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Works183 from public data
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- Transfer of Preformed Three‐Dimensional Photonic Crystals onto Dye‐Sensitized Solar Cells151
This technique allows the incorporation of photonic crystals onto independently processed devices, in which the transferred films have high optical quality.
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- Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline126
This work constructs a large-scale dataset named "RGB-D-D", in which the high-frequency component adaptively decomposed from RGB image to guide the depth map SR, and provides a fast depth map super-resolution (FDSR) baseline.
- A parallel down-up fusion network for salient object detection in optical remote sensing images108
A novel Parallel Down-up Fusion network (PDF-Net) for SOD in optical RSIs, which takes full advantage of the in-path low- and high-level features and cross-path multi-resolution features to distinguish diversely scaled salient objects and suppress the cluttered backgrounds.
- Body Joint Guided 3-D Deep Convolutional Descriptors for Action Recognition78
This paper proposes not to directly use the activations of fully connected layers of a 3-D CNN as the video feature, but to use selective convolutional layer activations to form a discriminative descriptor for video.
- Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning74
A debiasing loss is proposed to pursue response consistency between seen and unseen predictions, and a semantic-motivated instance decoder strengthens accurate cross-domain interactions between the matched pair for semantic-related instance adaption, en-couraging the generation of unambiguous visual representations.
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- Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow65
This paper embeds a correction layer in skipconnection and leverage the appearance flows in different layers to pre-correct the image features and proposes a parallel complementary structure that effectively reduces the burden of the decoder by separating content reconstruction and structure correction.
- Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition65
An image classification framework by leveraging the non-negative sparse coding, correlation constrained low rank and sparse matrix decomposition technique (CCLR-Sc+SPM), which achieves or outperforms the state-of-the-art results on several benchmarks.
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- Causality-based CTR prediction using graph neural networks47
A causality-based CTR prediction model in the GNNs framework (Causal-GNN) integrating representations of feature graph, user graph and ad graph in the context of online advertising is developed.
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- Image classification using spatial pyramid robust sparse coding47
A new image classification method by spatial pyramid robust sparse coding (SP-RSC), which tries to find the maximum likelihood estimation solution by alternatively optimizing over the codebook and local feature coding parameters, hence is more robust to outliers than traditional sparse coding based methods.
- CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation46
A new algorithm: compatible momentum contrast with Topology Preservation, dubbed CTP is proposed, which absorbs the knowledge of the current and previous-task models to flexibly update the modal feature and transfers the knowledge of embedding across tasks while preserving the flexibility of feature adjustment.
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- Image classification using Harr-like transformation of local features with coding residuals35
This paper proposes a novel image classification method using Harr-like transformation of local features with additional consideration of coding residuals to reduce the information loss during the local feature encoding process.
- BundleNet: Learning with Noisy Label via Sample Correlations34
BundleNet is proposed, a framework of sequential structure for deep neural networks to handle the label noise, and it is proved that the bundle-module performs a form of regularization, which is similar to dropout as regularization during training, which endows the BundleNet with strong robustness to the label Noise.
- Camera Network Based Person Re-identification by Leveraging Spatial-Temporal Constraint and Multiple Cameras Relations34
Quantitative experiments conducted on TMin and CamNeT datasets have shown that the proposed probabilistic method achieves a better performance to the novel CNPR problem.
- AMC: Adaptive Multi-expert Collaborative Network for Text-guided Image Retrieval32
Extensive experiments demonstrate that due to benefits from the image-text composing representation produced by an adaptive multi-expert collaboration mechanism, AMC has better retrieval performance and zero-shot generalization ability than the state-of-the-art method while keeping the lightweight model and fast retrieval speed.
- Set-label modeling and deep metric learning on person re-identification32
A novel method named Set-Label Model (SLM) is proposed to improve the performance of person re-identification under the multi-shot setting and a deep non-linear metric learning (DeepML) approach is developed based on Neighborhood Component Analysis and Deep Belief Network.
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- Image-level classification by hierarchical structure learning with visual and semantic similarities30
A novel image classification method is proposed by automatically learning the image-level hierarchical structure (ILHS) using both visual and semantic similarities of images to generate new representations.
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- Image classification by search with explicitly and implicitly semantic representations28
This paper proposes a novel image classification method by explicitly and implicitly representing the images with searching strategy and evaluates the effectiveness of the proposed EISR on the Scene-15 dataset, the MIT-Indoor datasets, the UIUC-Sports dataset and the PASCAL VOC 2007 dataset.
- In-situ evaluation the fluctuation of hypochlorous acid in acute liver injury mice models with a mitochondria-targeted NIR ratiometric fluorescent probe27
The novel BODIPY-based NIR ratiometric fluorescent probe Mito-BS makes it possible to achieve the non-invasive in-situ diagnosis of ALI through in mice, and provides a feasible strategy for early diagnosis and drug therapy of ALI and its complications.
- Object categorization in sub-semantic space27
This paper proposes a novel object categorization method by using the sub-semantic space based image representation, which is not only more semantically meaningful than exemplar based representation but also more reliable and resistant to noise than traditional semantic spacebased image representation.
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- Face image super-resolution through locality-induced support regression26
A novel face image super-resolution method named Locality-induced Support Regression (LiSR), which defines the support in the LR or HR patch manifold space by incorporating the locality constraint, which can well preserve the manifold structure of the training set.
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- A review of recent advance of ship detection in single-channel SAR images24
Aiming at the problems of the large amount of calculation, multi-scale and densely docked ship detection in single-channel SAR images, an improved deep learning-based detection algorithm is proposed, which has achieved excellent performance on the SAR ship detection dataset (SSDD).
- Downregulation of long non‑coding RNA LINC‑PINT serves as a diagnostic and prognostic biomarker in patients with non‑small cell lung cancer24
The results of the present study suggest that lncRNA LINC-PINT may be an independent diagnostic and prognostic biomarker in NSCLC.
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- A Unsupervised Person Re-identification Method Using Model Based Representation and Ranking24
This paper proposes a new unsupervised person re-identification method featured by its competitive accuracy and high efficiency, which is based on model based person image representation and ranking and can achieve over 20% matching rate at Rank 1 on the challenging VIPeR dataset.
- Boosted random contextual semantic space based representation for visual recognition23
The proposed BRCSS can alleviate the semantic gap by using semantic space based image representation using boosted random contextual semantic spaces and is able to jointly consider the visual and semantic information of images.
- Beyond visual features: A weak semantic image representation using exemplar classifiers for classification23
This paper proposes a simple but effective image representation for image classification, which is denoted as the responses to a set of exemplar image classifiers, which are learned using SVM algorithm to distinguish the image from others in different classes.
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- Distributed image understanding with semantic dictionary and semantic expansion22
This paper proposes a semantic dictionary to describe the images on the level of semantic, which characterizes the probability distribution between visual appearances and semantic concepts, and the learning procedure of semantic dictionary is formulated into a minimization optimization problem.
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- An Impedance Sensing Platform for Monitoring Heterogeneous Connectivity and Diagnostics in Lab-on-a-Chip Systems20
An impedance sensing platform is reported that can provide fast detection of a single physical connection in timescales of milliseconds and can be scaled up to support multiple channels or applied to other heterogeneously integrated systems that require real-time monitoring and diagnostics of multiconnectivity systems.
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- Neural Networks Integer Computation: Quantizing Convolutional Neural Networks of Inference and Training for Object Detection in Embedded Systems17
A hybrid precision posttraining quantization (H-PTQ) method that utilizes hybrid precision to perform forward inference, thereby mitigating quantization errors caused by nonlinear activation functions is introduced.
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- Hierarchical deep semantic representation for visual categorization15
This paper proposes Hierarchical Deep Semantic Representation (H-DSR), a hierarchical framework which combines semantic context modeling with visual features and verifies the effectiveness of H- DSR for visual categorization on two publicly available datasets.
- Image classification using boosted local features with random orientation and location selection15
A novel boosted local features method with random orientation and location selection is proposed to combine the local feature extraction and encoding with classifier training into a unified framework and gradually improve the image classification performance.
- PSVMA+: Exploring Multi-Granularity Semantic-Visual Adaption for Generalized Zero-Shot Learning14
A multi-granularity progressive semantic-visual mutual adaption (PSVMA+) network, where sufficient visual elements across granularity levels can be gathered to remedy the granularity inconsistency, and experimental results demonstrate that PSVMA+ consistently outperforms state-of-the-art methods.
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- Image Classification Using Spatial Pyramid Coding and Visual Word Reweighting14
An improved BoW representation using spatial pyramid coding (SPC) and visual word reweighting is presented and a relaxed but simple solution for semantic embedding into visual words is proposed.
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- Joint image representation and classification in random semantic spaces13
A novel algorithm is proposed by combining image representation and classification in the random semantic spaces by combining local features with the sparse coding technique and using the encoding parameters for raw image representation to achieve better performances.
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- Birds of a feather flock together: Visual representation with scale and class consistency11
This paper proposes a scale and class consistent local-feature encoding method for image representation, which is achieved through the dense extraction of local features in different scale spaces, and the subsequent learning of the encoding parameters.
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- Laplacian affine sparse coding with tilt and orientation consistency for image classification10
A Laplacian affine sparse coding algorithm which combines the tilt and orientations of affine local features as well as the dependency among local features is proposed, and tilt and orientation smooth constraints are added into the objective function of sparse coding.
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- GGRNet: Global Graph Reasoning Network for Salient Object Detection in Optical Remote Sensing Images9
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- Automatic face annotation in TV series by video/script alignment8
This paper considers the temporal order relationship of the faces and names over the whole video and directly align two heterogeneous sequences to annotate the faces by video/script alignment.
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- Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning6
This work proposes AENet, which endows semantic information into the visual prompt to distill semantic-enhanced prompt for visual representation enrichment, enabling effective knowledge transfer for ZSL.
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- Undo the codebook bias by linear transformation for visual applications6
An alternative optimization algorithm is proposed to jointly search for the optimal linear transformation matrixes and the encoding parameters and show the effectiveness of the proposed method on image classification experimental results.
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- Image Classification Using Spatial Difference Descriptor Under Spatial Pyramid Matching Framework5
This work exploits a novel descriptor called spatial difference to concatenate the histograms of bag-of-visual words model under spatial pyramid matching framework and conducts image classification experiments on several public datasets to demonstrate the effectiveness of the proposed scheme.
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- Image-regulated graph topic model for cross-media topic detection5
A novel Image-regulated Graph Topic Model (IGTM) is proposed, which combines cross-media data together in the modeling process and can achieve higher quality underlying topics as image relationships could serve as weakly-supervised information for topic modeling.
- Socio-mobile landmark recognition using local features with adaptive region selection5
This work proposes a novel socio-mobile visual recognition method using local features with adaptive region selection based local features for sodo-mobile landmark recognition that achieves the state-of-the-art performance on the Stanford mobile visual search dataset and the San Francisco landmark dataset.
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- Beyond visual word ambiguity: Weighted local feature encoding with governing region4
A novel image representation method is proposed by going one step beyond visual word ambiguity and consider the governing regions of visual words and a weighted feature sign search algorithm is proposed to solve the problem.
- Beyond bag of words4
A novel image representation method in the sub-semantic space based representation of images that is not only more semantically meaningful but also more reliable and resistant to noise.
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- Undoing the codebook bias by linear transformation with sparsity and F-norm constraints for image classification3
The sparsity constraints and the F-norm of the transformation matrix are used in this paper to propose an alternative optimization algorithm to jointly search for the optimal linear transformation matrixes and the encoding parameters.
- Person Re-identification by Smooth Metric Learning3
This paper proposes to replace hinge loss function with the logistic loss function, which transforms LMNN to a smooth unconstrained convex optimization problem easily solved with gradient descent algorithm and designs a stochastic sampling scheme to accelerate the optimization process of the above problem with randomly selected training samples.
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- Quantum Interference-Inspired Who-What-Where Composite-Semantics Instance Search for Story Videos2
A Quantum Interference Partial Decomposition (QIPD) method is proposed to model the diverse influences of semantic overlap from 2W to 3W INS, which incorporates two core modules, i.e., semantic interference and temporal interference.
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- Object Categorization Using Local Feature Context2
This paper proposes a novel object categorization method by considering the local feature context, which uses theLocal feature context to represent this position’s visual information and is also invariant to rotation and scale change.
- Concept-Specific Visual Vocabulary Construction for Object Categorization2
This work proposes a novel method to construct a concept-specific visual vocabulary by extracting various visual features from local image patches, and cluster them separately according to different features to generate an initial vocabulary.
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- User-Driven Sports Video Customization System for Mobile Devices1
A user-driven sports video customization system, aiming to provide interesting video clips for mobile users according to their personalized preferences, and the proposed extended-hidden Markov model extended-HMM is proved to be able to recognize the clock time precisely.
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- Saliency-Based Deformable Model for Pedestrian Detection1
In SBDM method, it is presented that, besides the local features, the saliency in the image provides important constraints that are not yet well utilized, and a probabilistic framework is proposed to model the relationship between Saliency detection and the feature Deformable Model via a Bayesian rule to detect pedestrians in the still image.
- Fine-Grained Image Classification Using Color Exemplar Classifiers1
This paper proposes a novel fine-grained image classification method by using color exemplar classifiers, which first decompose it into multiple color channels to take advantage of the color information.
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- Excitation Friction-Reducing Method Considering Low Power Consumption and High Operational Stability–
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- Passive energy-Saving Design Optimization of Buildings Based on Watershed and U-Net Model Algorithms–
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- Making a Thorough Inquiry about Studying and Learning Higher Mathematics in Higher Vocational School–
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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