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Found 16 papers, 3 papers with code
C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets
Conformal prediction, as an emerging uncertainty quantification technique, typically functions as post-hoc processing for the outputs of trained classifiers.
Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification
Quantifying the certainty of model predictions is crucial for the safe usage of predictive models, and this limitation restricts their application in critical contexts where the cost of prediction errors is significant.
Effective Generation of Feasible Solutions for Integer Programming via Guided Diffusion
We empirically evaluate our framework on four typical datasets of IP problems, and show that it effectively generates complete feasible solutions with a high probability (> 89. 7 \%) without the reliance of Solvers and the quality of solutions is comparable to the best heuristic solutions from Gurobi.
Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction
To address the challenges of spatial dependence and small sample sizes in predicting U. S. presidential election results using spatially dependent data, we propose a novel transfer learning framework within the SAR model, called as tranSAR.
Exploring Region-Word Alignment in Built-in Detector for Open-Vocabulary Object Detection
In the first stage an image-text dual encoder is trained to learn region-word alignment from a corpus of image-text pairs.
FlowFace++: Explicit Semantic Flow-supervised End-to-End Face Swapping
The discriminator is shape-aware and relies on a semantic flow-guided operation to explicitly calculate the shape discrepancies between the target and source faces, thus optimizing the face swapping network to generate highly realistic results.
FlowFace: Semantic Flow-guided Shape-aware Face Swapping
Unlike most previous methods that focus on transferring the source inner facial features but neglect facial contours, our FlowFace can transfer both of them to a target face, thus leading to more realistic face swapping.
Global-to-local Expression-aware Embeddings for Facial Action Unit Detection
Then, AU feature maps and their corresponding AU masks are multiplied to generate AU masked features focusing on local facial region.
Transformer-based Multimodal Information Fusion for Facial Expression Analysis
Then, we introduce a transformer-based fusion module that integrates the static vision features and the dynamic multimodal features.
A Deep Reinforcement Learning Approach for Online Parcel Assignment
In this paper, we investigate the online parcel assignment (OPA) problem, in which each stochastically generated parcel needs to be assigned to a candidate route for delivery to minimize the total cost subject to certain business constraints.
PcmNet: Position-Sensitive Context Modeling Network for Temporal Action Localization
In this paper, we propose a temporal-position-sensitive context modeling approach to incorporate both positional and semantic information for more precise action localization.
Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks
To further lift the classification performance, in this work we propose a graph convolution network (GCN) based framework for HSI classification that uses two clustering operations to better exploit multi-hop node correlations and also effectively reduce graph size.
What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning
As an important and challenging problem, multi-domain learning (MDL) typically seeks for a set of effective lightweight domain-specific adapter modules plugged into a common domain-agnostic network.
Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection
After aligning the interior points with fused features, the proposed network refines the prediction in a more accurate manner and encodes the whole box in a novel compact method.
SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds
Accurate 3D object detection from point clouds has become a crucial component in autonomous driving.
Ranked #1 on
3D Object Detection
on KITTI Pedestrians Hard
Relating lp regularization and reweighted l1 regularization
We propose a general framework of iteratively reweighted l1 methods for solving lp regularization problems.
