learning +1

Hierarchical Matching and Reasoning for Multi-Query Image Retrieval

1 code implementation26 Jun 2023 Zhong Ji, Zhihao LI, Yan Zhang, Haoran Wang, Yanwei Pang, Xuelong Li

Afterwards, the VR module is developed to excavate the potential semantic correlations among multiple region-query pairs, which further explores the high-level reasoning similarity.

Image Retrieval Retrieval

USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text Retrieval

1 code implementation17 Jan 2023 Yan Zhang, Zhong Ji, Di Wang, Yanwei Pang, Xuelong Li

(2) It limits the scale of negative sample pairs by employing the mini-batch based end-to-end training mechanism.

Contrastive Learning Retrieval +3

Conformal Loss-Controlling Prediction

no code implementations6 Jan 2023 Di Wang, Ping Wang, Zhong Ji, Xiaojun Yang, Hongyue Li

Conformal prediction is a learning framework controlling prediction coverage of prediction sets, which can be built on any learning algorithm for point prediction.

Conformal Prediction Weather Forecasting

Asymmetric Cross-Scale Alignment for Text-Based Person Search

1 code implementation26 Nov 2022 Zhong Ji, Junhua Hu, Deyin Liu, Lin Yuanbo Wu, Ye Zhao

To implement this task, one needs to extract multi-scale features from both image and text domains, and then perform the cross-modal alignment.

Person Search Retrieval +2

Learning from Students: Online Contrastive Distillation Network for General Continual Learning

1 code implementation Conference 2022 Jin Li, Zhong Ji, Gang Wang, Qiang Wang, Feng Gao

The goal of General Continual Learning (GCL) is to preserve learned knowledge and learn new knowledge with constant memory from an infinite data stream where task boundaries are blurry.

Continual Learning

CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval

no code implementations21 Aug 2022 Haoran Wang, Dongliang He, Wenhao Wu, Boyang xia, Min Yang, Fu Li, Yunlong Yu, Zhong Ji, Errui Ding, Jingdong Wang

We introduce dynamic dictionaries for both modalities to enlarge the scale of image-text pairs, and diversity-sensitiveness is achieved by adaptive negative pair weighting.

Clustering Contrastive Learning +4

Memorizing Complementation Network for Few-Shot Class-Incremental Learning

no code implementations11 Aug 2022 Zhong Ji, Zhishen Hou, Xiyao Liu, Yanwei Pang, Xuelong Li

Few-shot Class-Incremental Learning (FSCIL) aims at learning new concepts continually with only a few samples, which is prone to suffer the catastrophic forgetting and overfitting problems.

Few-Shot Class-Incremental Learning Incremental Learning +1

Boosting Video-Text Retrieval with Explicit High-Level Semantics

no code implementations8 Aug 2022 Haoran Wang, Di Xu, Dongliang He, Fu Li, Zhong Ji, Jungong Han, Errui Ding

Video-text retrieval (VTR) is an attractive yet challenging task for multi-modal understanding, which aims to search for relevant video (text) given a query (video).

Retrieval Text Retrieval +3

Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition

no code implementations3 Sep 2021 Xiyao Liu, Zhong Ji, Yanwei Pang, Zhongfei Zhang

However, the target domain is absolutely unknown during the training on the source domain, which results in lacking directed guidance for target tasks.

cross-domain few-shot learning Weakly-Supervised Object Localization

Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning

no code implementations3 Sep 2021 Zhong Ji, Zhishen Hou, Xiyao Liu, Yanwei Pang, Jungong Han

Semantic information provides intra-class consistency and inter-class discriminability beyond visual concepts, which has been employed in Few-Shot Learning (FSL) to achieve further gains.

Attribute Few-Shot Learning

Complementary Calibration: Boosting General Continual Learning with Collaborative Distillation and Self-Supervision

1 code implementation3 Sep 2021 Zhong Ji, Jin Li, Qiang Wang, Zhongfei Zhang

Furthermore, we explore a collaborative self-supervision idea to leverage pretext tasks and supervised contrastive learning for addressing the feature deviation problem by learning complete and discriminative features for all classes.

Continual Learning Contrastive Learning +2

Step-Wise Hierarchical Alignment Network for Image-Text Matching

no code implementations11 Jun 2021 Zhong Ji, Kexin Chen, Haoran Wang

Image-text matching plays a central role in bridging the semantic gap between vision and language.

Image-text matching Text Matching

Consensus-Aware Visual-Semantic Embedding for Image-Text Matching

1 code implementation ECCV 2020 Haoran Wang, Ying Zhang, Zhong Ji, Yanwei Pang, Lin Ma

In this paper, we propose a Consensus-aware Visual-Semantic Embedding (CVSE) model to incorporate the consensus information, namely the commonsense knowledge shared between both modalities, into image-text matching.

Image Captioning Image-text matching +3

GTNet: Generative Transfer Network for Zero-Shot Object Detection

1 code implementation19 Jan 2020 Shizhen Zhao, Changxin Gao, Yuanjie Shao, Lerenhan Li, Changqian Yu, Zhong Ji, Nong Sang

FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively.

Generative Adversarial Network Object +3

Episode-based Prototype Generating Network for Zero-Shot Learning

1 code implementation CVPR 2020 Yunlong Yu, Zhong Ji, Zhongfei Zhang, Jungong Han

We introduce a simple yet effective episode-based training framework for zero-shot learning (ZSL), where the learning system requires to recognize unseen classes given only the corresponding class semantics.

Zero-Shot Learning

A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification

no code implementations26 Aug 2019 Zhong Ji, Xuejie Yu, Yunlong Yu, Yanwei Pang, Zhongfei Zhang

Towards alleviating the class imbalance issue in ZSC, we propose a sample-balanced training process to encourage all training classes to contribute equally to the learned model.

General Classification Image Classification +2

Saliency-Guided Attention Network for Image-Sentence Matching

no code implementations ICCV 2019 Zhong Ji, Haoran Wang, Jungong Han, Yanwei Pang

Concretely, the saliency detector provides the visual saliency information as the guidance for the two attention modules.


Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning

1 code implementation NeurIPS 2018 Yunlong Yu, Zhong Ji, Yanwei Fu, Jichang Guo, Yanwei Pang, Zhongfei (Mark) Zhang

Zero-Shot Learning (ZSL) is generally achieved via aligning the semantic relationships between the visual features and the corresponding class semantic descriptions.

General Classification Multi-class Classification +2

Bi-Adversarial Auto-Encoder for Zero-Shot Learning

no code implementations20 Nov 2018 Yunlong Yu, Zhong Ji, Yanwei Pang, Jichang Guo, Zhongfei Zhang, Fei Wu

Existing generative Zero-Shot Learning (ZSL) methods only consider the unidirectional alignment from the class semantics to the visual features while ignoring the alignment from the visual features to the class semantics, which fails to construct the visual-semantic interactions well.

Decoder Zero-Shot Learning

Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning

no code implementations21 May 2018 Yunlong Yu, Zhong Ji, Yanwei Fu, Jichang Guo, Yanwei Pang, Zhongfei Zhang

To this end, we propose a novel stacked semantics-guided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions.

General Classification Multi-class Classification +2

Attribute-Guided Network for Cross-Modal Zero-Shot Hashing

no code implementations6 Feb 2018 Zhong Ji, Yuxin Sun, Yunlong Yu, Yanwei Pang, Jungong Han

To address the Cross-Modal Zero-Shot Hashing (CMZSH) retrieval task, we propose a novel Attribute-Guided Network (AgNet), which can perform not only IBIR, but also Text-Based Image Retrieval (TBIR).

Attribute Cross-Modal Retrieval +3

Zero-Shot Learning via Latent Space Encoding

no code implementations26 Dec 2017 Yunlong Yu, Zhong Ji, Jichang Guo, Zhongfei, Zhang

Instead of requiring a projection function to transfer information across different modalities like most previous work, LSE per- forms the interactions of different modalities via a feature aware latent space, which is learned in an implicit way.

Decoder Retrieval +1

Video Summarization with Attention-Based Encoder-Decoder Networks

no code implementations31 Aug 2017 Zhong Ji, Kailin Xiong, Yanwei Pang, Xuelong. Li

This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, the output is a keyshot sequence.

Ranked #4 on Video Summarization on TvSum (using extra training data)

Decoder Supervised Video Summarization

Query-Aware Sparse Coding for Multi-Video Summarization

no code implementations13 Jul 2017 Zhong Ji, Yaru Ma, Yanwei Pang, Xuelong. Li

Given the explosive growth of online videos, it is becoming increasingly important to relieve the tedious work of browsing and managing the video content of interest.

Video Summarization

Semantic Softmax Loss for Zero-Shot Learning

no code implementations22 May 2017 Zhong Ji, Yunxin Sun, Yulong Yu, Jichang Guo, Yanwei Pang

However, the visual features and the class semantic descriptors locate in different structural spaces, a linear or bilinear model can not capture the semantic interactions between different modalities well.

Classification General Classification +3

Transductive Zero-Shot Learning with a Self-training dictionary approach

no code implementations27 Mar 2017 Yunlong Yu, Zhong Ji, Xi Li, Jichang Guo, Zhongfei Zhang, Haibin Ling, Fei Wu

As an important and challenging problem in computer vision, zero-shot learning (ZSL) aims at automatically recognizing the instances from unseen object classes without training data.

Transductive Learning Transfer Learning +1

Transductive Zero-Shot Learning with Adaptive Structural Embedding

no code implementations27 Mar 2017 Yunlong Yu, Zhong Ji, Jichang Guo, Yanwei Pang

Two fundamental challenges in it are visual-semantic embedding and domain adaptation in cross-modality learning and unseen class prediction steps, respectively.

Domain Adaptation Zero-Shot Learning

Zero-Shot Learning with Multi-Battery Factor Analysis

no code implementations30 Jun 2016 Zhong Ji, Yuzhong Xie, Yanwei Pang, Lei Chen, Zhongfei Zhang

Zero-shot learning (ZSL) extends the conventional image classification technique to a more challenging situation where the test image categories are not seen in the training samples.

Image Classification Zero-Shot Learning

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