Search Results for author:
Found 74 papers, 27 papers with code
Microservice Deployment in Space Computing Power Networks via Robust Reinforcement Learning
With the growing demand for Earth observation, it is important to provide reliable real-time remote sensing inference services to meet the low-latency requirements.
WiS Platform: Enhancing Evaluation of LLM-Based Multi-Agent Systems Through Game-Based Analysis
In this paper, to facilitate the research on LLM-based MAS, we introduce an open, scalable, and real-time updated platform for accessing and analyzing the LLM-based MAS based on the games Who is Spy?"
Simple but Efficient: A Multi-Scenario Nearline Retrieval Framework for Recommendation on Taobao
In recommendation systems, the matching stage is becoming increasingly critical, serving as the upper limit for the entire recommendation process.
Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights
Despite the recognized potential of multimodal data to improve model accuracy, many large-scale industrial recommendation systems, including Taobao display advertising system, predominantly depend on sparse ID features in their models.
A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids
The distinctive contribution of this paper lies in its holistic approach to both static and dynamic uncertainties in smart grids.
Principled Preferential Bayesian Optimization
We study the problem of preferential Bayesian optimization (BO), where we aim to optimize a black-box function with only preference feedback over a pair of candidate solutions.
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction
A typical practice is privileged features distillation (PFD): train a teacher model using all features (including privileged ones) and then distill the knowledge from the teacher model using a student model (excluding the privileged features), which is then employed for online serving.
Robust Representation Learning for Unified Online Top-K Recommendation
The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations.
One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation
Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry.
Multi-Agent Bayesian Optimization with Coupled Black-Box and Affine Constraints
Additionally, the algorithm guarantees an $\mathcal{O}(N\sqrt{T})$ bound on the cumulative violation for the known affine constraints, where $N$ is the number of agents.
A Generalized Stopping Criterion for Real-Time MPC with Guaranteed Stability
Most of the real-time implementations of the stabilizing optimal control actions suffer from the necessity to provide high computational effort.
Hierarchical Masked 3D Diffusion Model for Video Outpainting
Our pipeline benefits from bidirectional learning of the mask modeling and thus can employ a hybrid strategy of infilling and interpolation when generating sparse frames.
Advancing Distributed AC Optimal Power Flow for Integrated Transmission-Distribution Systems
This paper introduces a distributed operational solution for coordinating integrated transmission-distribution (ITD) systems regarding data privacy.
Federated Linear Bandit Learning via Over-the-Air Computation
In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise.
Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks
To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed.
TextPainter: Multimodal Text Image Generation with Visual-harmony and Text-comprehension for Poster Design
Specifically, TextPainter takes the global-local background image as a hint of style and guides the text image generation with visual harmony.
Visual Captioning at Will: Describing Images and Videos Guided by a Few Stylized Sentences
To address these limitations, we explore the problem of Few-Shot Stylized Visual Captioning, which aims to generate captions in any desired style, using only a few examples as guidance during inference, without requiring further training.
Bayesian Optimization of Expensive Nested Grey-Box Functions
We consider the problem of optimizing a grey-box objective function, i. e., nested function composed of both black-box and white-box functions.
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao
Another line of work relies on costly uniform data that is inadequate to train industrial models.
COPR: Consistency-Oriented Pre-Ranking for Online Advertising
In this architecture, the pre-ranking model is expected to be a lightweight approximation of the ranking model, which handles more candidates with strict latency requirements.
Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach
However, we observe that a well-trained CVR prediction model often performs sub-optimally during sales promotions.
Edit As You Wish: Video Caption Editing with Multi-grained User Control
In this paper, we propose a novel \textbf{V}ideo \textbf{C}aption \textbf{E}diting \textbf{(VCE)} task to automatically revise an existing video description guided by multi-grained user requests.
Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints
This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances.
Unsupervised Domain Adaption with Pixel-level Discriminator for Image-aware Layout Generation
This paper focuses on using the GAN-based model conditioned on image contents to generate advertising poster graphic layouts, which requires an advertising poster layout dataset with paired product images and graphic layouts.
CF-Font: Content Fusion for Few-shot Font Generation
Content and style disentanglement is an effective way to achieve few-shot font generation.
CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints
In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints.
Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow
This algorithm achieves both computation and communication efficiency, as well as trajectory data privacy, through parallel calculation.
Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control
This paper develops an optimal EV charging/discharging control strategy for different EV users under dynamic environments to maximize EV users' benefits.
Motion and Appearance Adaptation for Cross-Domain Motion Transfer
Motion transfer aims to transfer the motion of a driving video to a source image.
Motion Transformer for Unsupervised Image Animation
Image animation aims to animate a source image by using motion learned from a driving video.
Lower Bounds on the Worst-Case Complexity of Efficient Global Optimization
In this paper, we study the worst-case complexity of the efficient global optimization problem and, in contrast to existing kernel-specific results, we derive a unified lower bound for the complexity of efficient global optimization in terms of the metric entropy of a ball in its corresponding reproducing kernel Hilbert space~(RKHS).
Geometry Aligned Variational Transformer for Image-conditioned Layout Generation
First, self-attention mechanism is adopted to model the contextual relationship within layout elements, while cross-attention mechanism is used to fuse the visual information of conditional images.
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
It can be attributed to the calibration ability of the pointwise loss since the prediction can be viewed as the click probability.
A Proximal-Point Lagrangian Based Parallelizable Nonconvex Solver for Bilinear Model Predictive Control
The proposed algorithm is validated on a simulation of an HVAC system control.
Scheduling Delays and Curtailment for Household Appliances with Deterministic Load Profiles using MPC
Smart home appliances can time-shift and curtail their power demand to assist demand side management or allow operation with limited power, as in an off-grid application.
Attract me to Buy: Advertisement Copywriting Generation with Multimodal Multi-structured Information
Therefore, we present a dataset, E-MMAD (e-commercial multimodal multi-structured advertisement copywriting), which requires, and supports much more detailed information in text generation.
Dual-Level Decoupled Transformer for Video Captioning
As for the latter, \textbf{\textit{"couple"}} means treating the generation of visual semantic and syntax-related words equally.
Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs
Through both quantitative and qualitative evaluations, we demonstrate that the proposed model can synthesize high-quality graphic layouts according to image compositions.
CapOnImage: Context-driven Dense-Captioning on Image
Existing image captioning systems are dedicated to generating narrative captions for images, which are spatially detached from the image in presentation.
Self-Supervised Text Erasing with Controllable Image Synthesis
The synthetic training images with erasure ground-truth are then fed to train a coarse-to-fine erasing network.
Estimation of Reliable Proposal Quality for Temporal Action Detection
For the region perspective, we introduce Region Evaluate Module (REM) which uses a new and efficient sampling method for proposal feature representation containing more contextual information compared with point feature to refine category score and proposal boundary.
Structure-Aware Motion Transfer with Deformable Anchor Model
Specifically, inspired by the known deformable part model (DPM), our DAM introduces two types of anchors or keypoints: i) a number of motion anchors that capture both appearance and motion information from the source image and driving video; ii) a latent root anchor, which is linked to the motion anchors to facilitate better learning of the representations of the object structure information.
Learning Pixel-Level Distinctions for Video Highlight Detection
To this end, we propose to learn pixel-level distinctions to improve the video highlight detection.
Rapid Scalable Distributed Power Flow with Open-Source Implementation
This new variant is characterized by using a reduced modelling method of the distributed AC PF problem, which is reformulated as a zero-residual least-squares problem with consensus constraints.
Over-the-Air Federated Learning via Second-Order Optimization
To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation.
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
Under non-convex settings, we derive the convergence performance of the FedZO algorithm on non-independent and identically distributed data and characterize the impact of the numbers of local iterates and participating edge devices on the convergence.
Move As You Like: Image Animation in E-Commerce Scenario
Creative image animations are attractive in e-commerce applications, where motion transfer is one of the import ways to generate animations from static images.
Robust Resource-Aware Self-triggered Model Predictive Control
The wide adoption of wireless devices in the Internet of Things requires controllers that are able to operate with limited resources, such as battery life.
Boosting Image Outpainting with Semantic Layout Prediction
Secondly, another GAN model is trained to synthesize real images based on the extended semantic layouts.
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
To achieve this goal, the advertising platform needs to identify the advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives.
Over-the-Air Computation via Reconfigurable Intelligent Surface
Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition property of multiple-access channels.
Robust Uncertainty Bounds in Reproducing Kernel Hilbert Spaces: A Convex Optimization Approach
The problem of establishing out-of-sample bounds for the values of an unkonwn ground-truth function is considered.
Learning the Best Pooling Strategy for Visual Semantic Embedding
Visual Semantic Embedding (VSE) is a dominant approach for vision-language retrieval, which aims at learning a deep embedding space such that visual data are embedded close to their semantic text labels or descriptions.
Distributed Optimization for Massive Connectivity
Massive device connectivity in Internet of Thing (IoT) networks with sporadic traffic poses significant communication challenges.
ALADIN-$α$ -- An open-source MATLAB toolbox for distributed non-convex optimization
This paper introduces an open-source software for distributed and decentralized non-convex optimization named ALADIN-$\alpha$.
Controllable Person Image Synthesis with Attribute-Decomposed GAN
This paper introduces the Attribute-Decomposed GAN, a novel generative model for controllable person image synthesis, which can produce realistic person images with desired human attributes (e. g., pose, head, upper clothes and pants) provided in various source inputs.
Ranked #6 on
Pose Transfer
on Deep-Fashion
DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial Images and Trajectories
We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map.
Ranked #5 on
Semantic Segmentation
on TLCGIS
FoveaBox: Beyound Anchor-based Object Detection
While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors.
SOLO: Segmenting Objects by Locations
We present a new, embarrassingly simple approach to instance segmentation in images.
Ranked #69 on
Instance Segmentation
on COCO test-dev
Task-Aware Monocular Depth Estimation for 3D Object Detection
In this paper, we first analyse the data distributions and interaction of foreground and background, then propose the foreground-background separated monocular depth estimation (ForeSeE) method, to estimate the foreground depth and background depth using separate optimization objectives and depth decoders.
Effective Domain Knowledge Transfer with Soft Fine-tuning
However, the datasets from source domain are simply discarded in the fine-tuning process.
UniVSE: Robust Visual Semantic Embeddings via Structured Semantic Representations
We propose Unified Visual-Semantic Embeddings (UniVSE) for learning a joint space of visual and textual concepts.
FoveaBox: Beyond Anchor-based Object Detector
In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate. We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis.
Ranked #91 on
Object Detection
on COCO test-dev
(APM metric)
Consistent Optimization for Single-Shot Object Detection
We present consistent optimization for single stage object detection.
Acquisition of Localization Confidence for Accurate Object Detection
The network acquires this confidence of localization, which improves the NMS procedure by preserving accurately localized bounding boxes.
Ranked #186 on
Object Detection
on COCO test-dev
Unified Perceptual Parsing for Scene Understanding
In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image.
Ranked #90 on
Semantic Segmentation
on ADE20K val
Learning Visually-Grounded Semantics from Contrastive Adversarial Samples
Begin with an insightful adversarial attack on VSE embeddings, we show the limitation of current frameworks and image-text datasets (e. g., MS-COCO) both quantitatively and qualitatively.
Repulsion Loss: Detecting Pedestrians in a Crowd
In this paper, we first explore how a state-of-the-art pedestrian detector is harmed by crowd occlusion via experimentation, providing insights into the crowd occlusion problem.
Ranked #9 on
Pedestrian Detection
on Caltech
(using extra training data)
MegDet: A Large Mini-Batch Object Detector
The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network, new framework, or novel loss design.
What Can Help Pedestrian Detection?
Aggregating extra features has been considered as an effective approach to boost traditional pedestrian detection methods.
Ranked #15 on
Pedestrian Detection
on Caltech
FastMask: Segment Multi-scale Object Candidates in One Shot
Objects appear to scale differently in natural images.
UnitBox: An Advanced Object Detection Network
In present object detection systems, the deep convolutional neural networks (CNNs) are utilized to predict bounding boxes of object candidates, and have gained performance advantages over the traditional region proposal methods.
Learning Deep Face Representation
Our basic network is capable of achieving high recognition accuracy ($85. 8\%$ on LFW benchmark) with only 8 dimension representation.
