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- Learning Domain Invariant Prompt for Vision-Language Models93
This study introduces an innovative prompt learning paradigm, called MetaPrompt, aiming to directly learn domain invariant prompt in few-shot scenarios and introduces a novel asymmetric contrastive learning paradigm, where representations from the pre-trained encoder assume supervision to regularize prompts from the prompted encoder.
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- Scene Text Image Super-Resolution via Parallelly Contextual Attention Network66
A Parallelly Contextual Attention Network (PCAN), which effectively learns sequence-dependent features and focuses more on high-frequency information of the reconstruction in text images, and outperforms eleven state-of-the-art (SOTA) SR methods in terms of boosting text recognition performance.
- A novel statistical image thresholding method64
A novel criterion combining between-class variance and minimizing class variance is proposed to eliminate the described limitation for classic statistical approaches and improve segmentation performance.
- Similarity learning with joint transfer constraints for person re-identification59
The issue of person re-identification under inconsistent data distributions can be transformed into a consistent iterative convex optimization problem, and solved via the inexact augmented Lagrange multiplier (IALM) algorithm.
- Multilevel triplet deep learning model for person re-identification57
This paper improved the performance of deep CNN network with the proposed Multilevel feature extraction strategy and built a novelMultilevel triplet deep learning model corresponding to the method.
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- Maximum decision entropy-based attribute reduction in decision-theoretic rough set model47
A novel monotonic uncertainty measure is introduced for attribute reduction in the decision-theoretic rough set model and a heuristic attribute reduction algorithm based on the maximum decision entropy is developed, which maximizes the relevance of the reduct to the class attribute and also minimizes the redundancy of the condition attributes within the reductor.
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- Multi-scale gist feature manifold for building recognition31
The results show that the proposed model is superior to other models in practice of building recognition and can handle the building recognition problem caused by rotations, variant lighting conditions and occlusions very well.
- Diverse Person: Customize Your Own Dataset for Text-Based Person Search30
This paper proposes to leverage available images of clothing and accessories as reference attribute images to edit the original dataset images through diffusion models, and employs a Large Language Model (LLM) to produce annotations that are both high in quality and stylistically consistent with those found in real-world datasets.
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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