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Found 18 papers, 5 papers with code
Distilling ChatGPT for Explainable Automated Student Answer Assessment
Traditional methods of automating student answer assessment through text classification often suffer from issues such as lack of trustworthiness, transparency, and the ability to provide a rationale for the automated assessment process.
Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation
We demonstrate that appropriate placement of our parameter efficient adapters can significantly reduce the performance gap between frozen pretrained representations and full end-to-end fine-tuning without changes to the original representation and thus preserving original capabilities of the pretrained model.
How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation
Our analysis, both in simulation and in the real world, shows that our approach is the best across data budgets, while standard offline RL from teacher rollouts is surprisingly effective when enough data is given.
TCM-SD: A Benchmark for Probing Syndrome Differentiation via Natural Language Processing
Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system -- syndrome differentiation (SD) -- and we introduce the first public large-scale dataset for SD, called TCM-SD.
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
We study the problem of robotic stacking with objects of complex geometry.
Ranked #2 on
Skill Generalization
on RGB-Stacking
To be Closer: Learning to Link up Aspects with Opinions
Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA).
Learning rich touch representations through cross-modal self-supervision
The sense of touch is fundamental in several manipulation tasks, but rarely used in robot manipulation.
Self-Supervised Learning
Robotics
Learning Dexterous Manipulation from Suboptimal Experts
Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous action spaces often fail to effectively utilize combinations of highly off-policy expert data and on-policy exploration data.
Deep Residual Dense U-Net for Resolution Enhancement in Accelerated MRI Acquisition
Accelerated MRI scan may be achieved by acquiring less amount of k-space data (down-sampling in the k-space).
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation
In this work, we learn a latent state representation implicitly with deep reinforcement learning in simulation, and then adapt it to the real domain using unlabeled real robot data.
RetinaFace: Single-stage Dense Face Localisation in the Wild
Face Analysis Project on MXNet
Ranked #2 on
Face Detection
on WIDER Face (Medium)
Dense 3D Face Decoding over 2500FPS: Joint Texture & Shape Convolutional Mesh Decoders
3D Morphable Models (3DMMs) are statistical models that represent facial texture and shape variations using a set of linear bases and more particular Principal Component Analysis (PCA).
MeshGAN: Non-linear 3D Morphable Models of Faces
Generative Adversarial Networks (GANs) are currently the method of choice for generating visual data.
DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild
We define the regression task in terms of the intrinsic, U-V coordinates of a 3D deformable model that is brought into correspondence with image instances at training time.
UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition
We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance.
The Unconstrained Ear Recognition Challenge
In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions.
Joint Multi-view Face Alignment in the Wild
This encompasses two basic problems: i) the detection and deformable fitting steps are performed independently, while the detector might not provide best-suited initialisation for the fitting step, ii) the face appearance varies hugely across different poses, which makes the deformable face fitting very challenging and thus distinct models have to be used (\eg, one for profile and one for frontal faces).
Estimating Correspondences of Deformable Objects "In-The-Wild"
In this paper, we show for the first time, to the best of our knowledge, that it is possible to construct SDMs by putting object shapes in dense correspondence.
