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- See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks537
This work introduces a novel network, called as CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view and proposes a unified and end-to-end trainable framework where different co-att attention variants can be derived for mining the rich context within videos.
- Human-Aware Motion Deblurring403
A human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG) humans based on a triple-branch encoder-decoder architecture that performs favorably against the state-of-the-art motion deblurred methods, especially in capturing semantic details.
- Video Object Segmentation with Episodic Graph Memory Networks303
This work exploits an episodic memory network, organized as a fully connected graph, to store frames as nodes and capture cross-frame correlations by edges and yields a neat yet principled framework, which can generalize well both one-shot and zero-shot video object segmentation tasks.
- 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.
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- Deep Regression Tracking with Shrinkage Loss243
This work proposes a novel shrinkage loss to penalize the importance of easy training data and applies residual connections to fuse multiple convolutional layers as well as their output response maps to balance training data.
- Learning Video Object Segmentation From Unlabeled Videos184
A unified unsupervised/weakly supervised learning framework, called MuG, that comprehensively captures intrinsic properties of VOS at multiple granularities is introduced that can help advance understanding of visual patterns in VOS and significantly reduce annotation burden.
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- Distilled Siamese Networks for Visual Tracking148
This model is intuitively inspired by the one teacher versus multiple students learning method typically employed in schools and contains a single teacher-student distillation module and a student-student knowledge sharing mechanism that is designed using a tracking-specific distillation strategy to transfer knowledge from a teacher to students.
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- Paying Attention to Video Object Pattern Understanding128
For the first time, it is quantitatively verified the high consistency of visual attention behavior among human observers, and found strong correlation between human attention and explicit primary object judgments during dynamic, task-driven viewing.
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- Global-Local Attention Network for Aerial Scene Classification69
A novel end-to-end global-local attention network (GLANet) is proposed to capture both global and local information for aerial scene classification and the experimental results on three challenging large-scale scene datasets demonstrate the effectiveness of the proposed global- local attention network.
- Quality-Aware Selective Fusion Network for V-D-T Salient Object Detection58
A quality-aware selective fusion network (QSF-Net) to conduct VDT salient object detection, which contains three subnets including the initial feature extraction subnet, the quality-aware region selection subnet, and the region-guided selective fusion subnet.
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- Learning transform-aware attentive network for object tracking33
A transform-aware attentive tracking framework, which uses a deep attentive network to directly predict the target states via spatial transform parameters, built on the Spatial Transform Network (STN).
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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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