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Found 15 papers, 6 papers with code
Distribution-Aware Continual Test Time Adaptation for Semantic Segmentation
In this paper, we propose a distribution-aware tuning (DAT) method to make the semantic segmentation CTTA efficient and practical in real-world applications.
Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions
The performances on the representative VLN task R2R show that our method surpasses the leading zero-shot VLN model by a large margin on all metrics.
RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision
3D occupancy prediction holds significant promise in the fields of robot perception and autonomous driving, which quantifies 3D scenes into grid cells with semantic labels.
Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
The task of Visual Object Navigation (VON) involves an agent's ability to locate a particular object within a given scene.
An Overview of AI and Blockchain Integration for Privacy-Preserving
In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve a more comprehensive privacy protection of privacy.
Exploring Sparse Visual Prompt for Cross-domain Semantic Segmentation
Therefore, we propose a novel Sparse Visual Domain Prompts (SVDP) approach tailored for addressing domain shift problems in semantic segmentation, which holds minimal discrete trainable parameters (e. g. 10\%) of the prompt and reserves more spatial information.
Multi-latent Space Alignments for Unsupervised Domain Adaptation in Multi-view 3D Object Detection
Vision-Centric Bird-Eye-View (BEV) perception has shown promising potential and attracted increasing attention in autonomous driving.
Efficient Meta-Tuning for Content-aware Neural Video Delivery
Our method significantly reduces the computational cost and achieves even better performance, paving the way for applying neural video delivery techniques to practical applications.
Adaptive Patch Exiting for Scalable Single Image Super-Resolution
Once the incremental capacity is below the threshold, the patch can exit at the specific layer.
A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification
Image analysis technology is used to solve the inadvertences of artificial traditional methods in disease, wastewater treatment, environmental change monitoring analysis and convolutional neural networks (CNN) play an important role in microscopic image analysis.
What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review
In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly.
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution
However, existing methods mostly train the DNNs on uniformly sampled LR-HR patch pairs, which makes them fail to fully exploit informative patches within the image.
Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation
Internet video delivery has undergone a tremendous explosion of growth over the past few years.
A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches
This paper reviews the methods of WSI analysis based on machine learning.
CLUE: Towards Discovering Locked Cryptocurrencies in Ethereum
Because the locked cryptocurrencies can never be controlled by users, avoid interacting with the accounts discovered by CLUE and repeating the same mistakes again can help users to save money.
Cryptography and Security
