UNINEXT
VNext
| UNINEXT | VNext | |
|---|---|---|
| 2 | 3 | |
| 1,443 | 592 | |
| - | - | |
| 5.5 | 5.1 | |
| 10 months ago | 2 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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UNINEXT
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[R] Universal Instance Perception as Object Discovery and Retrieval (Video Demo)
Hi, we have uploaded the complete paper (https://github.com/MasterBin-IIAU/UNINEXT/blob/master/assets/UNINEXT_Paper.pdf) to our repo. You can find more details in the paper :) About the first question, the input videos are NOT segmented aforehand and all target masks are predicted by our UNINEXT model. For SOT and VOS, we use target annotations (box or mask) from the first frame as the prompts. This helps UNINEXT to segment corresponding targets in the following frames.
VNext
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Current State Of The Art In Instance Segmentation?
Check out VNext, which is IDOL+seqformer, IDOL won the CVPR'22 video instance segmentation benchmark: https://github.com/wjf5203/VNext
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[R]VNext: Next-generation Video instance recognition framework(ECCV 2022 Oral * 2)
Very nice! I've not followed this line of research closely, so I'm wondering if the benchmarks and datasets consider entities that leave the frame temporarily? Like the yellow-labelled duck in the bottom-rigth of this gif: https://github.com/wjf5203/VNext/raw/main/assets/IDOL/vid_2.gif It starts at id=0 and ends at id=12 when it comes back into the frame.
What are some alternatives?
VolleyVision - Applying Deep Learning Approaches to Volleyball Data
ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions - The official code for our ECCV22 oral paper: tracking objects as pixel-wise distributions.
ailia-models - The collection of pre-trained, state-of-the-art AI models for ailia SDK
py-motmetrics - :bar_chart: Benchmark multiple object trackers (MOT) in Python
norfair - Lightweight Python library for adding real-time multi-object tracking to any detector.
TagMaps - Spatio-Temporal Tag and Photo Location Clustering for generating Tag Maps
unimatch - [TPAMI'23] Unifying Flow, Stereo and Depth Estimation
UniTrack - [NeurIPS'21] Unified tracking framework with a single appearance model. It supports Single Object Tracking (SOT), Video Object Segmentation (VOS), Multi-Object Tracking (MOT), Multi-Object Tracking and Segmentation (MOTS), Pose Tracking, Video Instance Segmentation (VIS), and class-agnostic MOT (e.g. TAO dataset).