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- Deep Learning for Person Re-Identification: A Survey and Outlook2,304
A powerful AGW baseline is designed, achieving state-of-the-art or at least comparable performance on twelve datasets for four different Re-ID tasks, and a new evaluation metric (mINP) is introduced, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re- ID system for real applications.
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- Salient Object Detection in the Deep Learning Era: An In-Depth Survey810
This paper reviews deep SOD algorithms from different perspectives, including network architecture, level of supervision, learning paradigm, and object-/instance-level detection, and looks into the generalization and difficulty of existing SOD datasets.
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- Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-identification522
An intra-modality weighted-part attention module to extract discriminative part-aggregated features, by imposing the domain knowledge on the part relationship mining, and a parameter-free dynamic dual aggregation learning strategy to adaptively integrate the two components in a progressive joint training manner.
- Hi-Net: Hybrid-Fusion Network for Multi-Modal MR Image Synthesis399
A novel Hybrid-fusion Network (Hi-Net) is proposed for multi-modal MR image synthesis, which learns a mapping from multi- modal source images to target images, and effectively exploits the correlations among multiple modalities.
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- Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks302
This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS) and extends it to an additional task: image object co-segmentation (IOCS), demonstrating the generalizability of the framework.
- ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation276
A generic medical segmentation method, called Edge-aTtention guidance Network (ET-Net), which embeds edge-attention representations to guide the segmentation network, which outperforms current state-of-the-art segmentation methods.
- Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces251
A simple yet efficient deep network is proposed, named Multiple Color-space Fusion Network (MCF-Net), which integrates the different color-space representations at both a feature-level and prediction-level to predict image quality grades and demonstrates that the performances of automated diagnostic systems are highly dependent on image quality.
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- MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation212
A novel end-to-end learning neural network, i.e., MATNet, for zero-shot video object segmentation (ZVOS), motivated by the human visual attention behavior, which leverages motion cues as a bottom-up signal to guide the perception of object appearance.
- Striking the Right Balance With Uncertainty199
This paper demonstrates that the Bayesian uncertainty estimates directly correlate with the rarity of classes and the difficulty level of individual samples, and presents a novel framework for uncertainty based class imbalance learning that efficiently utilizes sample and class uncertainty information to learn robust features and more generalizable classifiers.
- Face Forensics in the Wild195
This work constructs a novel large-scale dataset, called FFIW10K, which comprises 10,000 high-quality forgery videos, with an average of three human faces in each frame, and proposes a novel algorithm to tackle the task of multi-person face forgery detection.
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- Quadruplet Network with One-Shot Learning for Visual Tracking.162
A new quadruplet deep network is proposed to examine the potential connections among the training instances, aiming to achieve a more powerful representation and is evaluated by model-free tracking-by-detection of objects from a single initial exemplar in several visual object tracking benchmarks.
- Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks161
This work proposes to class-wise disentangle the intermediate feature representations of deep networks, and forces the features for each class to lie inside a convex polytope that is maximally separated from the polytopes of other classes.
- LiDAR-Based Online 3D Video Object Detection With Graph-Based Message Passing and Spatiotemporal Transformer Attention158
An end-to-end online 3D video object detector that operates on point cloud sequences that enhances the conventional ConvGRU with an attentive memory gating mechanism is proposed.
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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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