Search Results for author:
Found 49 papers, 26 papers with code
Urban Architect: Steerable 3D Urban Scene Generation with Layout Prior
(2) To handle the unbounded nature of urban scenes, we represent 3D scene with a Scalable Hash Grid structure, incrementally adapting to the growing scale of urban scenes.
LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis
In light of this, we propose LiDAR4D, a differentiable LiDAR-only framework for novel space-time LiDAR view synthesis.
Pensieve: Retrospect-then-Compare Mitigates Visual Hallucination
Multi-modal Large Language Models (MLLMs) demonstrate remarkable success across various vision-language tasks.
GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning
GLC++ enhances the novel category clustering accuracy of GLC by 4. 3% in open-set scenarios on Office-Home.
MAP: MAsk-Pruning for Source-Free Model Intellectual Property Protection
In this paper, we target a practical setting where only a well-trained source model is available and investigate how we can realize IP protection.
LEAD: Learning Decomposition for Source-free Universal Domain Adaptation
Besides, LEAD is also appealing in that it is complementary to most existing methods.
Ranked #1 on
Universal Domain Adaptation
on VisDA2017
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds
Therefore, existing complementary learning approaches for MDE fuse intensity information from images and scene details from event data for better scene understanding.
GarchingSim: An Autonomous Driving Simulator with Photorealistic Scenes and Minimalist Workflow
However, the availability of free and open-source simulators is limited, and the installation and configuration process can be daunting for beginners and interdisciplinary researchers.
Spreeze: High-Throughput Parallel Reinforcement Learning Framework
While existing parallel RL frameworks encompass a variety of RL algorithms and parallelization techniques, the excessively burdensome communication frameworks hinder the attainment of the hardware's limit for final throughput and training effects on a single desktop.
HDMNet: A Hierarchical Matching Network with Double Attention for Large-scale Outdoor LiDAR Point Cloud Registration
Specifically, A novel feature consistency enhanced double-soft matching network is introduced to achieve two-stage matching with high flexibility while enlarging the receptive field with high efficiency in a patch-to patch manner, which significantly improves the registration performance.
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation
Nevertheless, the existing approaches just extract each interest independently for the corresponding sub-sequence while ignoring the global correlation of the entire interaction sequence, which may fail to capture the user's inherent preference for the potential interests generalization and unavoidably make the recommended items homogeneous with the historical behaviors.
Urban Radiance Field Representation with Deformable Neural Mesh Primitives
To construct urban-level radiance fields efficiently, we design Deformable Neural Mesh Primitive~(DNMP), and propose to parameterize the entire scene with such primitives.
NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation
In light of these issues, we present NeuralPCI: an end-to-end 4D spatio-temporal Neural field for 3D Point Cloud Interpolation, which implicitly integrates multi-frame information to handle nonlinear large motions for both indoor and outdoor scenarios.
Ranked #1 on
3D Point Cloud Interpolation
on NL-Drive
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.
Ranked #7 on
Video Captioning
on YouCook2
TMA: Temporal Motion Aggregation for Event-based Optical Flow
In this paper, we argue that temporal continuity is a vital element of event-based optical flow and propose a novel Temporal Motion Aggregation (TMA) approach to unlock its potential.
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.
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.
Ranked #2 on
Universal Domain Adaptation
on VisDA2017
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
Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment.
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.
Ranked #5 on
Weakly Supervised Action Localization
on THUMOS14
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.
Ranked #8 on
3D Lane Detection
on Apollo Synthetic 3D Lane
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.
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.
