The goal of the Kinetics dataset is to help the computer vision and machine learning communities advance models for video understanding. Given this large human action classification dataset, it may be possible to learn powerful video representations that transfer to different video tasks.
The Kinetics-700-2020 dataset will be used for this challenge. Kinetics-700-2020 is a large-scale, high-quality dataset of YouTube video URLs which include a diverse range of human focused actions. The aim of the Kinetics dataset is to help the machine learning community create more advanced models for video understanding. It is an approximate super-set of both Kinetics-400, released in 2017, Kinetics-600, released in 2018 and Kinetics-700, released in 2019.
The dataset consists of approximately 650,000 video clips, and covers 700 human action classes with at least 700 video clips for each action class. Each clip lasts around 10 seconds and is labeled with a single class. All of the clips have been through multiple rounds of human annotation, and each is taken from a unique YouTube video. The actions cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands and hugging.
More information about how to download the Kinetics dataset is available here.
Reflective boards (e.g., ENIG finish with black solder mask) can cause glare. Use 3D MIBA’s multi-angle projection to reduce specular reflections. Run a "lighting calibration" for each board panel.
The introduction of 3D printing technology has transformed the model building landscape, enabling enthusiasts to create complex and intricate designs with unprecedented precision and accuracy. 3D printing allows model builders to produce parts and entire models quickly and efficiently, reducing the need for traditional modeling techniques such as casting, machining, and manual sculpting.
examines how material properties and printing parameters influence the shrinkage of metal parts during the sintering phase, a critical challenge for Miba's precision components ResearchGate Multi-Material Printing : Recent papers focus on design guidelines for multi-metal parts
provides the height data, the low false-call rates, and the actionable insights that modern factories need. Whether you are manufacturing smartphones, medical implants, or automotive radars, implementing 3D MIBA is not merely an upgrade; it is a competitive necessity. 3d miba
Transitioning to 3D MIBA requires more than purchasing hardware. Follow these best practices for successful deployment:
Why are leading electronics manufacturers rushing to adopt 3D MIBA? The answer lies in three high-stakes applications.
3D MIBA generates massive amounts of data. Connect the system to your MES to enable: Reflective boards (e
While "3D Miba" is not a single specific paper title, the following research themes define the field Miba operates in: Sinter-Based Additive Manufacturing : Studies such as the review on Sinter-based AM of hardmetals
If you meant something else, please clarify:
If you're interested in exploring 3D MIBA, here are some steps to get started: The introduction of 3D printing technology has transformed
Unlike 2D, which only sees surface area, 3D MIBA detects insufficient paste (which leads to head-in-pillow defects) and excessive paste (which causes shorts).
solves these issues by adding a Z-axis (height) dimension. Using techniques such as laser triangulation, structured blue light, or phase-shifting moiré, 3D MIBA creates a topographic map of the circuit board. This allows the system to measure component standoff heights, verify coplanarity, and inspect solder joints under components like Ball Grid Arrays (BGAs) and Quad Flat No-leads (QFNs).
1. Possible to use ImageNet checkpoints?
We allow finetuning from public ImageNet checkpoints for the supervised track -- but a link to the specific checkpoint should be provided with each submission.
2. Possible to use optical flow?
Flow can be used as long as not trained on external datasets, except if they are synthetic.
3. Can we train on test data without labels (e.g. transductive)?
No.
4. Can we use semantic class label information?
Yes, for the supervised track.
5. Will there be special tracks for methods using fewer FLOPs / small models or just RGB vs RGB+Audio in the self-supervised track?
We will ask participants to provide the total number of model parameters and the modalities used and plan to create special mentions for those doing well in each setting, but not specific tracks.