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- A Bottom-Up Clustering Approach to Unsupervised Person Re-Identification589
The experimental results demonstrate that the bottom-up clustering approach to jointly optimize a convolutional neural network and the relationship among the individual samples is not only superior to state-of-the-art unsupervised re-ID approaches, but also performs favorably than competing transfer learning and semi-supervised learning methods.
- Post-Training Quantization on Diffusion Models376
This work accelerates generation of diffusion model (DM) generation from the perspective of compressing the noise estimation network and devise a DM-specific PTQ method, which especially targets the unique multi-time-step structure of DMs.
- Style Aggregated Network for Facial Landmark Detection339
This work proposes a style-aggregated approach to deal with the large intrinsic variance of image styles for facial landmark detection and demonstrates to perform well when compared with state-of-the-art algorithms on benchmark datasets AFLW and 300-W.
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- Person Re-identification via Recurrent Feature Aggregation253
This work shows that a progressive/sequential fusion framework based on long short term memory (LSTM) network aggregates the frame-wise human region representation at each time stamp and yields a sequence level human feature representation.
- Towards a Unified Middle Modality Learning for Visible-Infrared Person Re-Identification220
A non-linear middle modality generator (MMG) is proposed, which helps to reduce the modality discrepancy in visible-infrared person re-identification (VI-ReID), and amiddle modality network (MMN) is presented to further enhance the discrimination and richness of features in an explicit manner.
- Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition213
Experimental results show that the proposed FDRL method consistently achieves higher recognition accuracy than several state-of-the-art methods, which clearly highlights the benefit of feature decomposition and reconstruction for classifying expressions.
- Segmenting Objects in Day and Night: Edge-Conditioned CNN for Thermal Image Semantic Segmentation163
This article elaborately design a gated featurewise transform layer in EC-CNN to adaptively incorporate edge prior knowledge and introduces a new benchmark data set named “Segmenting Objects in Day and night” (SODA) for comprehensive evaluations in thermal image semantic segmentation.
- Graph self-representation method for unsupervised feature selection159
A new unsupervised feature selection by integrating a subspace learning method into a new feature selection method (i.e., Locality Preserving Projection) and adding a graph regularization term into the resulting feature selection model to simultaneously conduct feature selection and subspaceLearning.
- Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes146
This paper proposes a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes, which achieves a good trade-off between accuracy and speed.
- Cross-Modal Attention Network for Temporal Inconsistent Audio-Visual Event Localization119
Inspired by human system which puts different focuses at specific locations, time segments and media while performing multi-modality perception, this work provides an attention-based method to simulate such process and achieves state-of-the-art performance.
- GestureGAN for Hand Gesture-to-Gesture Translation in the Wild99
Extensive experiments demonstrate that the proposed GestureGAN achieves state-of-the-art performance on the unconstrained hand gesture-to-gesture translation task and the generated images are in high-quality and are photo-realistic, allowing them to be used as data augmentation to improve the performance of a hand gesture classifier.
- Few-shot Medical Image Segmentation with Cycle-resemblance Attention96
A novel self-supervised few-shot medical image segmentation network is proposed and a novel Cycle-Resemblance Attention (CRA) module is introduced to fully leverage the pixel-wise relation between query and support medical images.
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- Audio-Visual Event Localization via Recursive Fusion by Joint Co-Attention77
A novel joint attention mechanism with multi-modal fusion methods for audio-visual event localization and a concise yet valid architecture that effectively learns representations from multiple modalities in a joint manner is presented.
- Deep Disturbance-Disentangled Learning for Facial Expression Recognition74
This paper proposes a novel Deep Disturbance-disentangled Learning (DDL) method for FER that is capable of simultaneously and explicitly disentangling multiple disturbing factors by taking advantage of multi-task learning and adversarial transfer learning.
- A fast face detection method via convolutional neural network73
Experimental results on several popular face detection datasets show the efficiency and the effectiveness of the proposed fast face detection method based on discriminative complete features extracted by an elaborately designed convolutional neural network.
- Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression73
A novel cascaded cropping regression (CCR) method to perform image cropping by learning the knowledge from professional photographers is proposed, which improves the convergence speed of the cascaded method, which directly uses random-ferns regressors.
- Stacking of doxorubicin on folic acid-targeted multiwalled carbon nanotubes for in vivo chemotherapy of tumors72
The developed FA-modified MWCNTs with an unconventionally high DOX loading boosted in vivo anti-tumor efficacy, and the lower systemic toxicity may be utilized for tumor therapy upon clinical translation.
- Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes71
A novel Stage-aware Feature Alignment Network (SFANet) based on the encoder-decoder structure for real-time semantic segmentation of street scenes is presented and an auxiliary training strategy is developed to explicitly alleviate the multi-scale object problem without bringing additional computational costs during the inference phase.
- Deep Multi-Task Multi-Label CNN for Effective Facial Attribute Classification64
A novel deep multi-task multi-label CNN, termed DMM-CNN, is proposed for effective FAC, which jointly optimizes two closely-related tasks to improve the performance of FAC by taking advantage of multi- task learning.
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- Multi-Level Visual-Semantic Alignments with Relation-Wise Dual Attention Network for Image and Text Matching58
A relation-wise dual attention network (RDAN) for image-text matching is proposed, which maintains an over-complete set that contains pairs of regions and words and encodes the local correlations and the global dependencies between regions and Words by training a visual-semantic network.
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 2026-10-13. 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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