ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions
The official code for our ECCV22 oral paper: tracking objects as pixel-wise distributions. (by dvlab-research)
VNext
Next-generation Video instance recognition framework on top of Detectron2 which supports InstMove (CVPR 2023), SeqFormer(ECCV Oral), and IDOL(ECCV Oral)) (by wjf5203)
| ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions | VNext | |
|---|---|---|
| 1 | 3 | |
| 157 | 593 | |
| 0.0% | - | |
| 0.0 | 5.1 | |
| over 1 year ago | 3 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions
Posts with mentions or reviews of ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions.
We have used some of these posts to build our list of alternatives
and similar projects.
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[D] Approaches to new code: create a map of the code structure, does it make sense?
An example can be found here: https://github.com/dvlab-research/ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions/raw/main/figs/model_mind_flow.png
VNext
Posts with mentions or reviews of VNext.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-06.
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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?
When comparing ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions and VNext you can also consider the following projects:
Unicorn - [ECCV'22 Oral] Towards Grand Unification of Object Tracking
UNINEXT - [CVPR'23] Universal Instance Perception as Object Discovery and Retrieval
py-motmetrics - :bar_chart: Benchmark multiple object trackers (MOT) in Python
classy-sort-yolov5 - Ready-to-use realtime multi-object tracker that works for any object category. YOLOv5 + SORT implementation.
deep_sort_pytorch - MOT using deepsort and yolov3 with pytorch
ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions vs Unicorn
VNext vs UNINEXT
ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions vs py-motmetrics
ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions vs classy-sort-yolov5
ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions vs deep_sort_pytorch