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Found 13 papers, 10 papers with code
VRSO: Visual-Centric Reconstruction for Static Object Annotation
As a part of the perception results of intelligent driving systems, static object detection (SOD) in 3D space provides crucial cues for driving environment understanding.
Gyroscope-Assisted Motion Deblurring Network
Yet, their practical usage in real-world deblurring, especially motion blur, remains limited due to the lack of pixel-aligned training triplets (background, blurred image, and blur heat map) and restricted information inherent in blurred images.
SuperEdge: Towards a Generalization Model for Self-Supervised Edge Detection
Edge detection is a fundamental technique in various computer vision tasks.
Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image Captioning
To efficiently align the image-text, we propose a novel two-stage vision-language pre-training-based approach to bootstrap interactive image-text alignment for remote sensing image captioning, called BITA, which relies on the design of a lightweight interactive Fourier Transformer to better align remote sensing image-text features.
Skeleton Ground Truth Extraction: Methodology, Annotation Tool and Benchmarks
However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards.
A Vision-Centric Approach for Static Map Element Annotation
The recent development of online static map element (a. k. a.
Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection
Foreground shift only occurs when replaying images of previous tasks and refers to the fact that their background might contain foreground objects of the current task.
RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation
This paper introduces RoMe, a novel framework designed for the robust reconstruction of large-scale road surfaces.
Towards Accurate Ground Plane Normal Estimation from Ego-Motion
In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles.
Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and Fusion
The key idea in FedKF is to let the server return the global knowledge to be fused with the local knowledge in each training round so that the local model can be regularized towards the global optima.
CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization
Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization.
PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments
The experimental results show that PIoU loss can dramatically improve the performance of OBB detectors, particularly on objects with high aspect ratios and complex backgrounds.
User-Curated Image Collections: Modeling and Recommendation
We then consider image collection recommendation as a dynamic similarity measurement problem in response to user's clicked image set, and employ a metric learner to measure the similarity between the image collection and the clicked image set.
