Search Results for author:
Found 22 papers, 16 papers with code
Towards Responsible and Reliable Traffic Flow Prediction with Large Language Models
Achieving both accuracy and responsibility in traffic prediction models remains a challenge due to the complexity of traffic data and the inherent opacity of deep learning models.
Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies
There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology.
ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution
Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation.
Revolutionizing Agrifood Systems with Artificial Intelligence: A Survey
With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages.
SimDistill: Simulated Multi-modal Distillation for BEV 3D Object Detection
Multi-view camera-based 3D object detection has become popular due to its low cost, but accurately inferring 3D geometry solely from camera data remains challenging and may lead to inferior performance.
Vision Transformer with Quadrangle Attention
Window-based attention has become a popular choice in vision transformers due to its superior performance, lower computational complexity, and less memory footprint.
ViTPose++: Vision Transformer for Generic Body Pose Estimation
In this paper, we show the surprisingly good properties of plain vision transformers for body pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model dubbed ViTPose.
Ranked #1 on
Animal Pose Estimation
on AP-10K
(using extra training data)
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.
Rethinking Hierarchies in Pre-trained Plain Vision Transformer
Self-supervised pre-training vision transformer (ViT) via masked image modeling (MIM) has been proven very effective.
Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
Large-scale vision foundation models have made significant progress in visual tasks on natural images, with vision transformers being the primary choice due to their good scalability and representation ability.
Ranked #1 on
Aerial Scene Classification
on AID (50% as trainset)
Toward Real-world Single Image Deraining: A New Benchmark and Beyond
To address these issues, we establish a new high-quality dataset named RealRain-1k, consisting of $1, 120$ high-resolution paired clean and rainy images with low- and high-density rain streaks, respectively.
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
In this paper, we show the surprisingly good capabilities of plain vision transformers for pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model called ViTPose.
Ranked #1 on
Pose Estimation
on COCO test-dev
VSA: Learning Varied-Size Window Attention in Vision Transformers
Attention within windows has been widely explored in vision transformers to balance the performance, computation complexity, and memory footprint.
ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond
Vision transformers have shown great potential in various computer vision tasks owing to their strong capability to model long-range dependency using the self-attention mechanism.
Ranked #2 on
Image Classification
on ImageNet ReaL
RegionCL: Can Simple Region Swapping Contribute to Contrastive Learning?
In this paper, we make the first attempt to demonstrate the importance of both regions in cropping from a complete perspective and propose a simple yet effective pretext task called Region Contrastive Learning (RegionCL).
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias
Nevertheless, vision transformers treat an image as 1D sequence of visual tokens, lacking an intrinsic inductive bias (IB) in modeling local visual structures and dealing with scale variance.
Ranked #2 on
Video Object Segmentation
on DAVIS 2017
WiForce: Wireless Sensing and Localization of Contact Forces on a Space Continuum
Contact force is a natural way for humans to interact with the physical world around us.
Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing
In this paper, we propose a novel GRAph PYramid Mutual Learning (Grapy-ML) method to address the cross-dataset human parsing problem, where the annotations are at different granularities.
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation
Although there has been a progress in matching the marginal distributions between two domains, the classifier favors the source domain features and makes incorrect predictions on the target domain due to category-agnostic feature alignment.
Ranked #24 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
Structured Pruning for Efficient ConvNets via Incremental Regularization
Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance loss.
Structured Pruning for Efficient ConvNets via Incremental Regularization
Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance degrade.
Structured Probabilistic Pruning for Convolutional Neural Network Acceleration
Unlike existing deterministic pruning approaches, where unimportant weights are permanently eliminated, SPP introduces a pruning probability for each weight, and pruning is guided by sampling from the pruning probabilities.
