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- Cheng‐Lin LiuSuggested from co-authorship
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Works116 from public data
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- Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation223
A graph-based global reasoning unit is introduced to strengthen the classification network’s ability to capture global relations among disjoint and distant regions and a potential object mining module is proposed to reduce the false-negative rate in pseudo labels.
- TransZero: Attribute-Guided Transformer for Zero-Shot Learning206
This paper proposes an attribute-guided Transformer network to learn the attribute localization for discriminative visual-semantic embedding representations in ZSL, termed TransZero, which achieves a new state-of-the-art on three ZSL benchmarks.
- Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification186
A Noise Resistible Mutual-Training (NRMT) method, which maintains two networks during training to perform collaborative clustering and mutual instance selection, which outperforms the state-of-the-art UDA methods for person re-ID.
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- Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation168
By integrating the complementary information of MLR, CFV, and the CNN features of the fully connected layer, the state-of-the-art performance can be achieved on scene recognition and DA problems.
- MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning163
A Mutually Semantic Distillation Network (MSDN) is proposed, which progressively distills the intrinsic semantic representations between visual and attribute features for ZSL, leading to new state-of-the-art performances on three popular challenging benchmarks.
- Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation119
This work proposes a dynamic prototype convolution network (DPCN) to fully capture the aforementioned intrinsic details for accurate FSS, and shows that DPCN yields superior performances under both 1-shot and 5-shot settings.
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- TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning99
A cross attribute-guided Transformer network to refine visual features and learn accurate attribute localization for key semantic knowledge representations in ZSL, which achieves the new state-of-the-art results on three golden ZSL benchmarks and on the large-scale ImageNet dataset.
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- Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation71
A saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes and an object guided label refinement module to take a full use of both the segmentation prediction and the initial labels for obtaining superior pseudo-labels.
- LG-CNN: From local parts to global discrimination for fine-grained recognition70
This paper proposes a jointly trained Convolutional Neural Network (CNN) architecture to solve the fine-grained recognition problem without using part and bounding box information and applies this architecture to generic object recognition datasets with superior performance over the directly fine-tuned CNN architecture.
- MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition69
A novel Multi-Velocity Progressive-alignment (MVP-Shot) framework to progressively learn and align semantic-related action features at multi-velocity levels and outperforms current state-of-the-art methods on multiple standard few-shot benchmarks.
- Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification66
A novel approach to remove irrelevant samples from the real-world web images during training, and only utilize useful images for updating the networks is proposed, which can alleviate the harmful effects caused by irrelevant noisy web images to achieve better performance.
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- Semantically Meaningful Class Prototype Learning for One-Shot Image Segmentation60
This paper proposes to leverage the multi-class label information during the episodic training to encourage the network to generate more semantically meaningful features for each category and proposes a fused prototype guidance branch for the segmentation of the query image.
- Video super-resolution based on spatial-temporal recurrent residual networks59
The proposed Spatial-Temporal Recurrent Residual Network (STR-ResNet) is able to efficiently reconstruct videos with diversified contents and complex motions, which outperforms the existing video SR approaches and offers new state-of-the-art performances on benchmark 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 2026-10-14. 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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