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Works19 from public data
- Temporal Action Segmentation: An Analysis of Modern Techniques165
This survey analyzes and summarizes the most significant contributions and trends in temporal action segmentation in videos, and systematically investigates two essential techniques of this topic, i.e., frame representation and temporal modeling.
- Feature Affinity-Based Pseudo Labeling for Semi-Supervised Person Re-Identification91
This paper proposes a novel feature affinity-based pseudo labeling method with two possible label encodings, and is the first study that employs pseudo-labeling by measuring the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks.
- Feature mask network for person re-identification54
A Feature Mask Network (FMN) is proposed that takes advantage of ResNet high-level features to predict a feature map mask and then imposes it on the low- level features to dynamically re-weight different object parts for a complementary feature representation.
- Dispersion based Clustering for Unsupervised Person Re-identification.44
A Dispersion based Clustering (DBC) approach which performs better at discovering the underlying data patterns and can automatically prioritize standalone data points and prevents poor clustering.
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- Temporal Action Segmentation With High-Level Complex Activity Labels24
This work is the first to propose a Constituent Action Discovery framework that only requires the video-wise high-level complex activity label as supervision for temporal action segmentation, and adopts the Hungarian matching algorithm to relate latent action prototypes to ground truth semantic classes for evaluation.
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- Robust Fine-Grained Learning for Cloth-Changing Person Re-Identification6
A four-body-part attention module is introduced to enhance the learning of detailed pedestrian semantic features and a fine-grained semantic loss is designed to guide the model in learning identity-related, detailed semantic features, thereby improving its focus on cloth-agnostic regions.
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- Siamese global location-aware network for visual object tracking4
This paper proposes a siamese global location-aware object tracking algorithm (SiamGLA) and designs an internal feature combination (IFC) module that improves feature representation with almost no additional parameters, making it more likely to be applied in practice.
- Rapid Person Re-Identification via Sub-space Consistency Regularization2
A novel Sub-space Consistency Regularization (SCR) algorithm that can speed up the ReID procedure by 0.25 times than real-value features under same dimensions whilst maintain a competitive accuracy, especially under short codes.
- Gate-and-Merge: Zero-shot Compositional Personalization of Vision Language Models–
Gate-and-Merge, a zero-shot framework that enables compositional personalization without the need for co-occurrence training, is introduced, and consistent gains in performance across multiple personalization tasks in both single-concept and compositional settings are shown.
- Enhanced multi-view image clustering via dual-fusion contrastive learning–
DFCMVC is proposed, a dual-fusion framework featuring a differentiable, attention-based encoder (SBMHE) that implicitly learns view-specific architectural weights via dynamic gating, enabling end-to-end adaptation of representational capacity.
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- A Large Scale Benchmark of Person Re-Identification–
The LSMS-UAV dataset verifies that UAV data has strong transferability to traditional camera-based data and demonstrates LSMS’s excellent capability in addressing the domain gap issue when facing complex and unknown environments.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. 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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