Key Instance Selection for Unsupervised Video Object Segmentation
This paper proposes key instance selection based on video saliency covering objectness and dynamics for unsupervised video object segmentation (UVOS). Our method takes frames sequentially and extracts object proposals with corresponding masks for each frame. We link objects according to their similarity until the M-th frame and then assign them unique IDs (i.e., instances). Similarity measure takes into account multiple properties such as ReID descriptor, expected trajectory, and semantic co-segmentation result. After M-th frame, we select K IDs based on video saliency and frequency of appearance; then only these key IDs are tracked through the remaining frames. Thanks to these technical contributions, our results are ranked third on the leaderboard of UVOS DAVIS challenge.
Code (0)
등록된 구현이 없습니다.
Tasks
ObjectSegmentationSemantic SegmentationUnsupervised Video Object SegmentationVideo Object SegmentationVideo Semantic SegmentationSimilar Papers 제목 키워드 기반
Instance Embedding Transfer to Unsupervised Video Object Segmentation
We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks. The instance embedding network produces an embedding vector for each p…
ObjectOptical Flow EstimationSegmentationSemantic Segmentation+3Mask Selection and Propagation for Unsupervised Video Object Segmentation
In this work we present a novel approach for Unsupervised Video Object Segmentation, that is automatically generating instance level segmentation masks for salient objects and tracking them in a video. We efficiently han…
SegmentationSemantic SegmentationUnsupervised Video Object SegmentationVideo Object Segmentation+1Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
One major technique debt in video object segmentation is to label the object masks for training instances. As a result, we propose to prepare inexpensive, yet high quality pseudo ground truth corrected with motion cue fo…
Instance SegmentationObjectObject TrackingSegmentation+4S2D: Sparse-To-Dense Keymask Distillation for Unsupervised Video Instance Segmentation
In recent years, the state-of-the-art in unsupervised video instance segmentation has heavily relied on synthetic video data, generated from object-centric image datasets such as ImageNet. However, video synthesis by art…
Unsupervised Instance SegmentationVideo Instance SegmentationUVIS: 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