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Found 38 papers, 19 papers with code
Motion Compensated Dynamic MRI Reconstruction with Local Affine Optical Flow Estimation
This paper proposes a novel framework to reconstruct the dynamic magnetic resonance images (DMRI) with motion compensation (MC).
Recommendation System based on Semantic Scholar Mining and Topic modeling: A behavioral analysis of researchers from six conferences
According to importance of the subject, in this paper we discover the trends of the topics and find relationship between LDA topics and Scholar-Context-documents.
ZJUNlict Extended Team Description Paper for RoboCup 2019
For the Small Size League of RoboCup 2018, Team ZJUNLict has won the champion and therefore, this paper thoroughly described the devotion which ZJUNLict has devoted and the effort that ZJUNLict has contributed.
Robotics 68T40
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks
Hardware-friendly network quantization (e. g., binary/uniform quantization) can efficiently accelerate the inference and meanwhile reduce memory consumption of the deep neural networks, which is crucial for model deployment on resource-limited devices like mobile phones.
IoT-based Contact Tracing Systems for Infectious Diseases: Architecture and Analysis
The recent COVID-19 pandemic has become a major threat to human health and well-being.
Computers and Society
Contrastive Clustering
In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning.
Ranked #4 on
Image Clustering
on STL-10
(using extra training data)
Partially View-aligned Clustering
To solve this practical and challenging problem, we propose a novel multi-view clustering method termed partially view-aligned clustering (PVC).
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
To further employ the power of quantization, the mixed precision technique is incorporated in our framework by approximating the inter-layer and intra-layer sensitivity.
Partially View-aligned Representation Learning with Noise-robust Contrastive Loss
To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss.
PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery
Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional object detection pipelines.
Learning Cross-Modal Retrieval With Noisy Labels
Recently, cross-modal retrieval is emerging with the help of deep multimodal learning.
Unsupervised Neural Rendering for Image Hazing
To generate plausible haze, we study two less-touched but challenging problems in hazy image rendering, namely, i) how to estimate the transmission map from a single image without auxiliary information, and ii) how to adaptively learn the airlight from exemplars, i. e., unpaired real hazy images.
All-in-One Image Restoration for Unknown Corruption
In this paper, we study a challenging problem in image restoration, namely, how to develop an all-in-one method that could recover images from a variety of unknown corruption types and levels.
Learning With Twin Noisy Labels for Visible-Infrared Person Re-Identification
To solve the TNL problem, we propose a novel method for robust VI-ReID, termed DuAlly Robust Training (DART).
Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance
Community networks (CNs) have become an important paradigm for providing essential Internet connectivity in unserved and underserved areas across the world.
Multi-Scale Adaptive Network for Single Image Denoising
AFuB devotes to adaptively sampling and transferring the features from one scale to another scale, which fuses the multi-scale features with varying characteristics from coarse to fine.
OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization
Numerous network compression methods such as pruning and quantization are proposed to reduce the model size significantly, of which the key is to find suitable compression allocation (e. g., pruning sparsity and quantization codebook) of each layer.
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric
Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (\textit{e. g.}, gender, race, RNA sequencing technique) from dominating the clustering.
Ranked #1 on
Image Clustering
on HAR
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout
In this paper, we present a tractable approximation for BNN based on the Monte Carlo (MC) dropout method for capturing the uncertainty in the satellite telemetry time series, without sacrificing accuracy.
UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms
However, due to UAVs' high dynamics and complexity, the real-world deployment of a SAGIN becomes a major barrier for realizing such SAGINs.
Enabling Resilient and Real-Time Network Operations in Space: A Novel Multi-Layer Satellite Networking Scheme
However, the traditional access-based approach to satellite operations cannot meet the pressing requirements of real-time, reliable, and resilient operations for LEO satellites.
Graph Matching with Bi-level Noisy Correspondence
In this paper, we study a novel and widely existing problem in graph matching (GM), namely, Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence (NNC) and edge-level noisy correspondence (ENC).
Ranked #1 on
Graph Matching
on Willow Object Class
Relationship Quantification of Image Degradations
To tackle the first challenge, we proposed a Degradation Relationship Index (DRI) which is defined as the mean drop rate difference in the validation loss between two models which are respectively trained using the anchor degradation and the mixture of the anchor and the auxiliary degradations.
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective
Existing studies have empirically observed that the resolution of the low-frequency region is easier to enhance than that of the high-frequency one.
RONO: Robust Discriminative Learning With Noisy Labels for 2D-3D Cross-Modal Retrieval
Recently, with the advent of Metaverse and AI Generated Content, cross-modal retrieval becomes popular with a burst of 2D and 3D data.
Incomplete Multi-view Clustering via Prototype-based Imputation
Thanks to our dual-stream model, both cluster- and view-specific information could be captured, and thus the instance commonality and view versatility could be preserved to facilitate IMvC.
Satellite Anomaly Detection Using Variance Based Genetic Ensemble of Neural Networks
Then these uncertainty levels and each predictive model suggested by GA are used to generate a new model, which is then used for forecasting the TS and AD.
Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval
Thanks to the ISS and CCA, our method could encode the discrimination into the domain-invariant embedding space for unsupervised cross-domain image retrieval.
HAPS-UAV-Enabled Heterogeneous Networks: A Deep Reinforcement Learning Approach
The integrated use of non-terrestrial network (NTN) entities such as the high-altitude platform station (HAPS) and low-altitude platform station (LAPS) has become essential elements in the space-air-ground integrated networks (SAGINs).
Semantic Invariant Multi-view Clustering with Fully Incomplete Information
To address this problem, we present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information that does not require any paired samples.
Noisy-Correspondence Learning for Text-to-Image Person Re-identification
Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal community, which aims to retrieve the target person based on a textual query.
Ranked #2 on
Text based Person Retrieval
on ICFG-PEDES
(using extra training data)
Decoupled Contrastive Multi-View Clustering with High-Order Random Walks
In recent, some robust contrastive multi-view clustering (MvC) methods have been proposed, which construct data pairs from neighborhoods to alleviate the false negative issue, i. e., some intra-cluster samples are wrongly treated as negative pairs.
Image Clustering with External Guidance
The core of clustering is incorporating prior knowledge to construct supervision signals.
Cross-modal Active Complementary Learning with Self-refining Correspondence
Recently, image-text matching has attracted more and more attention from academia and industry, which is fundamental to understanding the latent correspondence across visual and textual modalities.
Latency versus Transmission Power Trade-off in Free-Space Optical (FSO) Satellite Networks with Multiple Inter-Continental Connections
In the absence of satellite transmission power constraints, as the LISL range extends from the minimum feasible range of 1575 km to the maximum feasible range of 5016 km, the average total network latency decreases from 589 ms to 311 ms.
Free-Space Optical (FSO) Satellite Networks Performance Analysis: Transmission Power, Latency, and Outage Probability
For the Toronto--Sydney inter-continental connection in an FSOSN with Starlink's Phase 1 Version 3 constellation, when the LISL range is approximately 2, 900 km, the mean network latency and mean average satellite transmission power intersect are approximately 135 ms and 380 mW, respectively.
PointCloud-Text Matching: Benchmark Datasets and a Baseline
We observe that the data is challenging and with noisy correspondence due to the sparsity, noise, or disorder of point clouds and the ambiguity, vagueness, or incompleteness of texts, which make existing cross-modal matching methods ineffective for PTM.
Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly
In this paper, we propose CLiKA, a systematic framework to assess the cross-lingual knowledge alignment of LLMs in the Performance, Consistency and Conductivity levels, and explored the effect of multilingual pretraining and instruction tuning on the degree of alignment.
