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- PraNet: Parallel Reverse Attention Network for Polyp Segmentation1,966
Quantitative and qualitative evaluations on five challenging datasets across six metrics show that the PraNet improves the segmentation accuracy significantly, and presents a number of advantages in terms of generalizability, and real-time segmentation efficiency.
- Triplet Loss in Siamese Network for Object Tracking647
A novel triplet loss is proposed to extract expressive deep feature for object tracking by adding it into Siamese network framework instead of pairwise loss for training.
- Video Salient Object Detection via Fully Convolutional Networks642
A novel data augmentation technique that simulates video training data from existing annotated image data sets, which enables the deep video saliency network to learn diverse saliency information and prevents overfitting with the limited number of training videos.
- Learning Human-Object Interactions by Graph Parsing Neural Networks596
This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images and videos with the Graph Parsing Neural Network (GPNN), a framework that incorporates structural knowledge while being differentiable end-to-end.
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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.
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- Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection492
This paper proposes a fast video salient object detection model, based on a novel recurrent network architecture, named Pyramid Dilated Bidirectional ConvLSTM (PDB-ConvL STM), which achieves state-of-the-art results on two popular benchmarks, well demonstrating its superior performance and high applicability.
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- 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.
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- RGB-D salient object detection: A survey322
This paper provides a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail, and investigates the ability of existing models to detect salient objects.
- Revisiting Video Saliency: A Large-Scale Benchmark and a New Model311
This work introduces a new benchmark for predicting human eye movements during dynamic scene free-viewing, and proposes a novel video saliency model that augments the CNN-LSTM network architecture with an attention mechanism to enable fast, end-to-end saliency learning.
- 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.
- Siamese Network for RGB-D Salient Object Detection and Beyond250
The proposed JL-DCF module provides robust saliency feature learning by exploiting cross-modal commonality via a Siamese network, while the DCF module is introduced for complementary feature discovery.
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- 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.
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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