Papers Video Instance Segmentation
“Video Instance Segmentation” 태그가 달린 논문 163편 · 필터 해제
VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation
Existing approaches to unsupervised video instance segmentation typically rely on motion estimates and experience difficulties tracking small or divergent motions. We present VideoCutLER, a simple method for unsupervised…
Instance SegmentationOptical Flow EstimationSegmentationSemantic Segmentation+31st Place Solution for the 5th LSVOS Challenge: Video Instance Segmentation
Video instance segmentation is a challenging task that serves as the cornerstone of numerous downstream applications, including video editing and autonomous driving. In this report, we present further improvements to the…
Autonomous DrivingDenoisingInstance SegmentationObject Tracking+41st Place Solution for CVPR2023 BURST Long Tail and Open World Challenges
Currently, Video Instance Segmentation (VIS) aims at segmenting and categorizing objects in videos from a closed set of training categories that contain only a few dozen of categories, lacking the ability to handle diver…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationCTVIS: Consistent Training for Online Video Instance Segmentation
The discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS). Instance embedding learning is directly supervised by the contrastive loss c…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationLearning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation
Transformer-based detection and segmentation methods use a list of learned detection queries to retrieve information from the transformer network and learn to predict the location and category of one specific object from…
Instance SegmentationObjectobject-detectionObject Detection+4Segment 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+43rd Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation
In order to deal with the task of video panoptic segmentation in the wild, we propose a robust integrated video panoptic segmentation solution. In our solution, we regard the video panoptic segmentation task as a segment…
Instance SegmentationPanoptic SegmentationSegmentationSemantic Segmentation+3RefineVIS: Video Instance Segmentation with Temporal Attention Refinement
We introduce a novel framework called RefineVIS for Video Instance Segmentation (VIS) that achieves good object association between frames and accurate segmentation masks by iteratively refining the representations using…
Contrastive LearningDenoisingInstance SegmentationSegmentation+2DVIS: Decoupled Video Instance Segmentation Framework
Video instance segmentation (VIS) is a critical task with diverse applications, including autonomous driving and video editing. Existing methods often underperform on complex and long videos in real world, primarily due …
Autonomous DrivingGPUInstance SegmentationSegmentation+4GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation
Recent trends in Video Instance Segmentation (VIS) have seen a growing reliance on online methods to model complex and lengthy video sequences. However, the degradation of representation and noise accumulation of the onl…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationOpenVIS: Open-vocabulary Video Instance Segmentation
Open-vocabulary Video Instance Segmentation (OpenVIS) can simultaneously detect, segment, and track arbitrary object categories in a video, without being constrained to categories seen during training. In this work, we p…
Instance SegmentationSegmentationSemantic SegmentationVideo Instance SegmentationTowards Open-Vocabulary Video Instance Segmentation
Video Instance Segmentation (VIS) aims at segmenting and categorizing objects in videos from a closed set of training categories, lacking the generalization ability to handle novel categories in real-world videos. To add…
Instance SegmentationSegmentationSemantic SegmentationVideo Instance Segmentation+1Video Instance Segmentation in an Open-World
Existing video instance segmentation (VIS) approaches generally follow a closed-world assumption, where only seen category instances are identified and spatio-temporally segmented at inference. Open-world formulation rel…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationMobileInst: Video Instance Segmentation on the Mobile
Video instance segmentation on mobile devices is an important yet very challenging edge AI problem. It mainly suffers from (1) heavy computation and memory costs for frame-by-frame pixel-level instance perception and (2)…
CPUDecoderInstance SegmentationSegmentation+2Mask-Free Video Instance Segmentation
The recent advancement in Video Instance Segmentation (VIS) has largely been driven by the use of deeper and increasingly data-hungry transformer-based models. However, video masks are tedious and expensive to annotate, …
Instance SegmentationOptical Flow EstimationPatch MatchingSegmentation+2BoxVIS: Video Instance Segmentation with Box Annotations
It is expensive and labour-extensive to label the pixel-wise object masks in a video. As a result, the amount of pixel-wise annotations in existing video instance segmentation (VIS) datasets is small, limiting the genera…
Instance SegmentationSemantic SegmentationvalidVideo Instance SegmentationMDQE: Mining Discriminative Query Embeddings to Segment Occluded Instances on Challenging Videos
While impressive progress has been achieved, video instance segmentation (VIS) methods with per-clip input often fail on challenging videos with occluded objects and crowded scenes. This is mainly because instance querie…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationTube-Link: A Flexible Cross Tube Framework for Universal Video Segmentation
Video segmentation aims to segment and track every pixel in diverse scenarios accurately. In this paper, we present Tube-Link, a versatile framework that addresses multiple core tasks of video segmentation with a unified…
Contrastive LearningSegmentationVideo Instance SegmentationVideo Panoptic Segmentation+2Universal Instance Perception as Object Discovery and Retrieval
All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independen…
Described Object DetectionGeneralized Referring Expression ComprehensionInstance SegmentationMulti-Object Tracking and Segmentation+16Offline-to-Online Knowledge Distillation for Video Instance Segmentation
In this paper, we present offline-to-online knowledge distillation (OOKD) for video instance segmentation (VIS), which transfers a wealth of video knowledge from an offline model to an online model for consistent predict…
Data AugmentationInstance SegmentationKnowledge DistillationSemantic Segmentation+1