Papers Video Instance Segmentation
“Video Instance Segmentation” 태그가 달린 논문 163편 · 필터 해제
1st Place Winner of the 2024 Pixel-level Video Understanding in the Wild (CVPR'24 PVUW) Challenge in Video Panoptic Segmentation and Best Long Video Consistency of Video Semantic Segmentation
The third Pixel-level Video Understanding in the Wild (PVUW CVPR 2024) challenge aims to advance the state of art in video understanding through benchmarking Video Panoptic Segmentation (VPS) and Video Semantic Segmentat…
BenchmarkingInstance SegmentationPanoptic SegmentationScene Parsing+6PM-VIS: High-Performance Box-Supervised Video Instance Segmentation
Labeling pixel-wise object masks in videos is a resource-intensive and laborious process. Box-supervised Video Instance Segmentation (VIS) methods have emerged as a viable solution to mitigate the labor-intensive annotat…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationOW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning
We propose the new task 'open-world video instance segmentation and captioning'. It requires to detect, segment, track and describe with rich captions never before seen objects. This challenging task can be addressed by …
DescriptiveDiversityInstance SegmentationLanguage Modeling+7What is Point Supervision Worth in Video Instance Segmentation?
Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos. Conventional VIS methods rely on densely-annotated object masks which are expensive. We reduce the…
Instance SegmentationObjectSemantic SegmentationVideo Instance SegmentationDVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries
Modern video segmentation methods adopt object queries to perform inter-frame association and demonstrate satisfactory performance in tracking continuously appearing objects despite large-scale motion and transient occlu…
ObjectVideo Instance SegmentationVideo SegmentationVideo Semantic SegmentationInternVideo2: Scaling Foundation Models for Multimodal Video Understanding
We introduce InternVideo2, a new family of video foundation models (ViFM) that achieve the state-of-the-art results in video recognition, video-text tasks, and video-centric dialogue. Our core design is a progressive tra…
Action ClassificationAction RecognitionAudio ClassificationContrastive Learning+13CLIP-VIS: Adapting CLIP for Open-Vocabulary Video Instance Segmentation
Open-vocabulary video instance segmentation strives to segment and track instances belonging to an open set of categories in a videos. The vision-language model Contrastive Language-Image Pre-training (CLIP) has shown ro…
DecoderInstance SegmentationLanguage ModellingSegmentation+4UniVS: 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+6TDViT: Temporal Dilated Video Transformer for Dense Video Tasks
Deep video models, for example, 3D CNNs or video transformers, have achieved promising performance on sparse video tasks, i.e., predicting one result per video. However, challenges arise when adapting existing deep video…
Instance Segmentationobject-detectionObject DetectionSemantic Segmentation+2Spatio-temporal Prompting Network for Robust Video Feature Extraction
Frame quality deterioration is one of the main challenges in the field of video understanding. To compensate for the information loss caused by deteriorated frames, recent approaches exploit transformer-based integration…
Instance Segmentationobject-detectionObject DetectionObject Tracking+5Instance Brownian Bridge as Texts for Open-vocabulary Video Instance Segmentation
Temporally locating objects with arbitrary class texts is the primary pursuit of open-vocabulary Video Instance Segmentation (VIS). Because of the insufficient vocabulary of video data, previous methods leverage image-te…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationDVIS++: Improved Decoupled Framework for Universal Video Segmentation
We present the \textbf{D}ecoupled \textbf{VI}deo \textbf{S}egmentation (DVIS) framework, a novel approach for the challenging task of universal video segmentation, including video instance segmentation (VIS), video seman…
Contrastive LearningDenoisingInstance SegmentationPanoptic Segmentation+6General Object Foundation Model for Images and Videos at Scale
We present GLEE in this work, an object-level foundation model for locating and identifying objects in images and videos. Through a unified framework, GLEE accomplishes detection, segmentation, tracking, grounding, and i…
Instance SegmentationLong-tail Video Object SegmentationMulti-Object TrackingObject+8TMT-VIS: Taxonomy-aware Multi-dataset Joint Training for Video Instance Segmentation
Training on large-scale datasets can boost the performance of video instance segmentation while the annotated datasets for VIS are hard to scale up due to the high labor cost. What we possess are numerous isolated filed-…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationVISAGE: Video Instance Segmentation with Appearance-Guided Enhancement
In recent years, online Video Instance Segmentation (VIS) methods have shown remarkable advancement with their powerful query-based detectors. Utilizing the output queries of the detector at the frame-level, these method…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationVideo Instance Matting
Conventional video matting outputs one alpha matte for all instances appearing in a video frame so that individual instances are not distinguished. While video instance segmentation provides time-consistent instance mask…
BinarizationImage MattingInstance SegmentationSemantic Segmentation+2CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation
The advancement of computer vision has pushed visual analysis tasks from still images to the video domain. In recent years, video instance segmentation, which aims to track and segment multiple objects in video frames, h…
Autonomous DrivingInstance SegmentationMulti-Object TrackingMulti-Object Tracking and Segmentation+7Deep Learning Techniques for Video Instance Segmentation: A Survey
Video instance segmentation, also known as multi-object tracking and segmentation, is an emerging computer vision research area introduced in 2019, aiming at detecting, segmenting, and tracking instances in videos simult…
Action RecognitionDeep LearningInstance SegmentationMulti-Object Tracking+7TCOVIS: Temporally Consistent Online Video Instance Segmentation
In recent years, significant progress has been made in video instance segmentation (VIS), with many offline and online methods achieving state-of-the-art performance. While offline methods have the advantage of producing…
Instance SegmentationSemantic SegmentationVideo Instance SegmentationNOVIS: A Case for End-to-End Near-Online Video Instance Segmentation
Until recently, the Video Instance Segmentation (VIS) community operated under the common belief that offline methods are generally superior to a frame by frame online processing. However, the recent success of online me…
Instance SegmentationSegmentationSemantic SegmentationVideo Instance Segmentation