Search Results for author:
Found 20 papers, 5 papers with code
FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models
Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction.
A Real-Time Framework for Domain-Adaptive Underwater Object Detection with Image Enhancement
Furthermore, to ensure balanced training for both tasks, we present a multi-stage training strategy aimed at consistently enhancing their performance.
VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding
We explore how reconciling several foundation models (large language models and vision-language models) with a novel unified memory mechanism could tackle the challenging video understanding problem, especially capturing the long-term temporal relations in lengthy videos.
Global-Local MAV Detection under Challenging Conditions based on Appearance and Motion
In this paper, we propose a global-local MAV detector that can fuse both motion and appearance features for MAV detection under challenging conditions.
CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update
However, these methods often overlook the potential for continual learning, typically by freezing the utilized tools, thus limiting their adaptation to environments requiring new knowledge.
Exploring Data Geometry for Continual Learning
Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and prevents forgetting by keeping geometric structures of old data into account.
Meta-causal Learning for Single Domain Generalization
Under this paradigm, we propose a meta-causal learning method to learn meta-knowledge, that is, how to infer the causes of domain shift between the auxiliary and source domains during training.
Ranked #1 on
Single-Source Domain Generalization
on PACS
Imbalance Knowledge-Driven Multi-modal Network for Land-Cover Semantic Segmentation Using Images and LiDAR Point Clouds
The present multi-modal methods usually map high-dimensional features to low-dimensional spaces as a preprocess before feature extraction to address the nonnegligible domain gap, which inevitably leads to information loss.
SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real Images
Although learning-based UIE methods have made remarkable achievements in recent years, it's still challenging for them to consistently deal with various underwater conditions, which could be caused by: 1) the use of the simplified atmospheric image formation model in UIE may result in severe errors; 2) the network trained solely with synthetic images might have difficulty in generalizing well to real underwater images.
Asymmetric Hash Code Learning for Remote Sensing Image Retrieval
In this paper, we propose a novel deep hashing method, named asymmetric hash code learning (AHCL), for RSIR.
Generating Multivariate Load States Using a Conditional Variational Autoencoder
In this paper, a multivariate load state generating model on the basis of a conditional variational autoencoder (CVAE) neural network is proposed.
Superevents: Towards Native Semantic Segmentation for Event-based Cameras
Most successful computer vision models transform low-level features, such as Gabor filter responses, into richer representations of intermediate or mid-level complexity for downstream visual tasks.
A Hyperbolic-to-Hyperbolic Graph Convolutional Network
Specifically, we developed a manifold-preserving graph convolution that consists of a hyperbolic feature transformation and a hyperbolic neighborhood aggregation.
Curvature Generation in Curved Spaces for Few-Shot Learning
Few-shot learning describes the challenging problem of recognizing samples from unseen classes given very few labeled examples.
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling
This work presents FG-Net, a general deep learning framework for large-scale point clouds understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 GPU.
Ranked #1 on
Semantic Segmentation
on Semantic3D
Learning to Optimize on SPD Manifolds
We parameterize the optimizer by the recurrent model and utilize Riemannian operations to ensure that our method is faithful to the geometry of SPD manifolds.
MLFcGAN: Multi-level Feature Fusion based Conditional GAN for Underwater Image Color Correction
Color correction for underwater images has received increasing interests, due to its critical role in facilitating available mature vision algorithms for underwater scenarios.
Learning a Robust Representation via a Deep Network on Symmetric Positive Definite Manifolds
To this end, several new layers are introduced in our network, including a nonlinear kernel aggregation layer, an SPD matrix transformation layer, and a vectorization layer.
Google Map Aided Visual Navigation for UAVs in GPS-denied Environment
We propose a framework for Google Map aided UAV navigation in GPS-denied environment.
Real-Time Optical flow-based Video Stabilization for Unmanned Aerial Vehicles
This paper describes the development of a novel algorithm to tackle the problem of real-time video stabilization for unmanned aerial vehicles (UAVs).
