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- 1Salient Object Detection: A Survey848
A comprehensive review of recent progress in salient object detection is provided and this field is situate among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction.
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- IRM441
The IRM measure for evaluating overall similarity between images incorporates properties of all the regions in the images by a region-matching scheme, which achieves more accurate retrieval at higher speed than several existing systems.
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- Learning Open Set Network with Discriminative Reciprocal Points294
A new concept, Reciprocal Point, which is the potential representation of the extra-class space corresponding to each known category, which can indirectly introduce the unknown information into the learner with only known classes, so as to learn more compact and discriminative representations.
- Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation210
This paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation by selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which is then used to regularize the fine-tuning process of the student stream.
- Selectivity or Invariance: Boundary-Aware Salient Object Detection206
A novel boundary-aware network with successive dilation for image-based SOD, where the feature selectivity at boundaries is enhanced by incorporating a boundary localization stream, while the feature invariance at interiors is guaranteed with a complex interior perception stream.
- Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning197
A Transductive Episodic-wise Adaptive Metric (TEAM) framework for few-shot learning is proposed, by integrating the meta-learning paradigm with both deep metric learning and transductive inference and leverages an attention-based bi-directional similarity strategy for extracting the more robust relationship between queries and prototypes.
- Pyramidal Feature Shrinking for Salient Object Detection189
The proposed pyramidal feature shrinking network (PFSNet), which aims to aggregate adjacent feature nodes in pairs with layer-by-layer shrinkage, so that the aggregated features fuse effective details and semantics together and discard interference information.
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- IMAGINATION172
The proposed IMAGINATION (IMAge Generation for INternet AuthenticaTION), a system for the generation of attack-resistant, user-friendly, image-based CAPTCHAs, produces controlled distortions on randomly chosen images and presents them to the user for annotation from a given list of words.
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- A Benchmark Dataset and Saliency-Guided Stacked Autoencoders for Video-Based Salient Object Detection154
The proposed unsupervised baseline approach for video-based SOD is compared with 31 state-of-the-art models on the proposed dataset and outperforms 30 of them, including 19 image-based classic (unsupervised or non-deep learning) models, six image- based deep learning models, and five video- based unsuper supervised models.
- Pyramid Grafting Network for One-Stage High Resolution Saliency Detection147
An attention-based Cross-Model Grafting Module (CMGM) is proposed to en-able CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process, and an Attention Guided Loss (AGL) to explicitly supervise the attention matrix generated by CMGM to help the network better interact with the attention from different models.
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- Fast Discrete Distribution Clustering Using Wasserstein Barycenter With Sparse Support138
A modified Bregman ADMM approach for computing the approximate discrete Wasserstein barycenter of large clusters that achieves high accuracy empirically at a much reduced computational cost and is highly competitive in comparison with some widely used methods in the corresponding areas.
- Complementary Trilateral Decoder for Fast and Accurate Salient Object Detection137
A novel framework to treat semantic context, spatial detail and boundary information separately in the decoder part of the Complementary Trilateral Decoder for saliency detection with three branches: Semantic Path, Spatial Path and Boundary Path is proposed.
- Collaboration over time: characterizing and modeling network evolution124
A novel stochastic model, Stochastic Poisson model with Optimization Tree (Spot) is proposed to efficiently predict any increment of collaboration based on the local neighborhood structure and empirical results show that Spot outperforms Support Vector Regression by better fitting collaboration records and predicting the rate of collaboration.
- Transformer-based Dual Relation Graph for Multi-label Image Recognition120
This paper proposes a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, i.e., structural relation graph and semantic relation graph, which aims to capture long-range correlations from object context by developing a cross-scale transformer-based architecture.
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- Health-Related Hot Topic Detection in Online Communities Using Text Clustering111
Automatic topic detection based on document clustering is an alternative approach for extracting health-related hot topics in online communities and is integrated into medical domain-specific features to represent the messages posted in online health communities.
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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 2026-10-14. 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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