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Found 11 papers, 8 papers with code
Learning Transferable Negative Prompts for Out-of-Distribution Detection
Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Robust Synthetic-to-Real Transfer for Stereo Matching
With advancements in domain generalized stereo matching networks, models pre-trained on synthetic data demonstrate strong robustness to unseen domains.
DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization
Our motivation stems from the highly efficient representation and surprising quality of the recent 3D Gaussian Splatting, despite it will encounter a geometry degradation when input views decrease.
Multi-relational Graph Diffusion Neural Network with Parallel Retention for Stock Trends Classification
Stock trend classification remains a fundamental yet challenging task, owing to the intricate time-evolving dynamics between and within stocks.
Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning
To this end, we introduce a novel calibrated outlier class learning (COCL) approach, in which 1) a debiased large margin learning method is introduced in the outlier class learning to distinguish OOD samples from both head and tail classes in the representation space and 2) an outlier-class-aware logit calibration method is defined to enhance the long-tailed classification confidence.
MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation Coefficient
Monocular 3D object detection is an inherently ill-posed problem, as it is challenging to predict accurate 3D localization from a single image.
Towards Fully Decoupled End-to-End Person Search
This enables independent learning for different objectives thus fully decoupled the model for persons earch.
Unsupervised Recognition of Unknown Objects for Open-World Object Detection
Open-World Object Detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge.
Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds
Open-Set Recognition (OSR) is dedicated to addressing the unknown class issue, but existing OSR methods are not designed to model the semantic information of the unseen classes.
Quantum Graph Convolutional Neural Networks
At present, there are a large number of quantum neural network models to deal with Euclidean spatial data, while little research have been conducted on non-Euclidean spatial data.
A Fast Face Detection Method via Convolutional Neural Network
Current face or object detection methods via convolutional neural network (such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features based on an image pyramid.
