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Found 152 papers, 44 papers with code
Aggregated Structural Representation with Large Language Models for Human-Centric Layout Generation
To address these challenges, we propose an Aggregation Structural Representation (ASR) module that integrates graph networks with large language models (LLMs) to preserve structural information while enhancing generative capability.
CONCORD: Concept-Informed Diffusion for Dataset Distillation
We demonstrate the efficacy of CONCORD by achieving state-of-the-art performance on ImageNet-1K and its subsets.
Improved Methods for Model Pruning and Knowledge Distillation
Model pruning is a performance optimization technique for large language models like R1 or o3-mini.
BiECVC: Gated Diversification of Bidirectional Contexts for Learned Video Compression
To tackle these challenges, we propose BiECVC, a BVC framework that incorporates diversified local and non-local context modeling along with adaptive context gating.
Multi-View Learning with Context-Guided Receptance for Image Denoising
The model is validated on multiple real-world image denoising datasets, outperforming the existing state-of-the-art methods quantitatively and reducing inference time up to 40\%.
MLICv2: Enhanced Multi-Reference Entropy Modeling for Learned Image Compression
In this paper, we present MLICv2 and MLICv2$^+$, enhanced versions of the MLIC series, featuring improved transform techniques, entropy modeling, and instance adaptability.
Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping
Our second algorithm incorporates a novel event-triggered refinement mechanism, which refines the utilized quantization level to enhance the precision of the estimated optimal solution.
TVC: Tokenized Video Compression with Ultra-Low Bitrate
The discrete tokens (i. e., code maps generated by FSQ) are partially masked using a strategic masking scheme, then compressed losslessly with a discrete checkerboard context model to reduce transmission overhead.
RealisDance-DiT: Simple yet Strong Baseline towards Controllable Character Animation in the Wild
In this paper, we propose a new perspective that, as long as the foundation model is powerful enough, straightforward model modifications with flexible fine-tuning strategies can largely address the above challenges, taking a step towards controllable character animation in the wild.
L-LBVC: Long-Term Motion Estimation and Prediction for Learned Bi-Directional Video Compression
Recently, learned video compression (LVC) has shown superior performance under low-delay configuration.
Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance
During collaborative training, we first adopt two SMs to form a co-prediction network and propose a dynamic-enhanced threshold strategy to divide the noisy data into different subsets, then select the clean and noisy samples from these subsets to feed the active annotator LLMs to rectify noisy samples.
CoSIL: Software Issue Localization via LLM-Driven Code Repository Graph Searching
CoSIL reduces the search space through module call graphs, iteratively searches the function call graph to obtain relevant contexts, and uses context pruning to control the search direction and manage contexts effectively.
How to Promote Autonomous Driving with Evolving Technology: Business Strategy and Pricing Decision
Specifically, for manufacturers with mature ADS technology, the bundle strategy provides consumers with a lower entry barrier by integrating SSH, making the flexible subscription model a dominant strategy; while perpetual licensing outperforms under the unbundle strategy.
Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarios
In recommender systems, the patterns of user behaviors (e. g., purchase, click) may vary greatly in different contexts (e. g., time and location).
ChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion Rate Modeling
Post-click conversion rate (CVR) estimation is a vital task in many recommender systems of revenue businesses, e. g., e-commerce and advertising.
HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates
Image compression under ultra-low bitrates remains challenging for both conventional learned image compression (LIC) and generative vector-quantized (VQ) modeling.
Mirror Descent Under Generalized Smoothness
In this paper, we address this limitation by introducing a new $\ell*$-smoothness concept that measures the norm of Hessians in terms of a general norm and its dual, and establish convergence for mirror-descent-type algorithms, matching the rates under the classic smoothness.
Exploring Contextual Attribute Density in Referring Expression Counting
In the scope of REC, we define the CAD as the measure of the information intensity of one certain fine-grained attribute in visual regions.
Efficiently Serving Large Multimodal Models Using EPD Disaggregation
Large Multimodal Models (LMMs) extend Large Language Models (LLMs) by handling diverse inputs such as images, audio, and video, but at the cost of adding a multimodal encoding stage that increases both computational and memory overhead.
Hybrid Local-Global Context Learning for Neural Video Compression
In this paper, we propose a hybrid context generation module, which combines the advantages of the above methods in an optimal way and achieves accurate compensation at a low bit cost.
DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression
Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment.
Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecasting
With the time-varying mechanistic affinity graphs computed with the epidemiology-informed location embeddings, a heterogeneous transmission graph network is designed to encode the mechanistic heterogeneity among locations, providing additional predictive signals to facilitate accurate forecasting.
Epidemiology-informed Network for Robust Rumor Detection
Given the variations in topics and social impact of the root node, different source information naturally has distinct outreach capabilities, resulting in different heights of propagation trees.
ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression
In Learned Video Compression (LVC), improving inter prediction, such as enhancing temporal context mining and mitigating accumulated errors, is crucial for boosting rate-distortion performance.
A Visual Cooperative Localization Method for Airborne Magnetic Surveying Based on a Manifold Sensor Fusion Algorithm Using Lie Groups
The solution incorporates a visual processing module and an improved manifold-based sensor fusion algorithm, delivering reliable and accurate positioning information.
Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks
Spiking Neural Networks (SNNs), the third generation neural networks, are known for their low energy consumption and high robustness.
Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement
In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels.
GameIR: A Large-Scale Synthesized Ground-Truth Dataset for Image Restoration over Gaming Content
The first is super-resolution with deferred rendering, to support the gaming solution of rendering and transferring LR images only and restoring HR images on the client side.
A Survey of Trojan Attacks and Defenses to Deep Neural Networks
Deep Neural Networks (DNNs) have found extensive applications in safety-critical artificial intelligence systems, such as autonomous driving and facial recognition systems.
Physics-guided Active Sample Reweighting for Urban Flow Prediction
Moreover, due to the interval-based predictions and intermittent nature of data filing in many transportation services, the instantaneous dynamics of urban flows can hardly be captured, rendering differential equation-based continuous modeling a loose fit for this setting.
Exposure Completing for Temporally Consistent Neural High Dynamic Range Video Rendering
Combining the interpolated and given LDR frames, the complete set of exposure information is available at each time stamp.
Task-oriented Over-the-air Computation for Edge-device Co-inference with Balanced Classification Accuracy
By maximizing the minimum pair-wise discriminant gain instead of its average counterpart, any pair of classes can be better separated in the feature space, and thus leading to a balanced and improved inference accuracy for all classes.
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things
Network slicing enables industrial Internet of Things (IIoT) networks with multiservice and differentiated resource requirements to meet increasing demands through efficient use and management of network resources.
Hacking Encrypted Wireless Power: Cyber-Security of Dynamic Charging
To stimulate the progress of energy encryption technology and point out security holes, this paper proposes a decryption method for the fundamental principle of encrypted frequency-varying wireless power transfer.
Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization
Existing projection-free algorithms for solving this problem suffer from two limitations: 1) they solely focus on the gradient mapping criterion and fail to match the optimal sample complexities in unconstrained settings; 2) their analysis is exclusively applicable to non-convex functions, without considering convex and strongly convex objectives.
Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions
This paper explores adaptive variance reduction methods for stochastic optimization based on the STORM technique.
Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction
Sign stochastic gradient descent (signSGD) is a communication-efficient method that transmits only the sign of stochastic gradients for parameter updating.
HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression
This paper investigates the challenging problem of learned image compression (LIC) with extreme low bitrates.
Image and Video Compression using Generative Sparse Representation with Fidelity Controls
Our framework can be conveniently used for both learned image compression (LIC) and learned video compression (LVC).
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping
In this work, we developed a 3D image patch-based diffusion model, namely QSMDiff, for robust QSM reconstruction across different scan parameters, alongside simultaneous super-resolution and image-denoising tasks.
Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond
In this paper, we investigate the empirical counterpart of Group Distributionally Robust Optimization (GDRO), which aims to minimize the maximal empirical risk across $m$ distinct groups.
REPOFUSE: Repository-Level Code Completion with Fused Dual Context
The success of language models in code assistance has spurred the proposal of repository-level code completion as a means to enhance prediction accuracy, utilizing the context from the entire codebase.
Group Distributionally Robust Dataset Distillation with Risk Minimization
The most popular methods for constructing the synthetic data rely on matching the convergence properties of training the model with the synthetic dataset and the training dataset.
UCVC: A Unified Contextual Video Compression Framework with Joint P-frame and B-frame Coding
This paper presents a learned video compression method in response to video compression track of the 6th Challenge on Learned Image Compression (CLIC), at DCC 2024. Specifically, we propose a unified contextual video compression framework (UCVC) for joint P-frame and B-frame coding.
LVC-LGMC: Joint Local and Global Motion Compensation for Learned Video Compression
To validate the effectiveness of our proposed LGMC, we integrate it with DCVC-TCM and obtain learned video compression with joint local and global motion compensation (LVC-LGMC).
Tensor-based process control and monitoring for semiconductor manufacturing with unstable disturbances
This paper proposes a novel process control and monitoring method for the complex structure of high-dimensional image-based overlay errors (modeled in tensor form), which are collected in semiconductor manufacturing processes.
Challenging Low Homophily in Social Recommendation
To comprehensively extract preference-aware homophily information latent in the social graph, we propose Social Heterophily-alleviating Rewiring (SHaRe), a data-centric framework for enhancing existing graph-based social recommendation models.
TLIC: Learned Image Compression with ROI-Weighted Distortion and Bit Allocation
This short paper describes our method for the track of image compression.
KEEC: Koopman Embedded Equivariant Control
To address these challenges, we propose Koopman Embedded Equivariant Control (KEEC) to learn an embedding of the states and vector fields such that a Koopman operator is approximated as the latent dynamics.
Efficient Dataset Distillation via Minimax Diffusion
Observing that key factors for constructing an effective surrogate dataset are representativeness and diversity, we design additional minimax criteria in the generative training to enhance these facets for the generated images of diffusion models.
Fast Controllable Diffusion Models for Undersampled MRI Reconstruction
Supervised deep learning methods have shown promise in undersampled Magnetic Resonance Imaging (MRI) reconstruction, but their requirement for paired data limits their generalizability to the diverse MRI acquisition parameters.
Discretized Distributed Optimization over Dynamic Digraphs
This work, particularly, is an improvement over existing stochastic-weight undirected networks in case of link removal or packet drops.
VGSG: Vision-Guided Semantic-Group Network for Text-based Person Search
With the help of relational knowledge transfer, VGKT is capable of aligning semantic-group textual features with corresponding visual features without external tools and complex pairwise interaction.
Ranked #9 on
Text based Person Retrieval
on ICFG-PEDES
DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching
Dataset distillation plays a crucial role in creating compact datasets with similar training performance compared with original large-scale ones.
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
It is specifically designed for code-related tasks with both English and Chinese prompts and supports over 40 programming languages.
Neural Image Compression Using Masked Sparse Visual Representation
A set of semantic-class-dependent basis codebooks are learned, which are weighted combined to generate a rich latent feature for high-quality reconstruction.
MFRL-BI: Design of a Model-free Reinforcement Learning Process Control Scheme by Using Bayesian Inference
Design of process control scheme is critical for quality assurance to reduce variations in manufacturing systems.
A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty
Aggregating distributed energy resources in power systems significantly increases uncertainties, in particular caused by the fluctuation of renewable energy generation.
CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending
In fact, anomalous behaviors harming long context extrapolation exist between Rotary Position Embedding (RoPE) and vanilla self-attention unveiled by our work.
Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication
Distributed methods in which nodes use quantized communication yield a solution at the proximity of the optimal solution, hence reaching an error floor that depends on the quantization level used; the finer the quantization the lower the error floor.
Region Generation and Assessment Network for Occluded Person Re-Identification
Then, to measure the importance of each generated region, we introduce a Region Assessment Module (RAM) that assigns confidence scores to different regions and reduces the negative impact of the occlusion regions by lower scores.
Color Prompting for Data-Free Continual Unsupervised Domain Adaptive Person Re-Identification
In this work, we propose a Color Prompting (CoP) method for data-free continual unsupervised domain adaptive person Re-ID.
Domain Adaptive Person Re-Identification
Person Re-Identification
+1
Exploring the Physical World Adversarial Robustness of Vehicle Detection
Through this pipeline, we establish the Discrete and Continuous Instant-level (DCI) dataset, enabling comprehensive experiments involving three detection models and three physical adversarial attacks.
Continual Learning in Predictive Autoscaling
In order to surmount this challenge and effectively integrate new sample distribution, we propose a density-based sample selection strategy that utilizes kernel density estimation to calculate sample density as a reference to compute sample weight, and employs weight sampling to construct a new memory set.
MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression
Additionally, to capture global contexts, we propose the linear complexity attention-based global correlations capturing by leveraging the decomposition of the softmax operation.
Ranked #1 on
Image Compression
on kodak
Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive Review
Different from earlier surveys, this paper presents a comprehensive treatment and technology survey on THz communications and sensing in terms of advantages, applications, propagation characterization, channel modeling, measurement campaigns, antennas, transceiver devices, beamforming, networking, the integration of communications and sensing, and experimental testbeds.
Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation
The inter-class relationships of semantic-related visual features are then required to be aligned with those in semantic space, thereby transferring semantic knowledge to visual feature learning.
A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management
Multi-agent reinforcement learning (MARL) models multiple agents that interact and learn within a shared environment.
Learning Unnormalized Statistical Models via Compositional Optimization
Learning unnormalized statistical models (e. g., energy-based models) is computationally challenging due to the complexity of handling the partition function.
Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles
Current testing scenarios typically employ predefined or scripted BVs, which inadequately reflect the complexity of human-like social behaviors in real-world driving scenarios, and also lack a systematic metric for evaluating the comprehensive intelligence of AVs.
AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing
Developing monolingual large Pre-trained Language Models (PLMs) is shown to be very successful in handling different tasks in Natural Language Processing (NLP).
Summarizing Stream Data for Memory-Constrained Online Continual Learning
Through maintaining the consistency of training gradients and relationship to the past tasks, the summarized samples are more representative for the stream data compared to the original images.
Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentation
In this work, we address the challenging task of few-shot and zero-shot 3D point cloud semantic segmentation.
Semantic-Promoted Debiasing and Background Disambiguation for Zero-Shot Instance Segmentation
It is desired to rescue novel objects from background and dominated seen categories.
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection
Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services.
Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees
Specifically, we first provide a novel dynamic regret analysis for an existing projection-free method named $\text{BOGD}_\text{IP}$, and establish an $\mathcal{O}(T^{3/4}(1+P_T))$ dynamic regret bound, where $P_T$ denotes the path-length of the comparator sequence.
HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform
In recent years, there has been rapid development in learned image compression techniques that prioritize ratedistortion-perceptual compression, preserving fine details even at lower bit-rates.
LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression
To tackle this issue, we propose Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression (LLIC).
MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID
Neural Architecture Search (NAS) has been increasingly appealing to the society of object Re-Identification (ReID), for that task-specific architectures significantly improve the retrieval performance.
Ranked #8 on
Vehicle Re-Identification
on VehicleID Large
DiM: Distilling Dataset into Generative Model
To the best of our knowledge, we are the first to achieve higher accuracy on complex architectures than simple ones, such as 75. 1\% with ResNet-18 and 72. 6\% with ConvNet-3 on ten images per class of CIFAR-10.
DREAM: Efficient Dataset Distillation by Representative Matching
Although there are various matching objectives, currently the strategy for selecting original images is limited to naive random sampling.
On Function-Coupled Watermarks for Deep Neural Networks
Various watermarking techniques are proposed to protect such intellectual properties (IPs), wherein the DNN providers implant secret information into the model so that they can later claim IP ownership by retrieving their embedded watermarks with some dedicated trigger inputs.
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management
In this paper, we consider the inventory management (IM) problem where we need to make replenishment decisions for a large number of stock keeping units (SKUs) to balance their supply and demand.
Distributed Optimization with Quantized Gradient Descent
For solving this distributed optimization problem, we combine a gradient descent method with a distributed quantized consensus algorithm (which requires the nodes to exchange quantized messages and converges in a finite number of steps).
MLIC: Multi-Reference Entropy Model for Learned Image Compression
Based on MEM and MEM$^+$, we propose image compression models MLIC and MLIC$^+$.
Ranked #1 on
Image Compression
on kodak
DTAC-ADMM: Delay-Tolerant Augmented Consensus ADMM-based Algorithm for Distributed Resource Allocation
Every node locally updates its state toward optimizing a global allocation cost function via received information of its neighbouring nodes even when the data exchange over the network is heterogeneously delayed at different links.
KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution
KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind power dataset, in which the participants are required to predict the future generation given the historical context factors.
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization
The key issue is to track and estimate a sequence of $\mathbf g(\mathbf{w})=(g_1(\mathbf{w}), \ldots, g_m(\mathbf{w}))$ across iterations, where $\mathbf g(\mathbf{w})$ has $m$ blocks and it is only allowed to probe $\mathcal{O}(1)$ blocks to attain their stochastic values and Jacobians.
Dynamic Gradient Reactivation for Backward Compatible Person Re-identification
To address this issue, we propose the Ranking-based Backward Compatible Learning (RBCL), which directly optimizes the ranking metric between new features and old features.
FAIVConf: Face enhancement for AI-based Video Conference with Low Bit-rate
Recently, high-quality video conferencing with fewer transmission bits has become a very hot and challenging problem.
Facke: a Survey on Generative Models for Face Swapping
In this work, we investigate into the performance of mainstream neural generative models on the very task of swapping faces.
Coarse-to-fine Deep Video Coding with Hyperprior-guided Mode Prediction
The previous deep video compression approaches only use the single scale motion compensation strategy and rarely adopt the mode prediction technique from the traditional standards like H. 264/H. 265 for both motion and residual compression.
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor
In this paper, we investigate an online prediction strategy named as Discounted-Normal-Predictor (Kapralov and Panigrahy, 2010) for smoothed online convex optimization (SOCO), in which the learner needs to minimize not only the hitting cost but also the switching cost.
Towards Homogeneous Modality Learning and Multi-Granularity Information Exploration for Visible-Infrared Person Re-Identification
In order to mitigate the impact of large modality discrepancy existing in heterogeneous images, previous methods attempt to apply generative adversarial network (GAN) to generate the modality-consisitent data.
NeuMan: Neural Human Radiance Field from a Single Video
Photorealistic rendering and reposing of humans is important for enabling augmented reality experiences.
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization
To address these limitations, we propose a Stochastic Multi-level Variance Reduction method (SMVR), which achieves the optimal sample complexity of $\mathcal{O}\left(1 / \epsilon^{3}\right)$ to find an $\epsilon$-stationary point for non-convex objectives.
Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication
This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition by exploiting the distributed integrated sensing and communication (ISAC).
LSVC: A Learning-Based Stereo Video Compression Framework
In this work, we propose the first end-to-end optimized framework for compressing automotive stereo videos (i. e., stereo videos from autonomous driving applications) from both left and right views.
Fast Data-Driven Adaptation of Radar Detection via Meta-Learning
One approach is based on transfer learning: it first pre-trains a detector such that it works well on data collected in previously observed environments, and then it adapts the pre-trained detector to the specific current environment.
Online Meta Adaptation for Variable-Rate Learned Image Compression
This work addresses two major issues of end-to-end learned image compression (LIC) based on deep neural networks: variable-rate learning where separate networks are required to generate compressed images with varying qualities, and the train-test mismatch between differentiable approximate quantization and true hard quantization.
Reinforcement Learning for Process Control with Application in Semiconductor Manufacturing
Process control is widely discussed in the manufacturing process, especially for semiconductor manufacturing.
A Two-stage Pricing Strategy Considering Learning Effects and Word-of-Mouth
This paper proposes a two-stage pricing strategy for nondurable (such as typical electronics) products, where retail price is cut down at certain time points of the product lifecycle.
Machine learning based iterative learning control for non-repetitive time-varying systems
A machine learning (ML) based nominal model update mechanism, which utilizes the linear regression technique to update the nominal model at each ILC trial only using the current trial information, is proposed for non-repetitive TVSs in order to enhance the ILC performance.
Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression
Compressing Deep Neural Network (DNN) models to alleviate the storage and computation requirements is essential for practical applications, especially for resource limited devices.
Substitutional Neural Image Compression
The key idea is to replace the image to be compressed with a substitutional one that outperforms the original one in a desired way.
Multi-agent consensus with heterogeneous time-varying input and communication delays in digraphs
First, for all agents whose state matrix has no eigenvalues with positive real parts, a communication-delay-related observer, which is used to construct the controller, is designed for followers to estimate the leader's state information.
An Iteratively Reweighted Method for Sparse Optimization on Nonconvex $\ell_{p}$ Ball
Specifically, if the current iterate is in the interior of the feasible set, then the weighted $\ell_{1}$ ball is formed by linearizing the $\ell_{p}$ norm at the current iterate.
COTR: Correspondence Transformer for Matching Across Images
We propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence in the other.
Ranked #1 on
Dense Pixel Correspondence Estimation
on KITTI 2012
Dense Pixel Correspondence Estimation
Optical Flow Estimation
Joint Design of Radar Waveform and Detector via End-to-end Learning with Waveform Constraints
In the second approach, the transmitter and detector are trained simultaneously.
Revisiting Smoothed Online Learning
Moreover, when the hitting cost is both convex and $\lambda$-quadratic growth, we reduce the competitive ratio to $1 + \frac{2}{\sqrt{\lambda}}$ by minimizing the weighted sum of the hitting cost and the switching cost.
TransReID: Transformer-based Object Re-Identification
Extracting robust feature representation is one of the key challenges in object re-identification (ReID).
Ranked #1 on
Person Re-Identification
on Market-1501-C
Predictive Relay Selection: A Cooperative Diversity Scheme Using Deep Learning
In this paper, we propose a novel cooperative multi-relay transmission scheme for mobile terminals to exploit spatial diversity.
Information Theory Signal Processing Information Theory
A Simple Cooperative Diversity Method Based on Deep-Learning-Aided Relay Selection
Opportunistic relay selection (ORS) has been recognized as a simple but efficient method for mobile nodes to achieve cooperative diversity in slow fading channels.
The Road Towards 6G: A Comprehensive Survey
As of today, the fifth generation (5G) mobile communication system has been rolled out in many countries and the number of 5G subscribers already reaches a very large scale.
Implementation of OpenAirInterface-based real-world channel measurement for evaluating wireless transmission algorithms
The results from the measurement are then trained and tested on the channel prediction algorithm.
An Abstracted Survey on 6G: Drivers, Requirements, Efforts, and Enablers
As of today, 5G mobile systems have been already widely rolled out, it is the right time for academia and industry to explore the next generation mobile communication system beyond 5G.
Networking and Internet Architecture
1st Place Solution to VisDA-2020: Bias Elimination for Domain Adaptive Pedestrian Re-identification
Considering the large gap between the source domain and target domain, we focused on solving two biases that influenced the performance on domain adaptive pedestrian Re-ID and proposed a two-stage training procedure.
A pipeline for fair comparison of graph neural networks in node classification tasks
We introduce a standard, reproducible benchmark to which the same training settings can be applied for node classification.
DeRF: Decomposed Radiance Fields
Moreover, we show that a Voronoi spatial decomposition is preferable for this purpose, as it is provably compatible with the Painter's Algorithm for efficient and GPU-friendly rendering.
SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation
Recovering multi-person 3D poses with absolute scales from a single RGB image is a challenging problem due to the inherent depth and scale ambiguity from a single view.
Ranked #11 on
3D Multi-Person Pose Estimation (absolute)
on MuPoTS-3D
ArchNet: Data Hiding Model in Distributed Machine Learning System
Specifically, our design can improve the accuracy on MNIST up to 97. 26% compared with RC4. The accuracies on the datasets encrypted by ArchNet are 97. 26%, 84. 15% and 79. 80%, and they are 97. 31%, 82. 31% and 80. 22% on the original datasets, which shows that the encrypted accuracy of ArchNet has the same performance as the base model.
Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID.
Survey of Privacy-Preserving Collaborative Filtering
Collaborative filtering recommendation systems provide recommendations to users based on their own past preferences, as well as those of other users who share similar interests.
Improving Uyghur ASR systems with decoders using morpheme-based language models
The experimental results show that the decoding based on the static and fully composed graph reduces state-of-the-art Word Error Rate (WER) on the clean and noiseless speech test task in THUYG-20 to 14. 24%.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Cross-Spectrum Dual-Subspace Pairing for RGB-infrared Cross-Modality Person Re-Identification
Another challenge of RGB-infrared ReID is that the intra-person (images from the same person) discrepancy is often larger than the inter-person (images from different persons) discrepancy, so a dual-subspace pairing strategy is proposed to alleviate this problem.
End-to-end Learning of Waveform Generation and Detection for Radar Systems
An end-to-end learning approach is proposed for the joint design of transmitted waveform and detector in a radar system.
Signal Processing
SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT
Our final submission ranks the third on the overall MRP evaluation metric, the first on EDS and the second on UCCA.
Stripe-based and Attribute-aware Network: A Two-Branch Deep Model for Vehicle Re-identification
Vehicle re-identification (Re-ID) has been attracting increasing interest in the field of computer vision due to the growing utilization of surveillance cameras in public security.
Optimizing Through Learned Errors for Accurate Sports Field Registration
We propose an optimization-based framework to register sports field templates onto broadcast videos.
Adaptive Bayesian SLOPE -- High-dimensional Model Selection with Missing Values
We consider the problem of variable selection in high-dimensional settings with missing observations among the covariates.
Methodology Applications Computation
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning
Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds.
A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification
The present study collects and evaluates these effective training tricks in person ReID.
Ranked #48 on
Person Re-Identification
on DukeMTMC-reID
Analytical Derivatives for Differentiable Renderer: 3D Pose Estimation by Silhouette Consistency
The experimental results also show that the proposed differentiable renderer is of higher accuracy and efficiency compared with previous method of differentiable renderer.
A Dual-level Model Predictive Control Scheme for Multi-timescale Dynamical Systems--Extended Version
So far, many control algorithms have been developed for singularly perturbed systems.
HLT@SUDA at SemEval-2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing
The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery.
Ranked #1 on
UCCA Parsing
on SemEval 2019 Task 1
A Seq-to-Seq Transformer Premised Temporal Convolutional Network for Chinese Word Segmentation
The prevalent approaches of Chinese word segmentation task almost rely on the Bi-LSTM neural network.
ShopSign: a Diverse Scene Text Dataset of Chinese Shop Signs in Street Views
Hence, we collect and annotate the ShopSign dataset to advance research in Chinese scene text detection and recognition.
Bag of Tricks and A Strong Baseline for Deep Person Re-identification
In the literature, some effective training tricks are briefly appeared in several papers or source codes.
Ranked #2 on
Person Re-Identification
on UAV-Human
STNReID : Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification
Competition (or confrontation) is observed between the STN module and the ReID module, and two-stage training is applied to acquire a strong STNReID for partial ReID.
An End-to-End Network for Panoptic Segmentation
Panoptic segmentation, which needs to assign a category label to each pixel and segment each object instance simultaneously, is a challenging topic.
HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing
The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery.
Improve Object Detection by Data Enhancement based on Generative Adversarial Nets
The accuracy of the object detection model depends on whether the anchor boxes effectively trained.
A Novel Self-Intersection Penalty Term for Statistical Body Shape Models and Its Applications in 3D Pose Estimation
To calculate the partial derivatives with respect to the coordinates of the vertices, we employed detection rays to divide vertices of statistical body shape models into different groups depending on whether the vertex is in the region of self-intersection.
Linearized Multi-Sampling for Differentiable Image Transformation
We propose a novel image sampling method for differentiable image transformation in deep neural networks.
SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification
Holistic person re-identification (ReID) has received extensive study in the past few years and achieves impressive progress.
Distributing and Obfuscating Firewalls via Oblivious Bloom Filter Evaluation
In this paper, we develop an innovative distributed architecture to obliviously manage and evaluate firewalls to prevent both insider and external attacks oriented to the firewalls.
Cryptography and Security
SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification
In this paper, we use a modified softmax function, termed Sphere Softmax, to solve the classification problem and learn a hypersphere manifold embedding simultaneously.
DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block
We propose the DFNet and make two main contributions, one is dynamic loss weights, and the other is residual fusion block (RFB).
Stochastic Approximation EM for Logistic Regression with Missing Values
We propose a complete approach, including the estimation of parameters and their variance, derivation of confidence intervals, a model selection procedure, and a method for prediction on test sets with missing values.
Methodology
VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View
At the same time, we proposed a highly fused convolutional network (HFCN) based segmentation method for parking slot and lane markings based on the PSV dataset.
AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
In this paper, we propose a novel method called AlignedReID that extracts a global feature which is jointly learned with local features.
Ranked #1 on
Person Re-Identification
on CUHK-SYSU
A Feature Embedding Strategy for High-level CNN representations from Multiple ConvNets
Following the rapidly growing digital image usage, automatic image categorization has become preeminent research area.
