no code implementations • 25 Apr 2024 • Zhijie Rao, Jingcai Guo, Xiaocheng Lu, Jingming Liang, Jie Zhang, Haozhao Wang, Kang Wei, Xiaofeng Cao
Zero-shot learning has consistently yielded remarkable progress via modeling nuanced one-to-one visual-attribute correlation.
no code implementations • 11 Apr 2024 • Yang Chen, Jingcai Guo, Tian He, Ling Wang
However, previous works focus on establishing the bridges between the known skeleton representation space and semantic descriptions space at the coarse-grained level for recognizing unknown action categories, ignoring the fine-grained alignment of these two spaces, resulting in suboptimal performance in distinguishing high-similarity action categories.
no code implementations • 11 Mar 2024 • Sikai Bai, Jie Zhang, Shuaicheng Li, Song Guo, Jingcai Guo, Jun Hou, Tao Han, Xiaocheng Lu
Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target domain) is absent from the decentralized training data (source domains).
1 code implementation • 31 Jan 2024 • Jingcai Guo, Zhijie Rao, Zhi Chen, Jingren Zhou, DaCheng Tao
To enrich the literature of this domain and provide a sound basis for its future development, in this paper, we present a broad review of recent advances for fine-grained analysis in ZSL.
no code implementations • 15 Dec 2023 • Jingcai Guo, Qihua Zhou, Ruibing Li, Xiaocheng Lu, Ziming Liu, Junyang Chen, Xin Xie, Jie Zhang
Then, to facilitate the generalization of local linearities, we construct a maximal margin geometry on the learned features by enforcing low-rank constraints on intra-class samples and high-rank constraints on inter-class samples, resulting in orthogonal subspaces for different classes and each subspace lies on a compact manifold.
no code implementations • 25 Nov 2023 • Ruibin Li, Jingcai Guo, Song Guo, Qihua Zhou, Jie Zhang
Specifically, we find that the very last few steps of the denoising (i. e., generation) process strongly correspond to the stylistic information of images, and based on this, we propose to augment the latent features of both the foreground and background images with Gaussians for a direct denoising-based harmonization.
no code implementations • 23 Nov 2023 • Zhijie Rao, Jingcai Guo, Xiaocheng Lu, Qihua Zhou, Jie Zhang, Kang Wei, Chenxin Li, Song Guo
In this paper, we propose a simple yet effective Attribute-Aware Representation Rectification framework for GZSL, dubbed $\mathbf{(AR)^{2}}$, to adaptively rectify the feature extractor to learn novel features while keeping original valuable features.
no code implementations • 2 Sep 2023 • Ziming Liu, Jingcai Guo, Xiaocheng Lu, Song Guo, Peiran Dong, Jiewei Zhang
That is, in the process of inferring unseen classes, global features represent the principal direction of the image in the feature space, while local features should maintain uniqueness within a certain range.
no code implementations • 4 Jul 2023 • Zhijie Rao, Jingcai Guo, Luyao Tang, Yue Huang, Xinghao Ding, Song Guo
In this paper, we introduce Semantic Reasoning with Compound Domains (SRCD) for Single-DGOD.
Ranked #2 on Robust Object Detection on DWD
no code implementations • 1 Jun 2023 • Ruibin Li, Qihua Zhou, Song Guo, Jie Zhang, Jingcai Guo, Xinyang Jiang, Yifei Shen, Zhenhua Han
Diffusion-based Generative Models (DGMs) have achieved unparalleled performance in synthesizing high-quality visual content, opening up the opportunity to improve image super-resolution (SR) tasks.
no code implementations • 19 May 2023 • Yingchun Wang, Jingcai Guo, Yi Liu, Song Guo, Weizhan Zhang, Xiangyong Cao, Qinghua Zheng
Based on the idea that in-distribution (ID) data with spurious features may have a lower experience risk, in this paper, we propose a novel Spurious Feature-targeted model Pruning framework, dubbed SFP, to automatically explore invariant substructures without referring to the above drawbacks.
no code implementations • 2 May 2023 • Jingcai Guo, Yuanyuan Xu, Wenchao Xu, Yufeng Zhan, Yuxia Sun, Song Guo
Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively.
no code implementations • 2 May 2023 • Jingcai Guo, Song Guo, Shiheng Ma, Yuxia Sun, Yuanyuan Xu
Previous works usually assume the malware families are known to the classifier in a close-set scenario, i. e., testing families are the subset or at most identical to training families.
no code implementations • 2 May 2023 • Xiaocheng Lu, Ziming Liu, Song Guo, Jingcai Guo, Fushuo Huo, Sikai Bai, Tao Han
Compositional Zero-shot Learning (CZSL) aims to recognize novel concepts composed of known knowledge without training samples.
no code implementations • 20 Mar 2023 • Fushuo Huo, Wenchao Xu, Jingcai Guo, Haozhao Wang, Yunfeng Fan, Song Guo
In this paper, we propose a novel Dual-prototype Self-augment and Refinement method (DSR) for NO-CL problem, which consists of two strategies: 1) Dual class prototypes: vanilla and high-dimensional prototypes are exploited to utilize the pre-trained information and obtain robust quasi-orthogonal representations rather than example buffers for both privacy preservation and memory reduction.
no code implementations • 9 Feb 2023 • Yingchun Wang, Jingcai Guo, Jie Zhang, Song Guo, Weizhan Zhang, Qinghua Zheng
Federated learning (FL) is an emerging technique that trains massive and geographically distributed edge data while maintaining privacy.
no code implementations • 9 Feb 2023 • Yingchun Wang, Jingcai Guo, Song Guo, Weizhan Zhang
Mixed-precision quantization mostly predetermines the model bit-width settings before actual training due to the non-differential bit-width sampling process, obtaining sub-optimal performance.
no code implementations • CVPR 2023 • Ziming Liu, Song Guo, Xiaocheng Lu, Jingcai Guo, Jiewei Zhang, Yue Zeng, Fushuo Huo
Recent studies usually approach multi-label zero-shot learning (MLZSL) with visual-semantic mapping on spatial-class correlation, which can be computationally costly, and worse still, fails to capture fine-grained class-specific semantics.
no code implementations • 19 Dec 2022 • Yingchun Wang, Jingcai Guo, Song Guo, Weizhan Zhang, Jie Zhang
Recent studies show that even highly biased dense networks contain an unbiased substructure that can achieve better out-of-distribution (OOD) generalization than the original model.
no code implementations • 7 Dec 2022 • Yingchun Wang, Song Guo, Jingcai Guo, Weizhan Zhang, Yida Xu, Jie Zhang, Yi Liu
Extensive experiments based on small Cifar-10 and large-scaled ImageNet demonstrate that our method can obtain sparser networks with great generalization performance while providing quantified reliability for the pruned model.
no code implementations • 19 Nov 2022 • Fushuo Huo, Wenchao Xu, Song Guo, Jingcai Guo, Haozhao Wang, Ziming Liu, Xiaocheng Lu
Open-World Compositional Zero-shot Learning (OW-CZSL) aims to recognize novel compositions of state and object primitives in images with no priors on the compositional space, which induces a tremendously large output space containing all possible state-object compositions.
1 code implementation • CVPR 2023 • Xiaocheng Lu, Ziming Liu, Song Guo, Jingcai Guo
Existing methods either learn the combined state-object representation, challenging the generalization of unseen compositions, or design two classifiers to identify state and object separately from image features, ignoring the intrinsic relationship between them.
no code implementations • 15 Nov 2022 • Qihua Zhou, Ruibin Li, Song Guo, Peiran Dong, Yi Liu, Jingcai Guo, Zhenda Xu
Recent years have witnessed the dramatic growth of Internet video traffic, where the video bitstreams are often compressed and delivered in low quality to fit the streamer's uplink bandwidth.
no code implementations • 21 Aug 2022 • Jingcai Guo, Song Guo, Jie Zhang, Ziming Liu
Concretely, we maintain an edge-agnostic hidden model in the cloud server to estimate a less-accurate while direction-aware inversion of the global model.
no code implementations • 7 Mar 2022 • Ziming Liu, Song Guo, Jingcai Guo, Yuanyuan Xu, Fushuo Huo
We argue that disregarding the connection between major and minor classes, i. e., correspond to the global and local information, respectively, is the cause of the problem.
no code implementations • 24 Jun 2021 • Xueyang Tang, Song Guo, Jingcai Guo
The prevalent personalized federated learning (PFL) usually pursues a trade-off between personalization and generalization by maintaining a shared global model to guide the training process of local models.
no code implementations • 12 Apr 2021 • Jingcai Guo
In ZSL, the common practice is to train a mapping function between the visual and semantic feature spaces with labeled seen class examples.
no code implementations • 30 Apr 2020 • Jingcai Guo, Song Guo
One common practice in zero-shot learning is to train a projection between the visual and semantic feature spaces with labeled seen classes examples.
no code implementations • 12 Apr 2019 • Jingcai Guo, Song Guo
It considers the Alignment of Manifold Structures by Semantic Feature Expansion.
no code implementations • 12 Apr 2019 • Shiheng Ma, Jingcai Guo, Song Guo, Minyi Guo
Our approach employs the inception backbone network to capture rich features of traffic distribution on the whole area.
1 code implementation • 12 Apr 2019 • Jingcai Guo, Shiheng Ma, Song Guo
Specifically, we propose the local aware (LA) and global aware (GA) attention to deal with LR features in unequal manners, which can highlight the high-frequency components and discriminate each feature from LR images in the local and the global views, respectively.
no code implementations • 30 Mar 2019 • Jingcai Guo, Song Guo
In order to deal with this issue, we propose an Exclusivity Enhanced (EE) unsupervised feature learning approach to improve the conventional AE.
no code implementations • 30 Mar 2019 • Jingcai Guo, Song Guo
To the best of our knowledge, our work is the first to consider the adaptive adjustment of semantic FS in ZSR.