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Found 37 papers, 23 papers with code
Multi-Behavior Graph Neural Networks for Recommender System
Recent years have witnessed the emerging success of many deep learning-based recommendation models for augmenting collaborative filtering architectures with various neural network architectures, such as multi-layer perceptron and autoencoder.
4K-NeRF: High Fidelity Neural Radiance Fields at Ultra High Resolutions
In this paper, we present a novel and effective framework, named 4K-NeRF, to pursue high fidelity view synthesis on the challenging scenarios of ultra high resolutions, building on the methodology of neural radiance fields (NeRF).
Compressing Volumetric Radiance Fields to 1 MB
Approximating radiance fields with volumetric grids is one of promising directions for improving NeRF, represented by methods like Plenoxels and DVGO, which achieve super-fast training convergence and real-time rendering.
A-ACT: Action Anticipation through Cycle Transformations
To incorporate this ability in intelligent systems a question worth pondering upon is how exactly do we anticipate?
An Efficient and Robust System for Vertically Federated Random Forest
As there is a growing interest in utilizing data across multiple resources to build better machine learning models, many vertically federated learning algorithms have been proposed to preserve the data privacy of the participating organizations.
Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning
Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e. g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes.
Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling
Due to the overlook of user's multi-behavioral patterns over different items, existing recommendation methods are insufficient to capture heterogeneous collaborative signals from user multi-behavior data.
Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network
The overlook of multiplex behavior relations can hardly recognize the multi-modal contextual signals across different types of interactions, which limit the feasibility of current recommendation methods.
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment.
Social Recommendation with Self-Supervised Metagraph Informax Network
To model relation heterogeneity, we design a metapath-guided heterogeneous graph neural network to aggregate feature embeddings from different types of meta-relations across users and items, em-powering SMIN to maintain dedicated representations for multi-faceted user- and item-wise dependencies.
Graph Meta Network for Multi-Behavior Recommendation
Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions.
Knowledge-aware Coupled Graph Neural Network for Social Recommendation
While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques.
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and high-level item relationships in a common latent space.
Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation
In particular: i) complex inter-dependencies across different types of user behaviors; ii) the incorporation of knowledge-aware item relations into the multi-behavior recommendation framework; iii) dynamic characteristics of multi-typed user-item interactions.
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization
To address the challenges of communication and computation resource utilization, we propose an asynchronous stochastic quasi-Newton (AsySQN) framework for VFL, under which three algorithms, i. e. AsySQN-SGD, -SVRG and -SAGA, are proposed.
Memory-Augmented Non-Local Attention for Video Super-Resolution
Those methods achieve limited performance as they suffer from the challenge in spatial frame alignment and the lack of useful information from similar LR neighbor frames.
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms.
Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark
To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured largescale dataset, named as DroneCrowd, formed by 112 video clips with 33, 600 HD frames in various scenarios.
Data Augmentation for Object Detection via Differentiable Neural Rendering
We propose a new offline data augmentation method for object detection, which semantically interpolates the training data with novel views.
Transformer-based Conditional Variational Autoencoder for Controllable Story Generation
In this paper, we advocate to revive latent variable modeling, essentially the power of representation learning, in the era of Transformers to enhance controllability without hurting state-of-the-art generation effectiveness.
Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events
Our paper is among the first ones by our knowledge to propose a model and to create datasets for the task of "outline to story".
Efficient Pig Counting in Crowds with Keypoints Tracking and Spatial-aware Temporal Response Filtering
Existing methods only focused on pig counting using single image, and its accuracy is challenged by several factors, including pig movements, occlusion and overlapping.
EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph
Existing fraud detection methods try to identify unexpected dense subgraphs and treat related nodes as suspicious.
Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network
This paper proposes a space-time multi-scale attention network (STANet) to solve density map estimation, localization and tracking in dense crowds of video clips captured by drones with arbitrary crowd density, perspective, and flight altitude.
Heterogeneous Deep Graph Infomax
The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.
Ranked #7 on
Heterogeneous Node Classification
on DBLP (PACT) 14k
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
Then we propose a novel loss function, named Adaptive Wing loss, that is able to adapt its shape to different types of ground truth heatmap pixels.
Ranked #6 on
Face Alignment
on 300W
Spatiotemporal CNN for Video Object Segmentation
Specifically, the temporal coherence branch pretrained in an adversarial fashion from unlabeled video data, is designed to capture the dynamic appearance and motion cues of video sequences to guide object segmentation.
Ranked #2 on
Semi-Supervised Video Object Segmentation
on YouTube
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+3
ScratchDet: Training Single-Shot Object Detectors from Scratch
Taking this advantage, we are able to explore various types of networks for object detection, without suffering from the poor convergence.
Multipath Sparse Coding Using Hierarchical Matching Pursuit
Complex real-world signals, such as images, contain discriminative structures that differ in many aspects including scale, invariance, and data channel.
Unsupervised Template Learning for Fine-Grained Object Recognition
It differs from recognition of basic categories, such as humans, tables, and computers, in that there are global similarities in shape or structure shared within a category, and the differences are in the details of the object parts.
Discriminatively Trained Sparse Code Gradients for Contour Detection
Finding contours in natural images is a fundamental problem that serves as the basis of many tasks such as image segmentation and object recognition.
A Joint Model of Language and Perception for Grounded Attribute Learning
As robots become more ubiquitous and capable, it becomes ever more important to enable untrained users to easily interact with them.
Kernel Descriptors for Visual Recognition
We highlight the kernel view of orientation histograms, and show that they are equivalent to a certain type of match kernels over image patches.
Twin gaussian processes for structured prediction
We describe twin Gaussian processes (TGP), a generic structured prediction method that uses Gaussian process (GP) priors on both covariates and responses, both multivariate, and estimates outputs by minimizing the Kullback-Leibler divergence between two GP modeled as normal distributions over finite index sets of training and testing examples, emphasizing the goal that similar inputs should produce similar percepts and this should hold, on average, between their marginal distributions.
Ranked #21 on
3D Human Pose Estimation
on HumanEva-I
Conditional Neural Fields
To model the nonlinear relationship between input features and outputs we propose Conditional Neural Fields (CNF), a new conditional probabilistic graphical model for sequence labeling.
Efficient Match Kernel between Sets of Features for Visual Recognition
To address this problem, we propose an efficient match kernel (EMK), which maps local features to a low dimensional feature space, average the resulting feature vectors to form a set-level feature, then apply a linear classifier.
