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Found 33 papers, 12 papers with code
TCLC-GS: Tightly Coupled LiDAR-Camera Gaussian Splatting for Surrounding Autonomous Driving Scenes
Most 3D Gaussian Splatting (3D-GS) based methods for urban scenes initialize 3D Gaussians directly with 3D LiDAR points, which not only underutilizes LiDAR data capabilities but also overlooks the potential advantages of fusing LiDAR with camera data.
Behind the Veil: Enhanced Indoor 3D Scene Reconstruction with Occluded Surfaces Completion
The 3D Inpainter with abstract representation at coarse levels is trained offline using various scenes to complete occluded surfaces.
SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects
We argue that the cause of failure is the sensitivity of depth regression losses to noise of larger objects.
3D Object Detection
3D Object Detection From Monocular Images
+3
UPNeRF: A Unified Framework for Monocular 3D Object Reconstruction and Pose Estimation
Monocular 3D reconstruction for categorical objects heavily relies on accurately perceiving each object's pose.
Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM).
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection
A major challenge in monocular 3D object detection is the limited diversity and quantity of objects in real datasets.
Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication
A DT of the ISAC device is constructed to predict the impact of potential decisions on the long-term computation cost of the server, based on which the decisions are made with closed-form formulas.
Open-Set Image Tagging with Multi-Grained Text Supervision
Specifically, for predefined commonly used tag categories, RAM++ showcases 10. 2 mAP and 15. 4 mAP enhancements over CLIP on OpenImages and ImageNet.
Digital Twin-Assisted Resource Demand Prediction for Multicast Short Video Streaming
In this paper, we propose a digital twin (DT)-assisted resource demand prediction scheme to enhance prediction accuracy for multicast short video streaming.
Recognize Anything: A Strong Image Tagging Model
We are releasing the RAM at \url{https://recognize-anything. github. io/} to foster the advancements of large models in computer vision.
Tag2Text: Guiding Vision-Language Model via Image Tagging
This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic features.
Molecular Communication for Quorum Sensing Inspired Cooperative Drug Delivery
Our results show that the activation probability at the B-NM increases as this B-NM is located closer to the center of the B-NM population and the aggregate absorption rate of the drug molecules non-linearly increases as the population density increases.
Digital Twin-Assisted Collaborative Transcoding for Better User Satisfaction in Live Streaming
Particularly, two DTs are constructed for emulating the cloud-edge collaborative transcoding process by analyzing spatial-temporal information of individual videos and transcoding configurations of transcoding queues, respectively.
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-training
However, the image-text pairs co-occurrent on the Internet typically lack explicit alignment information, which is suboptimal for VLP.
IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach
In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels.
A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer
Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning.
Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer
The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis.
Histopathological Image Classification
Image Classification
+3
Personalized QoE Enhancement for Adaptive Video Streaming: A Digital Twin-Assisted Scheme
In this paper, we present a digital twin (DT)-assisted adaptive video streaming scheme to enhance personalized quality-of-experience (PQoE).
TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos
The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments.
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology
Method: This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions of breast histopathological images.
OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion
In this paper, we propose a 360 monocular depth estimation pipeline, OmniFusion, to tackle the spherical distortion issue.
Ranked #6 on
Depth Estimation
on Stanford2D3D Panoramic
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation
Traditional machine learning methods achieve maximum accuracy of 76. 02% and deep learning method achieves a maximum accuracy of 95. 37%.
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.
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
Multi-label learning in the presence of missing labels (MLML) is a challenging problem.
A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers
Finally, the application prospect of the analytical method in this field is discussed.
QoE-driven Secure Video Transmission in Cloud-edge Collaborative Networks
In this paper, by considering the interaction between video encoding and edge caching, we investigate the quality of experience (QoE)-driven cross-layer optimization of secure video transmission over the wireless backhaul link in cloud-edge collaborative networks.
Multimedia
Revenue and Energy Efficiency-Driven Delay Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach
In this paper, we propose a joint task type and vehicle speed-aware task offloading and resource allocation strategy to decrease the vehicl's energy cost for executing tasks and increase the revenue of the vehicle for processing tasks within the delay constraint.
AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms
Our augmented approach combines the flexibility in a virtual environment (e. g., vehicle movements) with the richness of the real world to allow effective simulation of anywhere on earth.
Part-level Car Parsing and Reconstruction from Single Street View
As it is labor-intensive to annotate semantic parts on real street views, we propose a specific approach to implicitly transfer part features from synthesized images to real street views.
RealPoint3D: Point Cloud Generation from a Single Image with Complex Background
Then, the image together with the retrieved shape model is fed into the proposed network to generate the fine-grained 3D point cloud.
A Network Structure to Explicitly Reduce Confusion Errors in Semantic Segmentation
Confusing classes that are ubiquitous in real world often degrade performance for many vision related applications like object detection, classification, and segmentation.
The ApolloScape Open Dataset for Autonomous Driving and its Application
In this paper, we provide a sensor fusion scheme integrating camera videos, consumer-grade motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robust self-localization and semantic segmentation for autonomous driving.
Mask-off: Synthesizing Face Images in the Presence of Head-mounted Displays
The main purpose of this paper is to synthesize realistic face images without occlusions based on the images captured by these cameras.
