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Works216 from public data
- 4,660
- Random Erasing Data Augmentation4,342
In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values and yields consistent improvement over strong baselines in image classification, object detection and person re-identification.
- Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)2,522
This paper targets at learning discriminative part-informed features for person retrieval and lays emphasis on the content consistency within each part, resulting in refined parts with enhanced within-part consistency.
- Beyond Part Models: Person Retrieval with Refined Part Pooling.2,442
This paper targets at learning discriminative part-informed features for person retrieval and lays emphasis on the content consistency within each part, resulting in refined parts with enhanced within-part consistency.
- Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro2,076
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).
- Re-ranking Person Re-identification with k-Reciprocal Encoding1,681
This paper proposes a k-reciprocal encoding method to re-rank the re-ID results, and hypothesis is that if a gallery image is similar to the probe in the k- Reciprocal nearest neighbors, it is more likely to be a true match.
- Towards Real-Time Multi-Object Tracking1,148
This work incorporates the appearance embedding model into a single-shot detector, such that the model can simultaneously output detections and the corresponding embeddings, and is formulated as a multi-task learning problem.
- MARS: A Video Benchmark for Large-Scale Person Re-Identification1,088
It is shown that CNN in classification mode can be trained from scratch using the consecutive bounding boxes of each identity, and the learned CNN embedding outperforms other competing methods considerably and has good generalization ability on other video re-id datasets upon fine-tuning.
- Circle Loss: A Unified Perspective of Pair Similarity Optimization1,086
The Circle loss is demonstrated, which has a unified formula for two elemental deep feature learning paradigms, learning with class-level labels and pair-wise labels, and the superiority of the Circle loss on a variety ofDeep feature learning tasks.
- Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification1,011
Through domain adaptation experiment, it is shown that images generated by SPGAN are more suitable for domain adaptation and yield consistent and competitive re-ID accuracy on two large-scale datasets.
- 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.
- 922
- Improving person re-identification by attribute and identity learning888
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-Identification856
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.
- Person Re-identification in the Wild830
A new dataset, PRW, is introduced to evaluate Person Re-identification in the Wild, and it is shown that pedestrian detection aids re-ID through two simple yet effective improvements: a cascaded fine-tuning strategy that trains a detection model first and then the classification model, and a Confidence Weighted Similarity (CWS) metric that incorporates detection scores into similarity measurement.
- Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation829
A category-level adversarial network is introduced, aiming to enforce local semantic consistency during the trend of global alignment, to take a close look at the category- level data distribution and align each class with an adaptive adversarial loss.
- SVDNet for Pedestrian Retrieval825
This paper proposes the SVDNet for retrieval problems, with focus on the application of person re-identification (reID), and shows that RRI effectively reduces the correlation among the projection vectors, produces more discriminative FC descriptors, and significantly improves the re-ID accuracy.
- SIFT Meets CNN: A Decade Survey of Instance Retrieval756
A comprehensive survey of instance retrieval over the last decade, presenting milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods.
- Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-Identification685
Results on three re-ID domains show that the domain adaptation accuracy outperforms the state of the art by a large margin and the three invariance properties and the proposed memory are indispensable towards an effective domain adaptation system.
- Unsupervised Person Re-identification636
A progressive unsupervised learning (PUL) method to transfer pretrained deep representations to unseen domains and demonstrates that PUL outputs discriminative features that improve the re-ID accuracy.
- Camera Style Adaptation for Person Re-identification635
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.
- Dual-path Convolutional Image-Text Embeddings with Instance Loss617
An end-to-end dual-path convolutional network to learn the image and text representations based on an unsupervised assumption that each image/text group can be viewed as a class, which allows the system to directly learn from the data and fully utilize the supervision.
- Pose-Invariant Embedding for Deep Person Re-Identification598
This paper introduces pose-invariant embedding (PIE) as a pedestrian descriptor and shows that PoseBox alone yields decent re-ID accuracy and that when integrated in the PBF network, the learned PIE descriptor produces competitive performance compared with state-of-the-art approaches.
- 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.
- Pedestrian Alignment Network for Large-scale Person Re-Identification519
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.
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 2026-10-15. 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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