Hi everyone,
I registered for CVPR 2026, but I haven’t seen any official mobile app announcement yet for Android/iOS.
Is there any official CVPR 2026 app released or expected soon for schedules, networking, workshops, etc.?
Would appreciate if anyone has details or download links. Thanks!
Something that has been on my mind lately:
Humans can usually get used to a place and learn fast with just a little bit of experience.
For example a person can figure out rooms, objects, obstacles and how things move around after seeing just a few examples.
Physical AI systems seem to need a huge amount of real-world data, simulation, retraining and coverage of all the edge cases before they work well.
Then small changes in the environment can still cause them to fail.
Some examples of these changes include:
Is the main reason for this that current systems still don't really understand space and the world around them?
Do we really need a lot of different kinds of data, for AI systems that interact with the world?
Been reading more about Physical AI lately and one thing that keeps standing out is how different real-world deployment is compared to benchmark testing.
A lot of Vision AI systems seem fine in controlled environments, but once they move into real spaces:
performance drops pretty quickly.
It feels like the challenge is becoming less about raw detection accuracy and more about contextual understanding + multimodal data quality.
This article had an interesting breakdown of how Vision AI is being used to help Physical AI systems interpret real-world environments beyond just frame-by-frame detection:
Physical AI: How Vision AI Helps Machines Understand the Real World
Curious whether people here think current Vision AI architectures are enough for long-term Physical AI systems, or if we still need fundamentally different approaches for scene understanding and real-world adaptation.
Been experimenting with a few CV models recently and something keeps bothering me.
A model can look great during testing, but once you put it into actual real-world conditions, performance drops way more than expected.
Stuff like:
seems to affect results a lot more than model benchmarks suggest.
Starting to wonder if dataset quality/diversity is becoming a bigger problem than the models themselves.
Curious how people here handle this in production systems, especially around edge cases and maintaining high-quality training data over time.
Been reading more Physical AI/robotics case studies lately, and one thing that keeps standing out is how much of the challenge is actually around data collection rather than the models themselves.
A lot of the work seems to involve:
Interesting to see how different teams are approaching this compared to traditional ML pipelines.
I came across a case study recently around Physical AI data workflows that touched on some of these issues:
[https://www.shaip.com/scaling-physical-ai-and-humanoid-robotics-case-study/\]
Curious whether people here think simulation will eventually reduce the need for large-scale real-world collection, or if real-world data remains the long-term moat.
Most of the conversation around Physical AI seem to be around models and reasoning but the harder problem may be gathering enough real world multimodal data (video, motion, sensor data, interactions, edge cases etc.) at scale.
Do people think Physical AI is currently more limited by models or by the difficulty of building high-quality real-world data pipelines out here?