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Found 16 papers, 8 papers with code
DPMesh: Exploiting Diffusion Prior for Occluded Human Mesh Recovery
The recovery of occluded human meshes presents challenges for current methods due to the difficulty in extracting effective image features under severe occlusion.
HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection
Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels.
PASTA: Towards Flexible and Efficient HDR Imaging Via Progressively Aggregated Spatio-Temporal Alignment
Leveraging Transformer attention has led to great advancements in HDR deghosting.
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction
In recent years, the fusion of high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI) has been recognized as an effective method for HSI super-resolution (HSI-SR).
Two-stream joint matching method based on contrastive learning for few-shot action recognition
Although few-shot action recognition based on metric learning paradigm has achieved significant success, it fails to address the following issues: (1) inadequate action relation modeling and underutilization of multi-modal information; (2) challenges in handling video matching problems with different lengths and speeds, and video matching problems with misalignment of video sub-actions.
MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant Features
MTS-LOF leverages the strengths of contrastive learning and Masked Autoencoder (MAE) methods, offering a unique approach to representation learning for medical time series data.
Contrastive Continual Multi-view Clustering with Filtered Structural Fusion
Precisely, considering that data correlations play a vital role in clustering and prior knowledge ought to guide the clustering process of a new view, we develop a data buffer with fixed size to store filtered structural information and utilize it to guide the generation of a robust partition matrix via contrastive learning.
Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution
Transformer-based methods have exhibited remarkable potential in single image super-resolution (SISR) by effectively extracting long-range dependencies.
Ranked #26 on
Image Super-Resolution
on Set14 - 4x upscaling
Knowledge Distillation Under Ideal Joint Classifier Assumption
Within this context, softmax regression representation learning serves as a widely embraced approach, leveraging a pre-established teacher network to guide the learning process of a diminutive student network.
A Transformer-based Diffusion Probabilistic Model for Heart Rate and Blood Pressure Forecasting in Intensive Care Unit
The TDSTF model showed state-of-the-art performance in predicting vital signs in the ICU, outperforming other models' ability to predict distributions of vital signs and being more computationally efficient.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal
Significance: This study is one of the first to extend the conditional diffusion-based generative model for ECG noise removal, and the DeScoD-ECG has the potential to be widely used in biomedical applications.
Deep Domain Adaptation for Pavement Crack Detection
In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available.
Fast top-K Cosine Similarity Search through XOR-Friendly Binary Quantization on GPUs
We explore the use of GPU for accelerating large scale nearest neighbor search and we propose a fast vector-quantization-based exhaustive nearest neighbor search algorithm that can achieve high accuracy without any indexing construction specifically designed for cosine similarity.
Spam Review Detection with Graph Convolutional Networks
Furthermore, we deploy our system to process million-scale data daily at Xianyu.
Polarimetric Dense Monocular SLAM
This paper presents a novel polarimetric dense monocular SLAM (PDMS) algorithm based on a polarization camera.
