1 code implementation • 17 May 2024 • Mushui Liu, Jun Dan, Ziqian Lu, Yunlong Yu, Yingming Li, Xi Li
In this paper, we propose CM-UNet, comprising a CNN-based encoder for extracting local image features and a Mamba-based decoder for aggregating and integrating global information, facilitating efficient semantic segmentation of remote sensing images.
no code implementations • 19 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.
1 code implementation • 6 Dec 2023 • Mushui Liu, Weijie He, Ziqian Lu, Yunlong Yu
Prompt learning is a powerful technique for transferring Vision-Language Models (VLMs) such as CLIP to downstream tasks.
no code implementations • 17 Jul 2023 • Hao Chen, Yonghan Dong, Zheming Lu, Yunlong Yu, Yingming Li, Jungong Han, Zhongfei Zhang
Few-Shot Segmentation (FSS) aims to segment the novel class images with a few annotated samples.
no code implementations • 6 Jun 2023 • Huanzhang Dou, Pengyi Zhang, Wei Su, Yunlong Yu, Xi Li
Towards this goal, MetaGait injects meta-knowledge, which could guide the model to perceive sample-specific properties, into the calibration network of the attention mechanism to improve the adaptiveness from the omni-scale, omni-dimension, and omni-process perspectives.
no code implementations • CVPR 2023 • Huanzhang Dou, Pengyi Zhang, Wei Su, Yunlong Yu, Yining Lin, Xi Li
Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns.
no code implementations • 6 Jun 2023 • Yike Yuan, Xinghe Fu, Yunlong Yu, Xi Li
In this paper, we propose a simple yet effective transformer framework for self-supervised learning called DenseDINO to learn dense visual representations.
no code implementations • 11 Mar 2023 • Hao Chen, Yunlong Yu, Yonghan Dong, Zheming Lu, Yingming Li, Zhongfei Zhang
Few-Shot Segmentation (FSS) is challenging for limited support images and large intra-class appearance discrepancies.
no code implementations • 21 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.
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.
no code implementations • 26 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.
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.
no code implementations • 20 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.
no code implementations • 21 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.
no code implementations • 6 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).
no code implementations • 26 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.
no code implementations • 27 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.
no code implementations • 27 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.