We will soon present at BMVC 2022 our work “Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach”. This work presents a novel algorithm for harnessing synthetic data in order to improve semantic segmentation in real images.

Our work is freely available here. Our source code, simulator, and new dataset can also be obtained freely at https://github.com/lsmcolab/Semantic-Segmentation-under-Adverse-Conditions.

Our work “Congestion control algorithms for robotic swarms with a common target based on the throughput of the target area” is already available on-line in the journal Robotics and Autonomous Systems. It presents novel algorithms for handling hard congestion situations when thousands of robots move towards a common target. This work is inspired by our previous theoretical work, but it is now focused on presenting algorithms for real robots.

Our author version is freely available at http://www.lancaster.ac.uk/staff/sorianom/Algorithms2023.pdf. The publisher final version is at https://www.sciencedirect.com/science/article/pii/S0921889022001737.

You can also find our source code freely available at https://github.com/yuri-tavares/swarm-common-target-area-congestion.

Our work “3DVerifier: Efficient Robustness Verification for 3D Point Cloud Models” was accepted in the journal Machine Learning. The paper is freely available at https://link.springer.com/article/10.1007/s10994-022-06235-3. It presents a novel method to verify the robustness against attacks of machine learning models aimed at classifying 3D Point Cloud data.

Our tool, 3DVerifier is also released, including its source code. You can find it at https://github.com/TrustAI/3DVerifier.