Papers Zero-Shot Video Object Segmentation
“Zero-Shot Video Object Segmentation” 태그가 달린 논문 16편 · 필터 해제
Rethinking Image-to-Video Adaptation: An Object-centric Perspective
Image-to-video adaptation seeks to efficiently adapt image models for use in the video domain. Instead of finetuning the entire image backbone, many image-to-video adaptation paradigms use lightweight adapters for tempor…
Action RecognitionObjectObject DiscoverySemantic Segmentation+5Depth-aware Test-Time Training for Zero-shot Video Object Segmentation
Zero-shot Video Object Segmentation (ZSVOS) aims at segmenting the primary moving object without any human annotations. Mainstream solutions mainly focus on learning a single model on large-scale video datasets, which st…
Depth EstimationDepth PredictionSemantic SegmentationVideo Object Segmentation+2UniVS: Unified and Universal Video Segmentation with Prompts as Queries
Despite the recent advances in unified image segmentation (IS), developing a unified video segmentation (VS) model remains a challenge. This is mainly because generic category-specified VS tasks need to detect all object…
DecoderReferring Expression SegmentationReferring Video Object SegmentationVideo Instance Segmentation+6Hierarchical Graph Pattern Understanding for Zero-Shot VOS
The optical flow guidance strategy is ideal for obtaining motion information of objects in the video. It is widely utilized in video segmentation tasks. However, existing optical flow-based methods have a significant dep…
DecoderGraph Neural NetworkOptical Flow EstimationSemantic Segmentation+4Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation
Recent leading zero-shot video object segmentation (ZVOS) works devote to integrating appearance and motion information by elaborately designing feature fusion modules and identically applying them in multiple feature st…
Semantic SegmentationVideo Object SegmentationVideo Semantic SegmentationZero-Shot Video Object SegmentationSegment Anything Meets Point Tracking
The Segment Anything Model (SAM) has established itself as a powerful zero-shot image segmentation model, enabled by efficient point-centric annotation and prompt-based models. While click and brush interactions are both…
Interactive Video Object SegmentationObjectPoint TrackingSegmentation+4Co-attention Propagation Network for Zero-Shot Video Object Segmentation
Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects. However, existing ZS-VOS methods often struggle to distinguish between foregro…
DecoderOptical Flow EstimationSemantic SegmentationVideo Object Segmentation+2Adaptive Multi-source Predictor for Zero-shot Video Object Segmentation
Static and moving objects often occur in real-life videos. Most video object segmentation methods only focus on extracting and exploiting motion cues to perceive moving objects. Once faced with the frames of static objec…
ObjectOptical Flow EstimationSemantic SegmentationUnsupervised Video Object Segmentation+3Multi-Source Fusion and Automatic Predictor Selection for Zero-Shot Video Object Segmentation
Location and appearance are the key cues for video object segmentation. Many sources such as RGB, depth, optical flow and static saliency can provide useful information about the objects. However, existing approaches onl…
Depth EstimationObjectSalient Object DetectionUnsupervised Video Object Segmentation+2Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation
How to make the appearance and motion information interact effectively to accommodate complex scenarios is a fundamental issue in flow-based zero-shot video object segmentation. In this paper, we propose an Attentive…
Semantic SegmentationUnsupervised Video Object SegmentationVideo Object SegmentationVideo Semantic Segmentation+1MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation
In this paper, we present a novel end-to-end learning neural network, i.e., MATNet, for zero-shot video object segmentation (ZVOS). Motivated by the human visual attention behavior, MATNet leverages motion cues as a bott…
ObjectSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+3Video Object Segmentation with Episodic Graph Memory Networks
How to make a segmentation model efficiently adapt to a specific video and to online target appearance variations are fundamentally crucial issues in the field of video object segmentation. In this work, a graph memory n…
ObjectSegmentationSemantic SegmentationVideo Object Segmentation+2ALBA : Reinforcement Learning for Video Object Segmentation
We consider the challenging problem of zero-shot video object segmentation (VOS). That is, segmenting and tracking multiple moving objects within a video fully automatically, without any manual initialization. We treat t…
ObjectOne-shot visual object segmentationreinforcement-learningReinforcement Learning+6Motion-Attentive Transition for Zero-Shot Video Object Segmentation
In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object repres…
DecoderObjectSegmentationSemantic Segmentation+4Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS). The suggested AGNN recasts this task as a process of iterative information fusion over video graphs. Specif…
Graph Neural NetworkSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+4RVOS: End-to-End Recurrent Network for Video Object Segmentation
Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequenc…
GPUObjectOne-shot visual object segmentationSegmentation+4