Papers Video Instance Segmentation
“Video Instance Segmentation” 태그가 달린 논문 163편 · 필터 해제
MoSAM: Motion-Guided Segment Anything Model with Spatial-Temporal Memory Selection
The recent Segment Anything Model 2 (SAM2) has demonstrated exceptional capabilities in interactive object segmentation for both images and videos. However, as a foundational model on interactive segmentation, SAM2 perfo…
Instance SegmentationInteractive SegmentationObjectObject Tracking+5RipVIS: Rip Currents Video Instance Segmentation Benchmark for Beach Monitoring and Safety
Rip currents are strong, localized and narrow currents of water that flow outwards into the sea, causing numerous beach-related injuries and fatalities worldwide. Accurate identification of rip currents remains challengi…
Instance SegmentationSegmentationSemantic SegmentationVideo Instance SegmentationA Temporal Modeling Framework for Video Pre-Training on Video Instance Segmentation
Contemporary Video Instance Segmentation (VIS) methods typically adhere to a pre-train then fine-tune regime, where a segmentation model trained on images is fine-tuned on videos. However, the lack of temporal knowledge …
Instance SegmentationSemantic SegmentationVideo Instance SegmentationDecoupled Motion Expression Video Segmentation
Motion expression video segmentation aims to segment objects based on input motion descriptions. Compared with traditional referring video object segmentation, it focuses on motion and multi-object expressions and is…
Instance SegmentationReferring Video Object SegmentationSegmentationSemantic Segmentation+4Minimizing Labeled, Maximizing Unlabeled: An Image-Driven Approach for Video Instance Segmentation
Traditional video instance segmentation (VIS) models rely on extensive per-frame video annotations, which are both time-consuming and costly. In this paper, we present MinMaxVIS, a novel VIS framework that reduces th…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationTowards Real-Time Open-Vocabulary Video Instance Segmentation
In this paper, we address the challenge of performing open-vocabulary video instance segmentation (OV-VIS) in real-time. We analyze the computational bottlenecks of state-of-the-art foundation models that performs OV-VIS…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationA2VIS: Amodal-Aware Approach to Video Instance Segmentation
Handling occlusion remains a significant challenge for video instance-level tasks like Multiple Object Tracking (MOT) and Video Instance Segmentation (VIS). In this paper, we propose a novel framework, Amodal-Aware Video…
Instance SegmentationMultiple Object TrackingObjectObject Tracking+3SyncVIS: Synchronized Video Instance Segmentation
Recent DETR-based methods have advanced the development of Video Instance Segmentation (VIS) through transformers' efficiency and capability in modeling spatial and temporal information. Despite harvesting remarkable pro…
Instance SegmentationSegmentationSemantic SegmentationVideo Instance SegmentationSelf-supervised Video Instance Segmentation Can Boost Geographic Entity Alignment in Historical Maps
Tracking geographic entities from historical maps, such as buildings, offers valuable insights into cultural heritage, urbanization patterns, environmental changes, and various historical research endeavors. However, lin…
Entity AlignmentInstance SegmentationSelf-Supervised LearningSemantic Segmentation+1SDI-Paste: Synthetic Dynamic Instance Copy-Paste for Video Instance Segmentation
Data augmentation methods such as Copy-Paste have been studied as effective ways to expand training datasets while incurring minimal costs. While such methods have been extensively implemented for image level tasks, we f…
Data AugmentationInstance SegmentationMORPHSemantic Segmentation+1Foundation Models for Amodal Video Instance Segmentation in Automated Driving
In this work, we study amodal video instance segmentation for automated driving. Previous works perform amodal video instance segmentation relying on methods trained on entirely labeled video data with techniques borrowe…
Amodal Instance SegmentationInstance SegmentationPoint TrackingSegmentation+2Are Deep Learning Models Robust to Partial Object Occlusion in Visual Recognition Tasks?
Image classification models, including convolutional neural networks (CNNs), perform well on a variety of classification tasks but struggle under conditions of partial occlusion, i.e., conditions in which objects are par…
Data Augmentationimage-classificationImage ClassificationInstance Segmentation+2MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Mice
Enabled by large annotated datasets, tracking and segmentation of objects in videos has made remarkable progress in recent years. Despite these advancements, algorithms still struggle under degraded conditions and during…
Instance SegmentationSegmentationSemantic SegmentationVideo Instance SegmentationImproving Weakly-supervised Video Instance Segmentation by Leveraging Spatio-temporal Consistency
The performance of Video Instance Segmentation (VIS) methods has improved significantly with the advent of transformer networks. However, these networks often face challenges in training due to the high annotation cost. …
Instance SegmentationSegmentationSemantic SegmentationVideo Instance SegmentationLSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS
Video Object Segmentation (VOS) presents several challenges, including object occlusion and fragmentation, the dis-appearance and re-appearance of objects, and tracking specific objects within crowded scenes. In this wor…
Instance SegmentationObjectSegmentationSemantic Segmentation+3Unified Embedding Alignment for Open-Vocabulary Video Instance Segmentation
Open-Vocabulary Video Instance Segmentation (VIS) is attracting increasing attention due to its ability to segment and track arbitrary objects. However, the recent Open-Vocabulary VIS attempts obtained unsatisfactory res…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationZero-shot GeneralizationContext-Aware Video Instance Segmentation
In this paper, we introduce the Context-Aware Video Instance Segmentation (CAVIS), a novel framework designed to enhance instance association by integrating contextual information adjacent to each object. To efficiently …
Instance SegmentationPanoptic SegmentationSegmentationSemantic Segmentation+2PM-VIS+: High-Performance Video Instance Segmentation without Video Annotation
Video instance segmentation requires detecting, segmenting, and tracking objects in videos, typically relying on costly video annotations. This paper introduces a method that eliminates video annotations by utilizing ima…
Instance SegmentationSegmentationSemantic SegmentationVideo Instance Segmentation2nd Place Solution for MeViS Track in CVPR 2024 PVUW Workshop: Motion Expression guided Video Segmentation
Motion Expression guided Video Segmentation is a challenging task that aims at segmenting objects in the video based on natural language expressions with motion descriptions. Unlike the previous referring video object se…
Instance SegmentationReferring Video Object SegmentationSegmentationSemantic Segmentation+4UVIS: Unsupervised Video Instance Segmentation
Video instance segmentation requires classifying, segmenting, and tracking every object across video frames. Unlike existing approaches that rely on masks, boxes, or category labels, we propose UVIS, a novel Unsupervised…
Instance SegmentationLanguage ModellingPseudo LabelSegmentation+2