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Found 28 papers, 15 papers with code
Mesure de similarité textuelle pour l’évaluation automatique de copies d’étudiants (Textual similarity measurement for automatic evaluation of students’ answers)
Cet article décrit la participation de l’équipe Nantalco à la tâche 2 du Défi Fouille de Textes 2021 (DEFT) : évaluation automatique de copies d’après une référence existante.
Classification automatique de questions spontanées vs. préparées dans des transcriptions de l’oral (Automatic Classification of Spontaneous vs)
Ce travail a pour objectif de développer un modèle linguistique pour classifier automatiquement des questions issues de transcriptions d’enregistrements provenant des corpus ESLO2 et ACSYNT en deux catégories “spontané” et “préparé”.
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models
Our work offers a more comprehensive financial knowledge evaluation benchmark, utilizing data of mock exams and covering a wide range of evaluated LLMs.
A Survey on Graph Classification and Link Prediction based on GNN
Traditional convolutional neural networks are limited to handling Euclidean space data, overlooking the vast realm of real-life scenarios represented as graph data, including transportation networks, social networks, and reference networks.
Knowledge Distillation for Efficient Sequences of Training Runs
We find that augmenting future runs with KD from previous runs dramatically reduces the time necessary to train these models, even taking into account the overhead of KD.
Deformer: Dynamic Fusion Transformer for Robust Hand Pose Estimation
Accurately estimating 3D hand pose is crucial for understanding how humans interact with the world.
Parallel Attention Interaction Network for Few-Shot Skeleton-Based Action Recognition
Learning discriminative features from very few labeled samples to identify novel classes has received increasing attention in skeleton-based action recognition.
HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration
The ability to learn from human demonstration endows robots with the ability to automate various tasks.
CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement
While category-level 9DoF object pose estimation has emerged recently, previous correspondence-based or direct regression methods are both limited in accuracy due to the huge intra-category variances in object shape and color, etc.
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition
Different from the conventional adversarial learning-based approaches for UDA, we utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets.
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
6D object pose estimation is a fundamental yet challenging problem in computer vision.
Tripartite: Tackle Noisy Labels by a More Precise Partition
Instead, we propose a Tripartite solution to partition training data more precisely into three subsets: hard, noisy, and clean.
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer
We interpolate between the source robot and the target robot by finding a continuous evolutionary change of robot parameters.
V-MAO: Generative Modeling for Multi-Arm Manipulation of Articulated Objects
Manipulating articulated objects requires multiple robot arms in general.
Sequential Voting with Relational Box Fields for Active Object Detection
While our voting function is able to improve the bounding box of the active object, one round of voting is typically not enough to accurately localize the active object.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation
To address this problem, we propose a novel continuous representation called Keypoint Distance Field (KDF) for projected 2D keypoint locations.
StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation
We present a large-scale stereo RGB image object pose estimation dataset named the $\textbf{StereOBJ-1M}$ dataset.
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering
Furthermore, we utilize differentiable Levenberg-Marquardt (LM) optimization to refine a pose fast and accurately by minimizing the feature-metric error between the input and rendered image representations without the need of zooming in.
Ranked #4 on
6D Pose Estimation using RGB
on LineMOD
Time-Efficient Mars Exploration of Simultaneous Coverage and Charging with Multiple Drones
This paper presents a time-efficient scheme for Mars exploration by the cooperation of multiple drones and a rover.
MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images
On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances.
Ranked #2 on
3D Instance Segmentation
on MitoEM
(AP75-R-Test metric)
KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects
We address two problems: first, we establish an easy method for capturing and labeling 3D keypoints on desktop objects with an RGB camera; and second, we develop a deep neural network, called $KeyPose$, that learns to accurately predict object poses using 3D keypoints, from stereo input, and works even for transparent objects.
MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences
Understanding dynamic 3D environment is crucial for robotic agents and many other applications.
Learning Video Representations from Correspondence Proposals
In particular, it can effectively learn representations for videos by mixing appearance and long-range motion with an RGB-only input.
FlowNet3D: Learning Scene Flow in 3D Point Clouds
In this work, we propose a novel deep neural network named $FlowNet3D$ that learns scene flow from point clouds in an end-to-end fashion.
Efficient Sparse-Winograd Convolutional Neural Networks
First, we move the ReLU operation into the Winograd domain to increase the sparsity of the transformed activations.
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Since memory reference is more than two orders of magnitude more expensive than arithmetic operations, the regularity of sparse structure leads to more efficient hardware design.
EIE: Efficient Inference Engine on Compressed Deep Neural Network
EIE has a processing power of 102GOPS/s working directly on a compressed network, corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of AlexNet at 1. 88x10^4 frames/sec with a power dissipation of only 600mW.
