Search Results for author:
Found 22 papers, 8 papers with code
OmniGS: Omnidirectional Gaussian Splatting for Fast Radiance Field Reconstruction using Omnidirectional Images
In this paper, we present OmniGS, a novel omnidirectional Gaussian splatting system, to take advantage of omnidirectional images for fast radiance field reconstruction.
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels
However, these prior methods do not learn noise filters by exploiting knowledge across all clients, leading to sub-optimal and inferior noise filtering performance and thus damaging training stability.
360Loc: A Dataset and Benchmark for Omnidirectional Visual Localization with Cross-device Queries
We propose a virtual camera approach to generate lower-FoV query frames from 360$^\circ$ images, which ensures a fair comparison of performance among different query types in visual localization tasks.
Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular, Stereo, and RGB-D Cameras
In addition to actively densifying hyper primitives based on geometric features, we further introduce a Gaussian-Pyramid-based training method to progressively learn multi-level features, enhancing photorealistic mapping performance.
Improving Knowledge Distillation via Transferring Learning Ability
Existing knowledge distillation methods generally use a teacher-student approach, where the student network solely learns from a well-trained teacher.
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning
We investigate a practical domain adaptation task, called source-free domain adaptation (SFUDA), where the source-pretrained model is adapted to the target domain without access to the source data.
Ranked #4 on
Source-Free Domain Adaptation
on VisDA-2017
Less is More: Adaptive Curriculum Learning for Thyroid Nodule Diagnosis
Thyroid nodule classification aims at determining whether the nodule is benign or malignant based on a given ultrasound image.
Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach
The problem of multi-robot navigation of connectivity maintenance is challenging in multi-robot applications.
Safe Learning-based Gradient-free Model Predictive Control Based on Cross-entropy Method
In this paper, a safe and learning-based control framework for model predictive control (MPC) is proposed to optimize nonlinear systems with a non-differentiable objective function under uncertain environmental disturbances.
Gaussian Processes
Robotics
PPR-Net:Point-wise Pose Regression Network for Instance Segmentation and 6D Pose Estimation in Bin-picking Scenarios
For each point in the point cloud, the network regresses a 6D pose of the object instance that the point belongs to.
Instance-Aware Representation Learning and Association for Online Multi-Person Tracking
Multi-Person Tracking (MPT) is often addressed within the detection-to-association paradigm.
MetaGrasp: Data Efficient Grasping by Affordance Interpreter Network
Although the model is trained using only RGB image, when changing the background textures, it also performs well and can achieve even 94% accuracy on the set of adversarial objects, which outperforms current state-of-the-art methods.
Deep Reasoning with Knowledge Graph for Social Relationship Understanding
And this structured knowledge can be efficiently integrated into the deep neural network architecture to promote social relationship understanding by an end-to-end trainable Graph Reasoning Model (GRM), in which a propagation mechanism is learned to propagate node message through the graph to explore the interaction between persons of interest and the contextual objects.
Ranked #2 on
Visual Social Relationship Recognition
on PIPA
Recurrent 3D Pose Sequence Machines
3D human articulated pose recovery from monocular image sequences is very challenging due to the diverse appearances, viewpoints, occlusions, and also the human 3D pose is inherently ambiguous from the monocular imagery.
Ranked #20 on
3D Human Pose Estimation
on HumanEva-I
Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction
This paper aims at task-oriented action prediction, i. e., predicting a sequence of actions towards accomplishing a specific task under a certain scene, which is a new problem in computer vision research.
Human Pose Estimation from Depth Images via Inference Embedded Multi-task Learning
In this paper, we propose a novel inference-embedded multi-task learning framework for predicting human pose from still depth images, which is implemented with a deep architecture of neural networks.
LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling
Another long short-term memorized fusion layer is set up to integrate the contexts along the vertical direction from different channels, and perform bi-directional propagation of the fused vertical contexts along the horizontal direction to obtain true 2D global contexts.
Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos
To our knowledge, this is the first Zero-Shot event detection model that is built on top of distributional semantics and extends it in the following directions: (a) semantic embedding of multimodal information in videos (with focus on the visual modalities), (b) automatically determining relevance of concepts/attributes to a free text query, which could be useful for other applications, and (c) retrieving videos by free text event query (e. g., "changing a vehicle tire") based on their content.
Depth Extraction from Videos Using Geometric Context and Occlusion Boundaries
We propose to learn and infer depth in videos from appearance, motion, occlusion boundaries, and geometric context of the scene.
Cluster Synchronization of Coupled Systems with Nonidentical Linear Dynamics
This paper considers the cluster synchronization problem of generic linear dynamical systems whose system models are distinct in different clusters.
Pedestrian Detection in Low-resolution Imagery by Learning Multi-scale Intrinsic Motion Structures (MIMS)
In this paper, we give a comparative evaluation of the proposed method and demonstrate that MIMS outperforms the state of the art approaches in identifying pedestrians from low resolution airborne videos.
3D Visual Proxemics: Recognizing Human Interactions in 3D from a Single Image
g Unlike previous approaches that directly map people/face locations in 2D image space into features for classification, we first estimate camera viewpoint and people positions in 3D space and then extract spatial configuration features from explicit 3D people positions.
