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.
Our work “On-line estimators for ad-hoc task execution: learning types and parameters of teammates for effective teamwork” was recently published at the Journal Autonomous Agents and Multi-Agent Systems (JAAMAS). It presents OEATE, a novel algorithm for online estimation of teammates’ type and parameters in decentralised task execution.
The paper is freely available at https://link.springer.com/article/10.1007/s10458-022-09571-9. The source code, built using our AdLeap-MAS framework, is available on Github.
We recently published an extensive theoretical study on the common target problem for swarm robotics. Our work, “On the Throughput of the Common Target Area for Robotic Swarm Strategies”, was published at Mathematica, and is freely available here.
The source code of our experimented strategies are available at https://github.com/yuri-tavares/swarm-strategies.
In collaboration with friends of Federal University of Viçosa (UFV), we investigated machine learning techniques in order to find potential existing drugs that could be used to inhibit the Covid-19 virus. The work, “Computational prediction of potential inhibitors for SARS-COV-2 main protease based on machine learning, docking, MM-PBSA calculations, and metadynamics” was published at PLOS ONE, and it is available here.
Source code and datasets are available in the Github repository at https://github.com/IsabelaGomes/Prediction_SARSCOV2_inhibitors.
We have released the AdLeap-MAS Framework, aimed at facilitating experiments on ad-hoc teamwork or opponent modeling, when an agent uses a model of others during decision-making. It was released as a demo paper at AAMAS, please find our paper here.
This video introduces our framework:
The framework and documentation is available in the Github repository at https://github.com/lsmcolab/adleap-mas/.
Our work “Text-driven video acceleration: A weakly-supervised reinforcement learning method” was accepted at the IEEE Transactions on Pattern Analysis and Machine Intelligence. The paper presents a novel method to automatically accelerate instructional videos based on the corresponding textual instructions. Check our video below:
The paper is available here. The source code of our algorithm is also available on Github at https://github.com/verlab/TextDrivenVideoAcceleration_TPAMI_2022.
Our BMVC paper “Sparse Adversarial Video Attacks with Spatial Transformations” is now available here. Our paper presents a novel adversarial attack strategy on videos, using both additive perturbation and spatial transformation in a unified optimisation framework.
Check our video below:
Our code is also openly available at https://github.com/TrustAI/DeepSAVA.
Our recent work “A Meta-interactive Compositional Approach that Fosters Musical Emergence through Ludic Expressivity” will be presented soon at CHI Play. The paper is now freely available here. Check our video below!
For more information, and to download our demo, check the Bubble Sounds project home page.