Search Results for author:
Found 36 papers, 17 papers with code
UMC: A Unified Bandwidth-efficient and Multi-resolution based Collaborative Perception Framework
To verify our algorithm, we conducted experiments on the V2X-Sim and OPV2V datasets.
Text with Knowledge Graph Augmented Transformer for Video Captioning
Meanwhile, the internal stream is designed to exploit the multi-modality information in videos (e. g., the appearance of video frames, speech transcripts, and video captions) to ensure the quality of caption results.
TMA: Temporal Motion Aggregation for Event-based Optical Flow
Event cameras have the ability to record continuous and detailed trajectories of objects with high temporal resolution, thereby providing intuitive motion cues for optical flow estimation.
Upcycling Models under Domain and Category Shift
We examine the superiority of our GLC on multiple benchmarks with different category shift scenarios, including partial-set, open-set, and open-partial-set DA.
Source-Free Domain Adaptation
Universal Domain Adaptation
+1
Modality-Agnostic Debiasing for Single Domain Generalization
We validate the superiority of our MAD in a variety of single-DG scenarios with different modalities, including recognition on 1D texts, 2D images, 3D point clouds, and semantic segmentation on 2D images.
SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving
However, there is a lack of underground parking scenario datasets with multiple sensors and well-labeled images that support both SLAM tasks and perception tasks, such as semantic segmentation and parking slot detection.
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors
Safe robot reinforcement learning (SRRL) is a crucial step towards achieving human-robot coexistence.
Dual-Stream Transformer for Generic Event Boundary Captioning
This paper describes our champion solution for the CVPR2022 Generic Event Boundary Captioning (GEBC) competition.
A Review of Safe Reinforcement Learning: Methods, Theory and Applications
To establish a good foundation for future research in this thread, in this paper, we provide a review for safe RL from the perspectives of methods, theory and applications.
Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning
Massive false rumors emerging along with breaking news or trending topics severely hinder the truth.
BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation
Source-free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to the unlabeled target domain without accessing the well-labeled source data, which is a much more practical setting due to the data privacy, security, and transmission issues.
Unsupervised Domain Adaptation for Nighttime Aerial Tracking
Previous advances in object tracking mostly reported on favorable illumination circumstances while neglecting performance at nighttime, which significantly impeded the development of related aerial robot applications.
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks
Rumors are rampant in the era of social media.
HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration
Extensive experiments are conducted on two large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HRegNet.
DMInet: An Accurate and Highly Flexible Deep Learning Framework for Drug Membrane Interaction with Membrane Selectivity
Inheriting from coarse-grained Martini representation of organic molecules and combined with deep learning, DMInet has the potential for more accelerated high throughput screening in drug discovery across a much larger chemical space than that can be explored by physics-based simulations alone.
ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization
Traditional methods mainly focus on foreground and background frames separation with only a single attention branch and class activation sequence.
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
Data Augmentation for Object Detection via Differentiable Neural Rendering
We propose a new offline data augmentation method for object detection, which semantically interpolates the training data with novel views.
NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting
Although Transformer has made breakthrough success in widespread domains especially in Natural Language Processing (NLP), applying it to time series forecasting is still a great challenge.
Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection
Extensive experiments on two public grasping datasets, Cornell and Jacquard demonstrate the state-of-the-art performance of our method in balancing accuracy and inference speed.
Ranked #1 on
Robotic Grasping
on Jacquard dataset
PointINet: Point Cloud Frame Interpolation Network
Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras.
MoNet: Motion-based Point Cloud Prediction Network
3D point clouds accurately model 3D information of surrounding environment and are crucial for intelligent vehicles to perceive the scene.
LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection
At each time step, this sampling strategy first estimates current action progression and then decide what temporal ranges should be used to aggregate the optimal supplementary features.
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
To tackle the information loss of random sampling, we exploit a novel random dilation cluster strategy to enlarge the receptive field of each sampled point and an attention mechanism to aggregate the positions and features of neighbor points.
PAN: Towards Fast Action Recognition via Learning Persistence of Appearance
In contrast to optical flow, our PA focuses more on distilling the motion information at boundaries.
Ranked #2 on
Action Recognition
on Jester (Gesture Recognition)
Efficient Pig Counting in Crowds with Keypoints Tracking and Spatial-aware Temporal Response Filtering
Existing methods only focused on pig counting using single image, and its accuracy is challenged by several factors, including pig movements, occlusion and overlapping.
Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection
The method, inspired by the latest state-of-the-art 3D-LaneNet, is a unified framework solving image encoding, spatial transform of features and 3D lane prediction in a single network.
Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehicle
Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing, which is beneficial to mobile robotic applications.
OVC-Net: Object-Oriented Video Captioning with Temporal Graph and Detail Enhancement
Thereafter, we construct a new dataset, providing consistent object-sentence pairs, to facilitate effective cross-modal learning.
Neuromorphic Visual Odometry System for Intelligent Vehicle Application with Bio-inspired Vision Sensor
The neuromorphic camera is a brand new vision sensor that has emerged in recent years.
Robotics
Globally optimal vertical direction estimation in Atlanta World
Accordingly, we propose a vertical direction estimation method by considering the relationship between the vertical frame and horizontal frames.
A Novel Method for the Absolute Pose Problem with Pairwise Constraints
In this paper, we consider pairwise constraints and propose a globally optimal algorithm for solving the absolute pose estimation problem.
Salience Biased Loss for Object Detection in Aerial Images
Experimental results show our proposed loss function with the RetinaNet architecture outperformed other state-of-art object detection models by at least 4. 31 mAP, and RetinaNet by 2. 26 mAP with the same inference speed of RetinaNet.
Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture
However, the mobile sensing perception brings new challenges for how to efficiently analyze and intelligently interpret the deluge of IoT data in mission- critical services.
Hierarchical Latent Semantic Mapping for Automated Topic Generation
Inspired by these algorithms, in this paper, we propose a novel method named Hierarchical Latent Semantic Mapping (HLSM), which automatically generates topics from corpus.
Large-Scale Visual Font Recognition
This paper addresses the large-scale visual font recognition (VFR) problem, which aims at automatic identification of the typeface, weight, and slope of the text in an image or photo without any knowledge of content.
Ranked #1 on
Font Recognition
on VFR-447
Detection Evolution with Multi-order Contextual Co-occurrence
The so-called (1 st -order) context feature is computed as a set of randomized binary comparisons on the response map of the baseline object detector.
