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Found 22 papers, 6 papers with code
YOLO-UniOW: Efficient Universal Open-World Object Detection
In this work, we introduce Universal Open-World Object Detection (Uni-OWD), a new paradigm that unifies open-vocabulary and open-world object detection tasks.
LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization
In fact, all the subspaces representing danger in the logical scenario space, rather than only the most critical concrete scenario, play a more significant role for the safety evaluation.
EADReg: Probabilistic Correspondence Generation with Efficient Autoregressive Diffusion Model for Outdoor Point Cloud Registration
To address this issue, we propose a novel framework named EADReg for efficient and robust registration of LiDAR point clouds based on autoregressive diffusion models.
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection
By incorporating the RASFormer block, our RAS method achieves superior contextual awareness capabilities, leading to remarkable performance.
Multi-class Anomaly Detection
Unsupervised Anomaly Detection
Optimised ProPainter for Video Diminished Reality Inpainting
In this paper, part of the DREAMING Challenge - Diminished Reality for Emerging Applications in Medicine through Inpainting, we introduce a refined video inpainting technique optimised from the ProPainter method to meet the specialised demands of medical imaging, specifically in the context of oral and maxillofacial surgery.
MAMBA4D: Efficient Long-Sequence Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models
Point cloud videos can faithfully capture real-world spatial geometries and temporal dynamics, which are essential for enabling intelligent agents to understand the dynamically changing world.
YOLOv10: Real-Time End-to-End Object Detection
In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and model architecture.
Ranked #23 on
Real-Time Object Detection
on MS COCO
Biophysics Informed Pathological Regularisation for Brain Tumour Segmentation
Recent advances in deep learning have significantly improved brain tumour segmentation techniques; however, the results still lack confidence and robustness as they solely consider image data without biophysical priors or pathological information.
TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios
Multi-object tracking in traffic videos is a crucial research area, offering immense potential for enhancing traffic monitoring accuracy and promoting road safety measures through the utilisation of advanced machine learning algorithms.
Traffic Video Object Detection using Motion Prior
In this work, we propose two innovative methods to exploit the motion prior and boost the performance of both fully-supervised and semi-supervised traffic video object detection.
MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening
This work devises MammoDG, a novel deep-learning framework for generalisable and reliable analysis of cross-domain multi-center mammography data.
Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation
We propose a novel framework for deformable image registration.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
Continuous U-Net: Faster, Greater and Noiseless
Despite the powerful performance reported by existing U-Net type networks, they suffer from several major limitations.
TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation
Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians.
SCOTCH and SODA: A Transformer Video Shadow Detection Framework
In this work, we argue that accounting for shadow deformation is essential when designing a video shadow detection method.
Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness
This is the question that we address in this work.
LAMBDA: Covering the Solution Set of Black-Box Inequality by Search Space Quantization
Black-box functions are broadly used to model complex problems that provide no explicit information but the input and output.
PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation
The core of our framework is two patch-based strategies, where we demonstrate that patch representation is key for performance gain.
Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting
In this work, we present a solution framed as a simultaneous semantic and instance segmentation framework.
Contrastive Registration for Unsupervised Medical Image Segmentation
Secondly, we embed a contrastive learning mechanism into the registration architecture to enhance the discriminating capacity of the network in the feature-level.
Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration
This paper presents a novel framework for unsupervised 3D brain image registration by capturing the feature-level transformation relationships between the unaligned image and reference image.
