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- Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions5,324
The Pyramid Vision Transformer (PVT) is introduced, which overcomes the difficulties of porting Transformer to various dense prediction tasks and is validated through extensive experiments, showing that it boosts the performance of many downstream tasks, including object detection, instance and semantic segmentation.
- PVT v2: Improved baselines with pyramid vision transformer2,673
This work improves the original Pyramid Vision Transformer (PVT v1) by adding three designs: a linear complexity attention layer, an overlapping patch embedding, and a convolutional feed-forward network to reduce the computational complexity of PVT v1 to linearity and provide significant improvements on fundamental vision tasks.
- Structure-Measure: A New Way to Evaluate Foreground Maps1,970
A novel, efficient, and easy to calculate measure known as S-measure (structural measure) to evaluate foreground maps, which simultaneously evaluates region-aware and object-aware structural similarity between a foreground map and a ground-truth map.
- 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.
- 1,938
- Enhanced-alignment Measure for Binary Foreground Map Evaluation1,716
This paper takes a detailed look at current binary FM evaluation measures and proposes a novel and effective E-measure (Enhanced-alignment measure), jointly capturing image-level statistics and local pixel matching information, which demonstrates the superiority of this measure over the available measures on 4 popular datasets via 5 meta-measures.
- 1,279
- EGNet: Edge Guidance Network for Salient Object Detection1,212
This paper presents an edge guidance network (EGNet) for salient object detection with three steps to simultaneously model these two kinds of complementary information in a single network to solve the complementarity between salient edge information and salient object information.
- 915
- Concealed Object Detection786
The first systematic study on concealed object detection (COD), which aims to identify objects that are visually embedded in their background, is presented, and rich annotations including object categories, object boundaries, challenging attributes, object-level labels, and instance-level annotations are provided.
- Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks729
It is demonstrated that D3Net can be used to efficiently extract salient object masks from real scenes, enabling effective background-changing application with a speed of 65 frames/s on a single GPU.
- Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers626
The proposed model, named Polyp-PVT, effectively suppresses noises in the features and significantly improves their expressive capabilities, and is more robust to various challenging situations than existing representative methods.
- Simultaneously Localize, Segment and Rank the Camouflaged Objects599
It is argued that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage and evolution of animals, but also provide guidance to design more sophisticated camouflage techniques.
- Camouflaged Object Segmentation with Distraction Mining564
This paper develops a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature and significantly outperforms 18 cutting-edge models on three challenging datasets under four standard metrics.
- 537
- Segment anything model for medical images?518
This comprehensive report comprehensively analyzed different models and strategies on the so-called COSMOS 1050K dataset to fully validate SAM's performance on medical data and help researchers explore the potential of SAM applications in MIS, and guide how to appropriately use and develop SAM.
- Salient Object Detection via Integrity Learning480
This work designs a novel integrity learning for SOD, which outperforms the baseline methods in terms of a wide range of metrics and introduces an integrity channel enhancement (ICE) component with the goal of enhancing feature channels that highlight the integral salient objects, while suppressing the other distracting ones.
- 442
- UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders408
Inspired by the saliency data labeling process, a probabilistic RGB-D saliency detection network via conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space is proposed.
- BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network402
This paper proposes a bifurcated backbone strategy (BBS) to split the multi-level features of RGB-D salient object detection into teacher and student features, and utilizes a depth-enhanced module (DEM) to excavate informative parts of depth cues from the channel and spatial views.
- JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection365
The JL module provides robust saliency feature learning, while the latter is introduced for complementary feature discovery, and the designed framework yields a robust RGB-D saliency detector with good generalization.
- Mutual Graph Learning for Camouflaged Object Detection350
A novel Mutual Graph Learning (MGL) model is designed, which generalizes the idea of conventional mutual learning from regular grids to the graph domain and fully exploits the mutual benefits by recurrently reasoning their high-order relations through graphs.
- Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground349
This work identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter, and proposes a new high quality dataset and updates the previous saliency benchmark.
- 335
- RGB-D salient object detection: A survey329
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.
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 2026-10-12. 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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