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Found 20 papers, 12 papers with code
Siamese Network for RGB-D Salient Object Detection and Beyond
Inspired by the observation that RGB and depth modalities actually present certain commonality in distinguishing salient objects, a novel joint learning and densely cooperative fusion (JL-DCF) architecture is designed to learn from both RGB and depth inputs through a shared network backbone, known as the Siamese architecture.
Ranked #3 on
RGB-D Salient Object Detection
on STERE
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection.
Ranked #6 on
RGB-D Salient Object Detection
on NLPR
Full-Duplex Strategy for Video Object Segmentation
Previous video object segmentation approaches mainly focus on using simplex solutions between appearance and motion, limiting feature collaboration efficiency among and across these two cues.
Ranked #7 on
Video Polyp Segmentation
on SUN-SEG-Hard (Unseen)
Light Field Salient Object Detection: A Review and Benchmark
Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models, are achieved.
Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection
To tackle this dilemma and also inspired by the fact that depth quality is a key factor influencing the accuracy, we propose a novel depth quality-inspired feature manipulation (DQFM) process, which is efficient itself and can serve as a gating mechanism for filtering depth features to greatly boost the accuracy.
Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection
Inspired by the fact that depth quality is a key factor influencing the accuracy, we propose an efficient depth quality-inspired feature manipulation (DQFM) process, which can dynamically filter depth features according to depth quality.
RGB-D Salient Object Detection via 3D Convolutional Neural Networks
The proposed model, named RD3D, aims at pre-fusion in the encoder stage and in-depth fusion in the decoder stage to effectively promote the full integration of RGB and depth streams.
BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection
Depth information has been proved beneficial in RGB-D salient object detection (SOD).
Ranked #2 on
RGB-D Salient Object Detection
on DES
Depth-Cooperated Trimodal Network for Video Salient Object Detection
However, existing video salient object detection (VSOD) methods only utilize spatiotemporal information and seldom exploit depth information for detection.
Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network
Camouflaged Object Detection (COD) aims to detect objects with similar patterns (e. g., texture, intensity, colour, etc) to their surroundings, and recently has attracted growing research interest.
EF-Net: A novel enhancement and fusion network for RGB-D saliency detection
Finally, we propose an effective layer-wise aggregation module to fuse the features extracted from the enhanced depth maps and RGB images for the accurate detection of salient objects.
Robust Visual Tracking via Inverse Nonnegative Matrix Factorization
It utilizes both the foreground and background information, and imposes a local coordinate constraint, where the basis matrix is sparse matrix from the linear combination of candidates with corresponding nonnegative coefficient vectors.
Saliency Propagation From Simple to Difficult
In the teaching-to-learn step, a teacher is designed to arrange the regions from simple to difficult and then assign the simplest regions to the learner.
Unsupervised Many-to-Many Image-to-Image Translation Across Multiple Domains
To improve the image quality, we propose an effective many-to-many mapping framework for unsupervised multi-domain image-to-image translation.
Guided Focal Stack Refinement Network for Light Field Salient Object Detection
To this end, we propose a guided refinement and fusion module (GRFM) to refine focal stacks and aggregate multi-modal features.
Promoting Segment Anything Model towards Highly Accurate Dichotomous Image Segmentation
We have high expectations regarding whether SAM, as a foundation model, can be improved towards highly accurate object segmentation, which is known as dichotomous image segmentation (DIS).
Dynamic Patch-aware Enrichment Transformer for Occluded Person Re-Identification
To facilitate the seamless integration of global classification features with the finely detailed local features selected by DPSM, we introduce a novel feature blending module (FBM).
Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection
The prompt fed to the motion stream is learned by supervising optical flow in a self-supervised manner.
Effectiveness Assessment of Recent Large Vision-Language Models
The advent of large vision-language models (LVLMs) represents a noteworthy advancement towards the pursuit of artificial general intelligence.
