Search Results for author:
Found 6 papers, 4 papers with code
Task-conditioned adaptation of visual features in multi-task policy learning
We evaluate the method on a wide variety of tasks from the CortexBench benchmark and show that, compared to existing work, it can be addressed with a single policy.
AutoNeRF: Training Implicit Scene Representations with Autonomous Agents
Empirical results show that NeRFs can be trained on actively collected data using just a single episode of experience in an unseen environment, and can be used for several downstream robotic tasks, and that modular trained exploration models outperform other classical and end-to-end baselines.
Multi-Object Navigation with dynamically learned neural implicit representations
Understanding and mapping a new environment are core abilities of any autonomously navigating agent.
An experimental study of the vision-bottleneck in VQA
We also study the impact of two methods to incorporate the information about objects necessary for answering a question, in the reasoning module directly, and earlier in the object selection stage.
Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation
In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently reach known goals.
DeepLPF: Deep Local Parametric Filters for Image Enhancement
We introduce a deep neural network, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image.
Ranked #8 on
Image Enhancement
on MIT-Adobe 5k
(SSIM on proRGB metric)
