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Found 14 papers, 5 papers with code
Detector-in-Detector: Multi-Level Analysis for Human-Parts
In particular, we adopt a region-based object detection structure with two carefully designed detectors to separately pay attention to the human body and body parts in a coarse-to-fine manner, which we call Detector-in-Detector network (DID-Net).
Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting
We firstly represent only face regions using the latent variable as the domain knowledge and combine it with the non-face parts textures to generate high-quality face images with plausible contents.
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space
In this paper, we examine the diversity of teacher models in the gradient space and regard the ensemble knowledge distillation as a multi-objective optimization problem so that we can determine a better optimization direction for the training of student network.
Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction
Extensive experiments on real-world data demonstrate the effectiveness of our approach.
Face Deblurring Based on Separable Normalization and Adaptive Denormalization
Face deblurring aims to restore a clear face image from a blurred input image with more explicit structure and facial details.
HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors
We observe that the core difficulty for heterogeneous KD (hetero-KD) is the significant semantic gap between the backbone features of heterogeneous detectors due to the different optimization manners.
Cross-Modality Domain Adaptation for Freespace Detection: A Simple yet Effective Baseline
To better bridge the domain gap between source domain (synthetic data) and target domain (real-world data), we also propose a Selective Feature Alignment (SFA) module which only aligns the features of consistent foreground area between the two domains, thus realizing inter-domain intra-modality adaptation.
Improving Hand Recognition in Uncontrolled and Uncooperative Environments using Multiple Spatial Transformers and Loss Functions
However, their performance deteriorates significantly in uncontrolled and uncooperative environments.
GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised Learning
To tackle these challenges, we present GenView, a controllable framework that augments the diversity of positive views leveraging the power of pretrained generative models while preserving semantics.
RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks
Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning.
Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes
The local estimators are integrated via information exchange to form a distributed estimation scheme, which provides estimates of the unmeasured/unmeasurable state variables of the original nonlinear process in a linear manner.
Partition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes
In this paper, we address a partition-based distributed state estimation problem for large-scale general nonlinear processes by proposing a Kalman-based approach.
Iterative distributed moving horizon estimation of linear systems with penalties on both system disturbances and noise
In this paper, partition-based distributed state estimation of general linear systems is considered.
Local Correlation Consistency for Knowledge Distillation
Sufficient knowledge extraction from the teacher network plays a critical role in the knowledge distillation task to improve the performance of the student network.
