Foreground Segmentation
20 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Foreground Segmentation
Most implemented papers
Real-time Instance Segmentation with Discriminative Orientation Maps
Although instance segmentation has made considerable advancement over recent years, it's still a challenge to design high accuracy algorithms with real-time performance.
Visual Boundary Knowledge Translation for Foreground Segmentation
zju-vipa/BKT
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To this end, we propose a Translation Segmentation Network (Trans-Net), which comprises a segmentation network and two boundary discriminators.
Autoencoder-based background reconstruction and foreground segmentation with background noise estimation
The main novelty of the proposed model is that the autoencoder is also trained to predict the background noise, which allows to compute for each frame a pixel-dependent threshold to perform the foreground segmentation.
Cascaded Sparse Feature Propagation Network for Interactive Segmentation
kleinzcy/csfpn
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We aim to tackle the problem of point-based interactive segmentation, in which the key challenge is to propagate the user-provided annotations to unlabeled regions efficiently.
Exploring the Interactive Guidance for Unified and Effective Image Matting
dinghow/uim
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Although avoiding the extensive labors of trimap annotation, existing methods still suffer from two limitations: (1) For the single image with multiple objects, it is essential to provide extra interaction information to help determining the matting target; (2) For transparent objects, the accurate regression of alpha matte from RGB image is much more difficult compared with the opaque ones.
Visual Prompting via Image Inpainting
How does one adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification?
Explicit Visual Prompting for Low-Level Structure Segmentations
nifangbaage/explicit-visual-prompt
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CVPR 2023
Different from the previous visual prompting which is typically a dataset-level implicit embedding, our key insight is to enforce the tunable parameters focusing on the explicit visual content from each individual image, i. e., the features from frozen patch embeddings and the input's high-frequency components.
ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection
casia-iva-lab/zbs
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CVPR 2023
However, previous unsupervised deep learning BGS algorithms perform poorly in sophisticated scenarios such as shadows or night lights, and they cannot detect objects outside the pre-defined categories.
AquaSAM: Underwater Image Foreground Segmentation
duooppa/AquaSAM
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The Segment Anything Model (SAM) has revolutionized natural image segmentation, nevertheless, its performance on underwater images is still restricted.
Instruct Me More! Random Prompting for Visual In-Context Learning
jackieam/inmemo
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Our findings suggest that InMeMo offers a versatile and efficient way to enhance the performance of visual ICL with lightweight training.
