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- Heng Tao ShenSuggested from co-authorship
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Works179 from public data
- Poly Kernel Inception Network for Remote Sensing Detection544
The Poly Kernel Inception Network (PKINet) is introduced, which employs multi-scale convolution kernels without dilation to extract object features of varying scales and capture local context in remote sensing images.
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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.
- Jo-SRC: A Contrastive Approach for Combating Noisy Labels206
A noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency) that trains the network in a contrastive learning manner to advance the model generalization performance by introducing consistency regularization.
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- FECANet: Boosting Few-Shot Semantic Segmentation With Feature-Enhanced Context-Aware Network109
A feature enhancement module is proposed to suppress the matching noise caused by inter-class local similarity and enhance the intra-class relevance in the naive correlation, and a novel correlation reconstruction module is proposed that encodes extra correspondence relations between foreground and background and multi-scale context semantic features.
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- Towards Automatic Construction of Diverse, High-Quality Image Datasets82
This work proposes a novel image dataset construction framework by employing multiple textual queries and formulate noisy textual queries removing and noisy images filtering as a multi-view and multi-instance learning problem separately.
- Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach76
Comprehensive experimental results and analyses on two new benchmark datasets demonstrate that the proposed method achieves superior performance over the competing baseline models and states-of-the-art.
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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.
- 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.
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- CRSSC: Salvage Reusable Samples from Noisy Data for Robust Learning54
This work proposes a certainty-based reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images, which is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the networks.
- Spatial Structure Constraints for Weakly Supervised Semantic Segmentation51
A CAM-driven reconstruction module to directly reconstruct the input image from deep CAM features, which constrains the diffusion of last-layer object attention by preserving the coarse spatial structure of the image content, and an activation self-modulation module to refine CAMs with finer spatial structure details by enhancing regional consistency are proposed.
- Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation50
This work proposes an end-to-end multi-granularity denoising and bidirectional alignment model, to alleviate the noisy label and multi-class generalization issues, and proposes the online noise filtering and progressive noise detection modules to tackle image-level and pixel-level noise.
- Set and Rebase: Determining the Semantic Graph Connectivity for Unsupervised Cross-Modal Hashing49
A novel unsupervised hashing method called Semantic-Rebased Cross-modal Hashing (SRCH) is proposed, which develops an alternating optimization routine to rebase the graph and train the hashing auto-encoders with closed-form solutions so that the overall framework is efficiently trained.
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- A Domain Robust Approach For Image Dataset Construction44
This work proposes a novel image dataset construction framework which can generalize well to unseen target domains and formulate image filtering as a multi-instance learning (MIL) problem with constrained positive bags.
- Clustering-driven unsupervised deep hashing for image retrieval43
A novel end-to-end deep framework for image retrieval, namely Clustering-driven Unsupervised Deep Hashing (CUDH), to recursively learn discriminative clusters by soft clustering model and produce binary code with high similarity responds is proposed.
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- Collaborative representation based local discriminant projection for feature extraction41
Experimental results on ORL, AR and CMU PIE face databases validate the superiority of CRLDP over other state-of-the-art algorithms.
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- CAN-GAN: Conditioned-attention normalized GAN for face age synthesis38
This work proposes a novel Conditioned-Attention Normalization GAN for age synthesis by leveraging the aging difference between two age groups to capture facial aging regions with different attention factors and presents a Contribution-Aware Age Classifier that finely measures the importance of face vector’s elements in terms of the age classification.
- Learning With Imbalanced Noisy Data by Preventing Bias in Sample Selection36
This work proposes Class-Balance-based sample Selection (CBS) to prevent the tail class samples from being neglected during training, and introduces the Average Confidence Margin (ACM) metric to measure the quality of corrected labels by leveraging the model's evolving training dynamics.
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- Curriculum-Based Meta-learning29
This paper presents a Curriculum-Based Meta-learning (CubMeta) method to train the meta-learner using tasks from easy to hard, and in each step, a module named BrotherNet is designed to establish harder tasks and an effective learning scheme for obtaining an ensemble of stronger meta-learners.
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- Data-driven Meta-set Based Fine-Grained Visual Recognition26
This work proposes a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition that can alleviate the harmful effects caused by out-of-distribution noise and properly exploit the in-dist distribution noisy samples for training.
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- Extracting Useful Knowledge from Noisy Web Images via Data Purification for Fine-Grained Recognition19
This work investigates the noisy label problem and proposes a method that can specifically distinguish in- and out-of-distribution noisy samples and train the model on a less noisy web training set to achieve better robustness and performance.
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- Automated object recognition in high-resolution optical remote sensing imagery13
The background and results of the Automated Object Recognition in Optical Remote Sensing Imagery, which is one of the tracks in 2022 International Algorithm Case Competition, are reported and the challenges, champion solutions, and future directions are summarized.
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- Video Representation Learning with Graph Contrastive Augmentation7
This work proposes a novel contrastive self-supervised video representation learning framework, termed Graph Contrastive Augmentation (GCA), by constructing a video temporal graph and devising a graph augmentation that is designed to enhance the correlation across frames of videos and developing a new view for exploring temporal structure in videos.
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- AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic Activity Recognition6
A novel Adapt-Focused bi-Propagating Prototype learning (AdaFPP) framework to jointly recognize individual, group, and global activities in panoramic activity scenes by learning an adapt-focused detector and multi-granularity prototypes as the pretext tasks in an end-to-end way is proposed.
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- CAA3
In this paper, a novel model termed Candidate-Aware Aggregation (CAA) is proposed, which designs the Global Awareness (GA) module to exploit long-range relations among all candidates from a global perspective, which enhances the features of action instances.
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- PGP-DiffSR: Phase-Guided Progressive Pruning for Efficient Diffusion-based Image Super-Resolution–
A lightweight diffusion method, PGP-DiffSR, is developed by removing redundant information from diffusion models under the guidance of the phase information of inputs for efficient image super-resolution and significantly reducing computational load and memory consumption.
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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 2026-10-13. 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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