Search Results for author:
Found 75 papers, 41 papers with code
Augmented Robust PCA For Foreground-Background Separation on Noisy, Moving Camera Video
This work presents a novel approach for robust PCA with total variation regularization for foreground-background separation and denoising on noisy, moving camera video.
Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Video
We perform extensive numerical experiments on both static and moving camera video subject to a variety of dense and sparse corruptions.
iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
Our core idea is that the appearance of a person or an object instance contains informative cues on which relevant parts of an image to attend to for facilitating interaction prediction.
Ranked #2 on
Human-Object Interaction Detection
on Ambiguious-HOI
Learning to Recommend with Multiple Cascading Behaviors
To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task.
LambdaOpt: Learn to Regularize Recommender Models in Finer Levels
We show how to employ LambdaOpt on matrix factorization, a classical model that is representative of a large family of recommender models.
UGAN: Untraceable GAN for Multi-Domain Face Translation
To address this issue, we propose a method called Untraceable GAN, which has a novel source classifier to differentiate which domain an image is translated from, and determines whether the translated image still retains the characteristics of the source domain.
PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer
In this paper, we address the makeup transfer task, which aims to transfer the makeup from a reference image to a source image.
AdversarialNAS: Adversarial Neural Architecture Search for GANs
In this paper, we propose an AdversarialNAS method specially tailored for Generative Adversarial Networks (GANs) to search for a superior generative model on the task of unconditional image generation.
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition
We validate the effectiveness of our method by transferring our pre-trained model to three different tasks, including action classification, temporal localization, and spatio-temporal action detection.
Price-aware Recommendation with Graph Convolutional Networks
Price, an important factor in marketing --- which determines whether a user will make the final purchase decision on an item --- surprisingly, has received relatively little scrutiny.
Bundle Recommendation with Graph Convolutional Networks
Existing solutions integrate user-item interaction modeling into bundle recommendation by sharing model parameters or learning in a multi-task manner, which cannot explicitly model the affiliation between items and bundles, and fail to explore the decision-making when a user chooses bundles.
Recapture as You Want
LGR module utilizes body skeleton knowledge to construct a layout graph that connects all relevant part features, where graph reasoning mechanism is used to propagate information among part nodes to mine their relations.
Disentangling User Interest and Conformity for Recommendation with Causal Embedding
We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.
DRG: Dual Relation Graph for Human-Object Interaction Detection
We tackle the challenging problem of human-object interaction (HOI) detection.
Ranked #26 on
Human-Object Interaction Detection
on V-COCO
NAS-DIP: Learning Deep Image Prior with Neural Architecture Search
Recent work has shown that the structure of deep convolutional neural networks can be used as a structured image prior for solving various inverse image restoration tasks.
Flow-edge Guided Video Completion
We present a new flow-based video completion algorithm.
Ranked #4 on
Video Inpainting
on DAVIS
Group-Buying Recommendation for Social E-Commerce
Group-buying recommendation for social e-commerce, which recommends an item list when users want to launch a group, plays an important role in the group success ratio and sales.
Portrait Neural Radiance Fields from a Single Image
We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait.
Language-Guided Global Image Editing via Cross-Modal Cyclic Mechanism
Given the cycle, we propose several free augmentation strategies to help our model understand various editing requests given the imbalanced dataset.
Learnable Embedding Sizes for Recommender Systems
Existing works that try to address the problem always cause a significant drop in recommendation performance or suffers from the limitation of unaffordable training time cost.
Dynamic View Synthesis from Dynamic Monocular Video
We present an algorithm for generating novel views at arbitrary viewpoints and any input time step given a monocular video of a dynamic scene.
PSGAN++: Robust Detail-Preserving Makeup Transfer and Removal
In this paper, we address the makeup transfer and removal tasks simultaneously, which aim to transfer the makeup from a reference image to a source image and remove the makeup from the with-makeup image respectively.
Efficient Data-specific Model Search for Collaborative Filtering
In this way, we can combinatorially generalize data-specific CF models, which have not been visited in the literature, from SOTA ones.
Room-and-Object Aware Knowledge Reasoning for Remote Embodied Referring Expression
The Remote Embodied Referring Expression (REVERIE) is a recently raised task that requires an agent to navigate to and localise a referred remote object according to a high-level language instruction.
Sequential Recommendation with Graph Neural Networks
This helps explicitly distinguish users' core interests, by forming dense clusters in the interest graph.
Mining the Benefits of Two-stage and One-stage HOI Detection
To this end, we propose a novel one-stage framework with disentangling human-object detection and interaction classification in a cascade manner.
Ranked #7 on
Human-Object Interaction Detection
on V-COCO
DGCN: Diversified Recommendation with Graph Convolutional Networks
These years much effort has been devoted to improving the accuracy or relevance of the recommendation system.
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems.
Improving Location Recommendation with Urban Knowledge Graph
We first conduct information propagation on two sub-graphs to learn the representations of POIs and users.
Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks
With the hypergraph convolutional networks, the social relations can be modeled in a more fine-grained manner, which more accurately depicts real users' preferences, and benefits the recommendation performance.
Session-aware Item-combination Recommendation with Transformer Network
In this paper, we detailedly describe our solution for the IEEE BigData Cup 2021: RL-based RecSys (Track 1: Item Combination Prediction).
Progressive Feature Interaction Search for Deep Sparse Network
Deep sparse networks (DSNs), of which the crux is exploring the high-order feature interactions, have become the state-of-the-art on the prediction task with high-sparsity features.
Remaining Useful Life Prediction Using Temporal Deep Degradation Network for Complex Machinery with Attention-based Feature Extraction
The degradation-related features extracted from the sensor streaming data with neural networks can dramatically improve the accuracy of the RUL prediction.
Disentangling Long and Short-Term Interests for Recommendation
Modeling user's long-term and short-term interests is crucial for accurate recommendation.
3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection
3D visual grounding aims to locate the referred target object in 3D point cloud scenes according to a free-form language description.
Reinforced Structured State-Evolution for Vision-Language Navigation
However, the crucial navigation clues (i. e., object-level environment layout) for embodied navigation task is discarded since the maintained vector is essentially unstructured.
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation
In particular, we design a cascading residual graph convolutional network structure, which enables our model to learn user preferences by continuously refining user embeddings across different types of behaviors.
Target-Driven Structured Transformer Planner for Vision-Language Navigation
Vision-language navigation is the task of directing an embodied agent to navigate in 3D scenes with natural language instructions.
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors.
Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design
Recommender systems are playing an increasingly important role in alleviating information overload and supporting users' various needs, e. g., consumption, socialization, and entertainment.
DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias
Recommender systems are prone to be misled by biases in the data.
DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
In particular, we first unify the fine-grained user similarity and the complex matching between user preferences and spatiotemporal activity into a heterogeneous hypergraph.
Causal Inference in Recommender Systems: A Survey and Future Directions
Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction.
Mutual Harmony: Sequential Recommendation with Dual Contrastive Network
Such user-centric recommendation will make it impossible for the provider to expose their new items, failing to consider the accordant interactions between user and item dimensions.
Adaptive Zone-Aware Hierarchical Planner for Vision-Language Navigation
In this paper, we propose an Adaptive Zone-aware Hierarchical Planner (AZHP) to explicitly divides the navigation process into two heterogeneous phases, i. e., sub-goal setting via zone partition/selection (high-level action) and sub-goal executing (low-level action), for hierarchical planning.
Robust Dynamic Radiance Fields
Dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene.
Dual-interest Factorization-heads Attention for Sequential Recommendation
In this paper, we propose Dual-interest Factorization-heads Attention for Sequential Recommendation (short for DFAR) consisting of feedback-aware encoding layer, dual-interest disentangling layer and prediction layer.
Robust Preference-Guided Denoising for Graph based Social Recommendation
Graph Neural Network(GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations.
Progressively Optimized Local Radiance Fields for Robust View Synthesis
For handling unknown poses, we jointly estimate the camera poses with radiance field in a progressive manner.
Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner.
Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network
Understanding and characterizing the vulnerability of urban infrastructures, which refers to the engineering facilities essential for the regular running of cities and that exist naturally in the form of networks, is of great value to us.
NEON: Living Needs Prediction System in Meituan
To address these two challenges, we design a system of living NEeds predictiON named NEON, consisting of three phases: feature mining, feature fusion, and multi-task prediction.
Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation
Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems.
Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback -- passive-negative feedback and traditional randomly-sampled negative feedback.
A Probabilistic Fluctuation based Membership Inference Attack for Diffusion Models
Membership Inference Attack (MIA) identifies whether a record exists in a machine learning model's training set by querying the model.
Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System
Specifically, in short-video recommendation, the easiest-to-collect user feedback is the skipping behavior, which leads to two critical challenges for the recommendation model.
Alleviating Video-Length Effect for Micro-video Recommendation
Micro-videos platforms such as TikTok are extremely popular nowadays.
Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception
Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue.
OmnimatteRF: Robust Omnimatte with 3D Background Modeling
Video matting has broad applications, from adding interesting effects to casually captured movies to assisting video production professionals.
Stance Detection with Collaborative Role-Infused LLM-Based Agents
Next, in the reasoning-enhanced debating stage, for each potential stance, we designate a specific LLM-based agent to advocate for it, guiding the LLM to detect logical connections between text features and stance, tackling the second challenge.
Large Language Model-Empowered Agents for Simulating Macroeconomic Activities
In this work, we take an early step in introducing a novel approach that leverages LLMs in macroeconomic simulation.
Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
Prior attempts have quantified the privacy risks of language models (LMs) via MIAs, but there is still no consensus on whether existing MIA algorithms can cause remarkable privacy leakage on practical Large Language Models (LLMs).
Inverse Learning with Extremely Sparse Feedback for Recommendation
In this paper, we propose a meta-learning method to annotate the unlabeled data from loss and gradient perspectives, which considers the noises in both positive and negative instances.
Mixed Attention Network for Cross-domain Sequential Recommendation
Recent proposed cross-domain sequential recommendation models such as PiNet and DASL have a common drawback relying heavily on overlapped users in different domains, which limits their usage in practical recommender systems.
Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment
Urban environments, characterized by their complex, multi-layered networks encompassing physical, social, economic, and environmental dimensions, face significant challenges in the face of rapid urbanization.
Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives
Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions.
SpecNeRF: Gaussian Directional Encoding for Specular Reflections
We show that our Gaussian directional encoding and geometry prior significantly improve the modeling of challenging specular reflections in neural radiance fields, which helps decompose appearance into more physically meaningful components.
Image Quality, Uniformity and Computation Improvement of Compressive Light Field Displays with U-Net
We apply the U-Net model for compressive light field synthesis.
Large Language Model Agent for Hyper-Parameter Optimization
Hyperparameter optimization is critical in modern machine learning, requiring expert knowledge, numerous trials, and high computational and human resources.
Improving Cognitive Diagnosis Models with Adaptive Relational Graph Neural Networks
Typically, these CD algorithms assist students by inferring their abilities (i. e., their proficiency levels on various knowledge concepts).
LLM4SBR: A Lightweight and Effective Framework for Integrating Large Language Models in Session-based Recommendation
However, constrained by high time and space costs, as well as the brief and anonymous nature of session data, the first LLM recommendation framework suitable for industrial deployment has yet to emerge in the field of SBR.
Identify Critical Nodes in Complex Network with Large Language Models
Identifying critical nodes in networks is a classical decision-making task, and many methods struggle to strike a balance between adaptability and utility.
Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation
We formalize our definition of a "deep" filter bubble within this context, and then explore various correlations within the data: first understanding the evolution of the deep filter bubble over time, and later revealing some of the factors that give rise to this phenomenon, such as specific categories, user demographics, and feedback type.
Eliminating Cross-modal Conflicts in BEV Space for LiDAR-Camera 3D Object Detection
Recent 3D object detectors typically utilize multi-sensor data and unify multi-modal features in the shared bird's-eye view (BEV) representation space.
Depression Detection on Social Media with Large Language Models
Depression detection aims to determine whether an individual suffers from depression by analyzing their history of posts on social media, which can significantly aid in early detection and intervention.
