Search Results for author:
Found 38 papers, 24 papers with code
Simulate Bumblebee and Extend It to Support LE Coded PHY in BLE version 5
Subsequently, we extend Bumblebee to support LE Coded PHY in BLE version 5 and conduct experiments to verify its performance.
DQS3D: Densely-matched Quantization-aware Semi-supervised 3D Detection
While this paradigm is natural for image-level or pixel-level prediction, adapting it to the detection problem is challenged by the issue of proposal matching.
Delving into Shape-aware Zero-shot Semantic Segmentation
Thanks to the impressive progress of large-scale vision-language pretraining, recent recognition models can classify arbitrary objects in a zero-shot and open-set manner, with a surprisingly high accuracy.
STRAP: Structured Object Affordance Segmentation with Point Supervision
By label affinity, we refer to affordance segmentation as a multi-label prediction problem: A plate can be both holdable and containable.
DPF: Learning Dense Prediction Fields with Weak Supervision
We showcase the effectiveness of DPFs using two substantially different tasks: high-level semantic parsing and low-level intrinsic image decomposition.
LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR
In this paper, we propose a novel Eikonal formulation that conditions the implicit representation on localized shape priors which function as dense boundary value constraints, and demonstrate it works on SemanticKITTI and SemanticPOSS.
STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation
By fitting a bridge-shaped curve to the illumination map distribution, both regions are suppressed and two tasks are bridged naturally.
ADAPT: Action-aware Driving Caption Transformer
To bridge the gap, we propose an end-to-end transformer-based architecture, ADAPT (Action-aware Driving cAPtion Transformer), which provides user-friendly natural language narrations and reasoning for each decision making step of autonomous vehicular control and action.
From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds
But adapting this scheme to the state-of-the-art (SOTA) solution for PC-based layout estimation is not straightforward.
AsyncNeRF: Learning Large-scale Radiance Fields from Asynchronous RGB-D Sequences with Time-Pose Function
Large-scale radiance fields are promising mapping tools for smart transportation applications like autonomous driving or drone delivery.
SC-wLS: Towards Interpretable Feed-forward Camera Re-localization
Visual re-localization aims to recover camera poses in a known environment, which is vital for applications like robotics or augmented reality.
VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum Modeling
In the first stage, we perform self-supervised representation learning on unlabeled points with the proposed Viewpoint Bottleneck loss function.
TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation
As such, we study the challenging problem of task oriented detection, which aims to find objects that best afford an action indicated by verbs like sit comfortably on.
Planning Assembly Sequence with Graph Transformer
In this paper, we present a graph-transformer based framework for the ASP problem which is trained and demonstrated on a self-collected ASP database.
Understanding Embodied Reference with Touch-Line Transformer
Human studies have revealed that objects referred to or pointed to do not lie on the elbow-wrist line, a common misconception; instead, they lie on the so-called virtual touch line.
City-scale Incremental Neural Mapping with Three-layer Sampling and Panoptic Representation
Neural implicit representations are drawing a lot of attention from the robotics community recently, as they are expressive, continuous and compact.
LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF
In this paper, we present LATITUDE: Global Localization with Truncated Dynamic Low-pass Filter, which introduces a two-stage localization mechanism in city-scale NeRF.
SOM-Net: Unrolling the Subspace-based Optimization for Solving Full-wave Inverse Scattering Problems
The final output of the SOM-Net is the full predicted induced current, from which the scattered field and the permittivity image can also be deduced analytically.
Distance-Aware Occlusion Detection with Focused Attention
In this work, (1) we propose a novel three-decoder architecture as the infrastructure for focused attention; 2) we use the generalized intersection box prediction task to effectively guide our model to focus on occlusion-specific regions; 3) our model achieves a new state-of-the-art performance on distance-aware relationship detection.
Human-Object Interaction Detection
Relationship Detection
+1
Language-guided Semantic Style Transfer of 3D Indoor Scenes
We address the new problem of language-guided semantic style transfer of 3D indoor scenes.
Model-Driven Based Deep Unfolding Equalizer for Underwater Acoustic OFDM Communications
Each layer of UDNet is designed according to the classical minimum mean square error (MMSE) equalizer.
SNAKE: Shape-aware Neural 3D Keypoint Field
Detecting 3D keypoints from point clouds is important for shape reconstruction, while this work investigates the dual question: can shape reconstruction benefit 3D keypoint detection?
High-Fidelity Human Avatars From a Single RGB Camera
To cope with the complexity of textures and generate photo-realistic results, we propose a reference-based neural rendering network and exploit a bottom-up sharpening-guided fine-tuning strategy to obtain detailed textures.
Transferable End-to-end Room Layout Estimation via Implicit Encoding
Here we propose an end-to-end method that directly predicts parametric layouts from an input panorama image.
Semi-supervised Implicit Scene Completion from Sparse LiDAR
Recent advances show that semi-supervised implicit representation learning can be achieved through physical constraints like Eikonal equations.
Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing
Multi-task indoor scene understanding is widely considered as an intriguing formulation, as the affinity of different tasks may lead to improved performance.
Ranked #31 on
Semantic Segmentation
on NYU Depth v2
Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck
Semantic understanding of 3D point clouds is important for various robotics applications.
PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds
Such a scheme has two limitations: 1) Storing and running several networks for different tasks are expensive for typical robotic platforms.
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications
In summary, the proposed ARC/FML for OoT is a promising scheme for information exchange across water and air.
LID 2020: The Learning from Imperfect Data Challenge Results
The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect data and improve the data-efficiency during training.
Constrained R-CNN: A general image manipulation detection model
Finally, the coarse localization information guides the model to further learn the finer local features and segment out the tampered region.
Efficient Semantic Scene Completion Network with Spatial Group Convolution
We introduce Spatial Group Convolution (SGC) for accelerating the computation of 3D dense prediction tasks.
Ranked #9 on
3D Semantic Scene Completion
on SemanticKITTI
Deeply-supervised Knowledge Synergy
Convolutional Neural Networks (CNNs) have become deeper and more complicated compared with the pioneering AlexNet.
A Closed-form Solution to Universal Style Transfer
Although plenty of methods have been proposed, a theoretical analysis of feature transform is still missing.
SnapQuant: A Probabilistic and Nested Parameterization for Binary Networks
During the training phase, we generate binary weights on-the-fly since what we actually maintain is the policy network, and all the binary weights are used in a burn-after-reading style.
Decoder Network Over Lightweight Reconstructed Feature for Fast Semantic Style Transfer
Our method decomposes the semantic style transfer problem into feature reconstruction part and feature decoder part.
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes.
Network Sketching: Exploiting Binary Structure in Deep CNNs
Convolutional neural networks (CNNs) with deep architectures have substantially advanced the state-of-the-art in computer vision tasks.
