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Found 6 papers, 4 papers with code
Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models
In this paper, we study Imitation Learning from Observation with pretrained models and find existing approaches such as BCO and AIME face knowledge barriers, specifically the Embodiment Knowledge Barrier (EKB) and the Demonstration Knowledge Barrier (DKB), greatly limiting their performance.
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
Our method is "zero-shot" in the sense that it does not require further training for the world model or online interactions with the environment after given the demonstration.
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
We evaluate existing offline RL algorithms on NeoRL and argue that the performance of a policy should also be compared with the deterministic version of the behavior policy, instead of the dataset reward.
Classification-driven Single Image Dehazing
The generated images generally have better visual appeal, but not always have better performance for high-level vision tasks, e. g. image classification.
Pixel-wise Regression: 3D Hand Pose Estimation via Spatial-form Representation and Differentiable Decoder
To use our method, we build a model, in which we design a particular SFR and its correlative DD which divided the 3D joint coordinates into two parts, plane coordinates and depth coordinates and use two modules named Plane Regression (PR) and Depth Regression (DR) to deal with them respectively.
Ranked #6 on
Hand Pose Estimation
on HANDS 2017
Effects of Image Degradations to CNN-based Image Classification
In this paper, we empirically study this problem for four kinds of degraded images -- hazy images, underwater images, motion-blurred images and fish-eye images.
