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Works117 from public data
- Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro2,082
A simple semisupervised pipeline that only uses the original training set without collecting extra data, which effectively improves the discriminative ability of learned CNN embeddings and proposes the label smoothing regularization for outliers (LSRO).
- A Discriminatively Learned CNN Embedding for Person Reidentification964
This article proposes a Siamese network that simultaneously computes the identification loss and verification loss and learns a discriminative embedding and a similarity measurement at the same time, thus taking full usage of the re-ID annotations.
- Improving person re-identification by attribute and identity learning917
An attribute-person recognition (APR) network is proposed, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes, and demonstrates that by learning a more discriminative representation, APR achieves competitive re-IDs performance compared with the state-of-the-art methods.
- Joint Discriminative and Generative Learning for Person Re-Identification865
This paper proposes a joint learning framework that couples re-id learning and data generation end-to-end and renders significant improvement over the baseline without using generated data, leading to the state-of-the-art performance on several benchmark datasets.
- Camera Style Adaptation for Person Re-identification652
CamStyle can serve as a data augmentation approach that smooths the camera style disparities in person re-identification and demonstrate consistent improvement in all systems regardless of the extent of over-fitting.
- Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation599
The proposed approach dynamically sets different confidence thresholds according to the prediction variance, rectifies the learning from noisy pseudo labels, and achieves significant improvements over the conventional pseudo label learning and yields competitive performance on all three benchmarks.
- Pedestrian Alignment Network for Large-scale Person Re-Identification555
This work introduces the pedestrian alignment network (PAN) which allows discriminative embedding learning pedestrian alignment without extra annotations, and observes that when the convolutional neural network learns to discriminate between different identities, the learned feature maps usually exhibit strong activations on the human body rather than the background.
- University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization505
It is argued that drones could serve as the third platform to deal with the geo-localization problem and propose a strong CNN baseline on this challenging dataset, named University-1652, which is the first drone-based geo- localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation.
- Each Part Matters: Local Patterns Facilitate Cross-View Geo-Localization329
It is argued that neighbor areas can be leveraged as auxiliary information, enriching discriminative clues for geo-localization, and introduced a simple and effective deep neural network, called Local Pattern Network (LPN), to take advantage of contextual information in an end-to-end manner.
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- Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark204
A large Multi-Attribute and Language Search dataset for text-based person retrieval, called MALS, is introduced and the feasibility of performing pre-training on both attribute recognition and image-text matching tasks in one stone is explored, achieving state-of-the-art retrieval performance via APTM on three challenging real-world benchmarks.
- VehicleNet: Learning Robust Visual Representation for Vehicle Re-Identification195
This work proposes to build a unique large-scale vehicle dataset by harnessing four public vehicle datasets, and design a simple yet effective two-stage progressive approach to learning more robust visual representation from VehicleNet.
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- Macro-Micro Adversarial Network for Human Parsing165
Compared with traditional adversarial networks, the proposed Macro-Micro Adversarial Net not only enforces local and semantic consistency explicitly, but also avoids the poor convergence problem of adversarial Networks when handling high resolution images.
- Thorax disease classification with attention guided convolutional neural network136
This paper proposes to integrate the global and local cues into a three-branch attention guided convolution neural network (AG-CNN) to identify thorax diseases and demonstrates that after integrating the local cues with the global information, the average AUC scores are improved by AG-CNN.
- Unsupervised Scene Adaptation with Memory Regularization in vivo130
This paper proposes an orthogonal method, called memory regularization in vivo, to exploit the intra-domain knowledge and regularize the model training, and refers to the segmentation model itself as the memory module, and minor the discrepancy of the two classifiers to reduce the prediction inconsistency.
- PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation96
This work revisits the current pixel contrast in semantic segmentation and proposes a unified pixel- and patch-wise self-supervised learning framework, called PiPa, for domain adaptive semantic segmentsation that facilitates intra-image pixel-wise correlations and patch -wise semantic consistency against different contexts.
- Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization94
A unified learning approach to simultaneously modeling the coarse- and fine-grained retrieval by considering the multi-grained uncertainty is introduced, which prevents the model from pushing away potential candidates in the early stage, and thus improves the recall rate.
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- VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation83
VL-Uncertainty is introduced, the first uncertainty-based framework for detecting hallucinations in LVLMs that utilizes uncertainty as an intrinsic metric and significantly outperforms strong baseline methods in hallucination detection.
- Parameter-Efficient Person Re-Identification in the 3D Space80
This work projects 2D images to a 3D space and introduces a novel parameter-efficient omni-scale graph network (OG-Net) to learn the pedestrian representation directly from 3D point clouds and exploits the complementary information of 2D appearance and 3D structure.
- Dual-Path Convolutional Image-Text Embedding.77
This paper builds a convolutional network amenable for fine-tuning the visual and textual representations, where the entire network only contains four components, i.e., convolution layer, pooling layer, rectified linear unit function (ReLU), and batch normalisation.
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- Progressive Local Filter Pruning for Image Retrieval Acceleration75
This article proposes a Progressive Local Filter Pruning (PLFP) method, which preserves the original model capacity while pruning and achieves superior performance to other widely-used pruning methods.
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 2026-10-12. 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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