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Works177 from public data
- Person Transfer GAN to Bridge Domain Gap for Person Re-identification2,033
A Person Transfer Generative Adversarial Network (PTGAN) is proposed to relieve the expensive costs of annotating new training samples and comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN.
- Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models855
The Qwen-Audio model, a multi-task training framework that achieves impressive performance across diverse benchmark tasks without requiring any task-specific fine-tuning, surpassing its counterparts.
- Pose-Driven Deep Convolutional Model for Person Re-identification843
A Pose-driven Deep Convolutional (PDC) model is proposed to learn improved feature extraction and matching models from end to end and explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts.
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- Deep Representation Learning With Part Loss for Person Re-Identification439
Experimental results on three person ReID datasets, i.e., Market1501, CUHK03, and VIPeR, show that the proposed deep representation learning procedure named part loss network outperforms existing deep representations.
- Unsupervised Person Re-Identification via Multi-Label Classification422
This paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels by leveraging the updated ReID model for label prediction and proposes the memory-based multi- label classification loss (MMCL).
- GLAD410
A Global-Local-Alignment Descriptor (GLAD) and an efficient indexing and retrieval framework that leverages the local and global cues in human body to generate a discriminative and robust representation are proposed.
- Bi-Directional Cascade Network for Perceptual Edge Detection362
A Bi-Directional Cascade Network (BDCN) structure is proposed, where an individual layer is supervised by labeled edges at its specific scale, rather than directly applying the same supervision to all CNN outputs.
- DR2-Net: Deep Residual Reconstruction Network for image compressive sensing359
A novel Deep Residual Reconstruction Network (DR2-Net) to reconstruct the image from its Compressively Sensed measurement by outperforms traditional iterative methods and recent deep learning-based methods by large margins at measurement rates 0.01, 0.1, and 0.25.
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- Watch, attend and parse: An end-to-end neural network based approach to handwritten mathematical expression recognition259
Watch, Attend and Parse (WAP), a novel end-to-end approach based on neural network that learns to recognize HMEs in a two-dimensional layout and outputs them as one-dimensional character sequences in LaTeX format, significantly outperformed the state-of-the-art method.
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- Descriptive visual words and visual phrases for image applications254
DVWs and DVPs are proposed as the visual correspondences to text words and phrases, where visual phrases refer to the frequently co-occurring visual word pairs, and are more comparable with the text words than the classic visual words.
- RAM: A Region-Aware Deep Model for Vehicle Re-Identification245
A novel learning algorithm is introduced to jointly use vehicle IDs, types/models, and colors to train the Region-Aware deep Model (RAM), which fuses more cues for training and results in more discriminative global and regional features.
- Intra-Inter Camera Similarity for Unsupervised Person Re-Identification235
This paper targets to address the challenge of degraded accuracy in label computation across cameras by studying a novel intra-inter camera similarity for pseudo-label generation, which produces surprisingly good performance on multiple datasets.
- AAformer: Auto-Aligned Transformer for Person Re-Identification205
An alignment scheme in transformer architecture for the first time is introduced and the auto-aligned transformer (AAformer) is proposed to automatically locate both the human parts and nonhuman ones at patch level to help in person re-identification.
- A glutamatergic reward input from the dorsal raphe to ventral tegmental area dopamine neurons195
It is shown that optogenetic activation of this pathway conveys reward in mice, indicating that the DR–VGluT3 pathway to VTA utilizes glutamate as a neurotransmitter and is a substrate linking the DR—one of the most sensitive reward sites in the brain—to VTA dopaminergic neurons.
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- Multi-Scale 3D Convolution Network for Video Based Person Re-Identification187
Evaluations on three widely used benchmarks datasets, i.e., MARS, PRID2011, and iLIDS-VID demonstrate the substantial advantages of the proposed two-stream convolution network over existing 3D convolution networks and state-of-art methods.
- Dorsal Raphe Dual Serotonin-Glutamate Neurons Drive Reward by Establishing Excitatory Synapses on VTA Mesoaccumbens Dopamine Neurons176
A path-specific input from DR serotonergic neurons to VTA that promotes reward by the release of glutamate and activation of mesoaccumbens dopamine neurons is suggested.
- Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-identification163
This paper tackles the challenge of jointly enforcing visual and temporal consistency in the combination of a local one-hot classification and a global multi-class classification by outperforming recent unsupervised domain adaptive methods, which leverage more labels for training.
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- Glutamatergic and dopaminergic neurons in the mouse ventral tegmental area144
It is found that low frequency of VGluT2 mRNA (+)/TH‐IR neurons in the mouse is due to lack of synthesis of TH protein, rather than TH protein trafficking.
- Building contextual visual vocabulary for large-scale image applications137
The experimental results show that the contextual visual vocabulary shows significant improvement over the classic visual vocabulary, and outperforms the state-of-the-art Bundled Feature in the terms of retrieval precision, memory consumption and efficiency.
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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