ReMix: Training Generalized Person Re-identification on a Mixture of Data
Our WACV 2025 paper demonstrates that intelligently leveraging a mixture of heterogeneous data can significantly enhance the quality of generalized person Re-ID.
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Paper Review: TETRIS: Towards Exploring the Robustness of Interactive Segmentation
It turns out that modern benchmarks incorrectly assess the robustness of interactive segmentation methods.
Why does Apple call neural networks machine learning?
Interpretability and validity are key factors for Apple when choosing names for its products.
Why does AI draw the wrong number of fingers on a hand?
We explain why a task that is simple for humans is very difficult for modern artificial intelligence.
How are smartphone cameras similar to human vision?
The cameras of modern smartphones are very similar to our vision and brain. In this article, we will explain this similarity.
Apple wants to buy publishers' articles to train generative AI
Apple's approach differs significantly from that of other companies, which buy rights to content after training generative models.
Why does Apple call neural networks machine learning?
Interpretability and validity are key factors for Apple when choosing names for its products.
Paper Review: Real-World Humanoid Locomotion with Reinforcement Learning
Robots have learned to decide for themselves what and how to do it.
Gemini is Google's answer to GPT-4: the revolution failed
Google's new neural networks have impressive performance, but are inferior to OpenAI's GPT-4.
Meta and IBM launch the AI Alliance: pros and cons
The new initiative by large companies has several positive and negative aspects for the artificial intelligence industry and startups.
Why does AI draw the wrong number of fingers on a hand?
We explain why a task that is simple for humans is very difficult for modern artificial intelligence.
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