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Found 55 papers, 31 papers with code
Distributionally Robust Graph-based Recommendation System
DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support.
Knowledge Translation: A New Pathway for Model Compression
Deep learning has witnessed significant advancements in recent years at the cost of increasing training, inference, and model storage overhead.
StyleRetoucher: Generalized Portrait Image Retouching with GAN Priors
To address such issues, we present StyleRetoucher, a novel automatic portrait image retouching framework, leveraging StyleGAN's generation and generalization ability to improve an input portrait image's skin condition while preserving its facial details.
Mesh-Guided Neural Implicit Field Editing
We first introduce a differentiable method using marching tetrahedra for polygonal mesh extraction from the neural implicit field and then design a differentiable color extractor to assign colors obtained from the volume renderings to this extracted mesh.
Fast ODE-based Sampling for Diffusion Models in Around 5 Steps
Sampling from diffusion models can be treated as solving the corresponding ordinary differential equations (ODEs), with the aim of obtaining an accurate solution with as few number of function evaluations (NFE) as possible.
Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction
Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task.
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning
The proposed method decouples functions between the decoder and the encoder by introducing a mask regressor, which predicts the masked patch representation from the visible patch representation encoded by the encoder and the decoder reconstructs the target from the predicted masked patch representation.
Ranked #3 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 10-way (20-shot)
(using extra training data)
Few-Shot 3D Point Cloud Classification
Representation Learning
+1
CDR: Conservative Doubly Robust Learning for Debiased Recommendation
In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data.
Customizing Synthetic Data for Data-Free Student Learning
Existing works generally synthesize data from the pre-trained teacher model to replace the original training data for student learning.
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
Moreover, we observe that the learned graph structure demonstrates a strong generalization ability across different GNN models, despite the high computational and space consumption.
Adaptive Multi-Teacher Knowledge Distillation with Meta-Learning
Multi-Teacher knowledge distillation provides students with additional supervision from multiple pre-trained teachers with diverse information sources.
A Geometric Perspective on Diffusion Models
Recent years have witnessed significant progress in developing effective training and fast sampling techniques for diffusion models.
Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields
Extensive experiments demonstrate that our Text2NeRF outperforms existing methods in producing photo-realistic, multi-view consistent, and diverse 3D scenes from a variety of natural language prompts.
Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture Recognition
Then, channel-dependent and temporal-dependent adjacency matrices corresponding to different channels and frames are calculated to capture the spatiotemporal dependencies between skeleton joints.
AvatarCraft: Transforming Text into Neural Human Avatars with Parameterized Shape and Pose Control
Neural implicit fields are powerful for representing 3D scenes and generating high-quality novel views, but it remains challenging to use such implicit representations for creating a 3D human avatar with a specific identity and artistic style that can be easily animated.
Jointly Complementary&Competitive Influence Maximization with Concurrent Ally-Boosting and Rival-Preventing
In this paper, we propose a new influence spread model, namely, Complementary\&Competitive Independent Cascade (C$^2$IC) model.
Robust Sequence Networked Submodular Maximization
The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology.
NeRF-Art: Text-Driven Neural Radiance Fields Stylization
As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images.
Accelerating Diffusion Sampling with Classifier-based Feature Distillation
Instead of aligning output images, we distill teacher's sharpened feature distribution into the student with a dataset-independent classifier, making the student focus on those important features to improve performance.
Online Cross-Layer Knowledge Distillation on Graph Neural Networks with Deep Supervision
Alignahead++ transfers structure and feature information in a student layer to the previous layer of another simultaneously trained student model in an alternating training procedure.
FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing
Unlike existing dynamic NeRFs that require dense images as input and can only be modeled for a single identity, our method enables face reconstruction across different persons with few-shot inputs.
Improving Knowledge Graph Embedding via Iterative Self-Semantic Knowledge Distillation
Iteratively incorporating and accumulating iteration-based semantic information enables the low-dimensional model to be more expressive for better link prediction in KGs.
Alignahead: Online Cross-Layer Knowledge Extraction on Graph Neural Networks
Existing knowledge distillation methods on graph neural networks (GNNs) are almost offline, where the student model extracts knowledge from a powerful teacher model to improve its performance.
Knowledge Distillation with the Reused Teacher Classifier
Knowledge distillation aims to compress a powerful yet cumbersome teacher model into a lightweight student model without much sacrifice of performance.
Ranked #3 on
Knowledge Distillation
on CIFAR-100
Knowledge Distillation with Deep Supervision
Knowledge distillation aims to enhance the performance of a lightweight student model by exploiting the knowledge from a pre-trained cumbersome teacher model.
Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization
PL approaches apply pseudo-labels to unlabeled data, and then train the model with a combination of the labeled and pseudo-labeled data iteratively.
Confidence-Aware Multi-Teacher Knowledge Distillation
Knowledge distillation is initially introduced to utilize additional supervision from a single teacher model for the student model training.
Online Adversarial Distillation for Graph Neural Networks
However, its effect on graph neural networks is less than satisfactory since the graph topology and node attributes are likely to change in a dynamic way and in this case a static teacher model is insufficient in guiding student training.
CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields
Furthermore, we propose an inverse optimization method that accurately projects an input image to the latent codes for manipulation to enable editing on real images.
Exploring the Connection between Knowledge Distillation and Logits Matching
Knowledge distillation is a generalized logits matching technique for model compression.
Distilling Holistic Knowledge with Graph Neural Networks
The holistic knowledge is represented as a unified graph-based embedding by aggregating individual knowledge from relational neighborhood samples with graph neural networks, the student network is learned by distilling the holistic knowledge in a contrastive manner.
Deep Portrait Lighting Enhancement with 3D Guidance
To address this, we present a novel deep learning framework for portrait lighting enhancement based on 3D facial guidance.
Human Pose Regression with Residual Log-likelihood Estimation
In light of this, we propose a novel regression paradigm with Residual Log-likelihood Estimation (RLE) to capture the underlying output distribution.
Ranked #59 on
3D Human Pose Estimation
on Human3.6M
Cross-Domain and Disentangled Face Manipulation with 3D Guidance
Face image manipulation via three-dimensional guidance has been widely applied in various interactive scenarios due to its semantically-meaningful understanding and user-friendly controllability.
Cross-Layer Distillation with Semantic Calibration
Knowledge distillation is a technique to enhance the generalization ability of a student model by exploiting outputs from a teacher model.
SamWalker++: recommendation with informative sampling strategy
However, the social network information may not be available in many recommender systems, which hinders application of SamWalker.
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
To deal with these problems, we propose an efficient and effective collaborative sampling method CoSam, which consists of: (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency; and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling.
TRec: Sequential Recommender Based On Latent Item Trend Information
Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.
HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation
The HMOR encodes interaction information as the ordinal relations of depths and angles hierarchically, which captures the body-part and joint level semantic and maintains global consistency at the same time.
3D Multi-Person Pose Estimation (absolute)
3D Multi-Person Pose Estimation (root-relative)
+2
Whole-Body Human Pose Estimation in the Wild
This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet.
Ranked #8 on
2D Human Pose Estimation
on COCO-WholeBody
Spectrum-Guided Adversarial Disparity Learning
It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class.
Pose-aware Adversarial Domain Adaptation for Personalized Facial Expression Recognition
The first aims to learn pose- and expression-related feature representations in the source domain and adapt both feature distributions to that of the target domain by imposing adversarial learning.
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.
Ranked #2 on
Traffic Prediction
on BJTaxi
Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback
A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence.
Online Knowledge Distillation with Diverse Peers
The second-level distillation is performed to transfer the knowledge in the ensemble of auxiliary peers further to the group leader, i. e., the model used for inference.
Hierarchical Graph Pooling with Structure Learning
HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs.
Ranked #1 on
Graph Classification
on PROTEINS
An Interactive Insight Identification and Annotation Framework for Power Grid Pixel Maps using DenseU-Hierarchical VAE
Insights in power grid pixel maps (PGPMs) refer to important facility operating states and unexpected changes in the power grid.
HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding
Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space.
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
In this paper, we propose a novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms.
Ranked #6 on
Multi-Person Pose Estimation
on OCHuman
Multi-modality Sensor Data Classification with Selective Attention
Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment.
Deep Style Match for Complementary Recommendation
In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper.
Sales Forecast in E-commerce using Convolutional Neural Network
To overcome the limitations of existing methods, we propose a novel approach in this paper to learn effective features automatically from the structured data using the Convolutional Neural Network (CNN).
Navigation Objects Extraction for Better Content Structure Understanding
Dynamic and personalized elements such as top stories, recommended list in a webpage are vital to the understanding of the dynamic nature of web 2. 0 sites.
Orientation Driven Bag of Appearances for Person Re-identification
Experimental results on three public datasets and two proposed datasets demonstrate the superiority of the proposed approach, indicating the effectiveness of body structure and orientation information for improving re-identification performance.
Binary Tree based Chinese Word Segmentation
A binary tree based framework is also designed to overcome the granularity mismatch problem.
