Semi-Supervised Video Object Segmentation
94 papers with code • 15 benchmarks • 13 datasets
The semi-supervised scenario assumes the user inputs a full mask of the object(s) of interest in the first frame of a video sequence. Methods have to produce the segmentation mask for that object(s) in the subsequent frames.
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Latest papers with no code
Real-time Surgical Instrument Segmentation in Video Using Point Tracking and Segment Anything
Inspired by this progress, we present a novel framework that combines an online point tracker with a lightweight SAM model that is fine-tuned for surgical instrument segmentation.
SpVOS: Efficient Video Object Segmentation with Triple Sparse Convolution
Therefore, we propose a sparse baseline of VOS named SpVOS in this work, which develops a novel triple sparse convolution to reduce the computation costs of the overall VOS framework.
Sub-token ViT Embedding via Stochastic Resonance Transformers
We term our method ``Stochastic Resonance Transformer" (SRT), which we show can effectively super-resolve features of pre-trained ViTs, capturing more of the local fine-grained structures that might otherwise be neglected as a result of tokenization.
Memory-Efficient Continual Learning Object Segmentation for Long Video
We propose two novel techniques to reduce the memory requirement of Online VOS methods while improving modeling accuracy and generalization on long videos.
Hierarchical Spatiotemporal Transformers for Video Object Segmentation
This paper presents a novel framework called HST for semi-supervised video object segmentation (VOS).
ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: Semi-Supervised Video Object Segmentation
The Associating Objects with Transformers (AOT) framework has exhibited exceptional performance in a wide range of complex scenarios for video object segmentation.
TrickVOS: A Bag of Tricks for Video Object Segmentation
Space-time memory (STM) network methods have been dominant in semi-supervised video object segmentation (SVOS) due to their remarkable performance.
Robust and Efficient Memory Network for Video Object Segmentation
For limitation 2, we first adaptively decide whether to update the memory features depending on the variation of foreground objects to reduce temporal redundancy.
MobileVOS: Real-Time Video Object Segmentation Contrastive Learning meets Knowledge Distillation
This paper tackles the problem of semi-supervised video object segmentation on resource-constrained devices, such as mobile phones.
Flow-guided Semi-supervised Video Object Segmentation
A model to extract the combined information from optical flow and the image is proposed, which is then used as input to the target model and the decoder network.