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Found 18 papers, 7 papers with code
Flow as the Cross-Domain Manipulation Interface
By leveraging real-world human videos and simulated robot play data, we bypass the challenges of teleoperating physical robots in the real world, resulting in a scalable system for diverse tasks.
ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data
In addition, we show that our system can generalize to unseen in-the-wild environments, by learning from diverse in-the-wild human demonstrations.
Neural Control: Concurrent System Identification and Control Learning with Neural ODE
To address this, we propose a neural ODE based method for controlling unknown dynamical systems, denoted as Neural Control (NC), which combines dynamics identification and optimal control learning using a coupled neural ODE.
XSkill: Cross Embodiment Skill Discovery
Human demonstration videos are a widely available data source for robot learning and an intuitive user interface for expressing desired behavior.
Exploiting Implicit Rigidity Constraints via Weight-Sharing Aggregation for Scene Flow Estimation from Point Clouds
Scene flow estimation, which predicts the 3D motion of scene points from point clouds, is a core task in autonomous driving and many other 3D vision applications.
IHNet: Iterative Hierarchical Network Guided by High-Resolution Estimated Information for Scene Flow Estimation
This approach circulates high-resolution estimated information (scene flow and feature) from the preceding iteration back to the low-resolution layer of the current iteration.
Don't overfit the history -- Recursive time series data augmentation
We apply RIM to diverse real world time series cases to achieve strong performance over non-augmented data on regression, classification, and reinforcement learning tasks.
A Deep Reinforcement Learning Framework For Column Generation
Column Generation (CG) is an iterative algorithm for solving linear programs (LPs) with an extremely large number of variables (columns).
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion
In this paper, we show that effective feature-level collaboration of the networks for the three respective tasks could achieve much greater performance improvement for all three tasks than only loss-level joint optimization.
GarmentNets: Category-Level Pose Estimation for Garments via Canonical Space Shape Completion
By mapping the observed partial surface to the canonical space and completing it in this space, the output representation describes the garment's full configuration using a complete 3D mesh with the per-vertex canonical coordinate label.
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder
The proposed module is named \emph{bridging visual representations} (BVR).
Ranked #65 on
Object Detection
on COCO test-dev
Loss Function Search for Face Recognition
In face recognition, designing margin-based (e. g., angular, additive, additive angular margins) softmax loss functions plays an important role in learning discriminative features.
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them.
Ranked #37 on
Object Detection
on COCO-O
Relational Learning for Joint Head and Human Detection
Head and human detection have been rapidly improved with the development of deep convolutional neural networks.
PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes
Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians.
RefineFace: Refinement Neural Network for High Performance Face Detection
To improve the classification ability for high recall efficiency, STC first filters out most simple negatives from low level detection layers to reduce search space for subsequent classifier, then SML is applied to better distinguish faces from background at various scales and FSM is introduced to let the backbone learn more discriminative features for classification.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
Selective Refinement Network for High Performance Face Detection
In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module.
Ranked #1 on
Face Detection
on PASCAL Face
