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Found 107 papers, 60 papers with code
Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting
Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges.
CLIP-guided Prototype Modulating for Few-shot Action Recognition
Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explored on the challenging few-shot action recognition (FSAR) task.
Invariant Collaborative Filtering to Popularity Distribution Shift
Collaborative Filtering (CF) models, despite their great success, suffer from severe performance drops due to popularity distribution shifts, where these changes are ubiquitous and inevitable in real-world scenarios.
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition
To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric.
Causal Inference for Knowledge Graph based Recommendation
Knowledge Graph (KG), as a side-information, tends to be utilized to supplement the collaborative filtering (CF) based recommendation model.
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection
However, existing datasets for unsupervised anomaly detection are biased towards manufacturing inspection, not considering maintenance inspection which is usually conducted under outdoor uncontrolled environment such as varying camera viewpoints, messy background and degradation of object surface after long-term working.
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network
First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain.
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network
In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e. g., 4x and 8x).
Adversarial Causal Augmentation for Graph Covariate Shift
Existing strategies of graph invariant learning and data augmentation suffer from limited environments or unstable causal features, which greatly limits their generalization ability on covariate shift.
Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning
The former is to reduce the memory cost by preserving only one condensed frame instead of the whole video, while the latter aims to compensate the lost spatio-temporal details in the Frame Condensing stage.
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup
Mixup is a data augmentation technique that relies on training using random convex combinations of data points and their labels.
Slippage-robust Gaze Tracking for Near-eye Display
In recent years, head-mounted near-eye display devices have become the key hardware foundation for virtual reality and augmented reality.
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences.
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example
Globally we observe that the training dynamics for our example has an interesting bifurcating behavior, which was also observed in the training of neural nets.
Vision-Based Defect Classification and Weight Estimation of Rice Kernels
To compensate for the imbalance of different kernel numbers and classify kernels with multiple flaws accurately, we propose a multi-stage workflow which is able to locate the kernels in the captured image and classify their properties.
Plateau in Monotonic Linear Interpolation -- A "Biased" View of Loss Landscape for Deep Networks
Monotonic linear interpolation (MLI) - on the line connecting a random initialization with the minimizer it converges to, the loss and accuracy are monotonic - is a phenomenon that is commonly observed in the training of neural networks.
Equivariant and Invariant Grounding for Video Question Answering
Specifically, the equivariant grounding encourages the answering to be sensitive to the semantic changes in the causal scene and question; in contrast, the invariant grounding enforces the answering to be insensitive to the changes in the environment scene.
MAR: Masked Autoencoders for Efficient Action Recognition
Inspired by this, we propose propose Masked Action Recognition (MAR), which reduces the redundant computation by discarding a proportion of patches and operating only on a part of the videos.
Ranked #7 on
Action Recognition
on Something-Something V2
MLGOPerf: An ML Guided Inliner to Optimize Performance
This paper presents MLGOPerf; the first end-to-end framework capable of optimizing performance using LLVM's ML-Inliner.
Context-aware Proposal Network for Temporal Action Detection
This technical report presents our first place winning solution for temporal action detection task in CVPR-2022 AcitivityNet Challenge.
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Specifically, without supervision signals, RGCL uses a rationale generator to reveal salient features about graph instance-discrimination as the rationale, and then creates rationale-aware views for contrastive learning.
Invariant Grounding for Video Question Answering
At its core is understanding the alignments between visual scenes in video and linguistic semantics in question to yield the answer.
CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation
Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively.
Differentiable Invariant Causal Discovery
This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions.
Benchmarking Unsupervised Anomaly Detection and Localization
From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation.
Copy Motion From One to Another: Fake Motion Video Generation
3) To enhance texture details, we encode facial features with geometric guidance and employ local GANs to refine the face, feet, and hands.
A Novel Speech-Driven Lip-Sync Model with CNN and LSTM
Generating synchronized and natural lip movement with speech is one of the most important tasks in creating realistic virtual characters.
Hybrid Relation Guided Set Matching for Few-shot Action Recognition
To overcome the two limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric.
Cross Pairwise Ranking for Unbiased Item Recommendation
In this work, we develop a new learning paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased recommendation without knowing the exposure mechanism.
Reinforced Causal Explainer for Graph Neural Networks
Such reward accounts for the dependency of the newly-added edge and the previously-added edges, thus reflecting whether they collaborate together and form a coalition to pursue better explanations.
Rumor Detection with Self-supervised Learning on Texts and Social Graph
At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth.
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency
In this work, we aim to learn representations by leveraging more abundant information in untrimmed videos.
Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation
State-of-the-art methods strive to incorporate additional visual evidences from neighboring frames (supporting frames) to facilitate the pose estimation of the current frame (key frame).
Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective
The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points.
Discovering Invariant Rationales for Graph Neural Networks
Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction.
Deconfounding to Explanation Evaluation in Graph Neural Networks
In this work, we propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.
Training Free Graph Neural Networks for Graph Matching
We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution without training (training-free).
On the Effectiveness of Sampled Softmax Loss for Item Recommendation
Nonetheless, limited studies use sampled softmax loss as the learning objective to train the recommender.
Causal Attention for Interpretable and Generalizable Graph Classification
To endow the classifier with better interpretation and generalization, we propose the Causal Attention Learning (CAL) strategy, which discovers the causal patterns and mitigates the confounding effect of shortcuts.
Towards Multi-Grained Explainability for Graph Neural Networks
A performant paradigm towards multi-grained explainability is until-now lacking and thus a focus of our work.
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
However, current methods can not effectively map image features to a tractable base distribution and ignore the relationship between local and global features which are important to identify anomalies.
Ranked #6 on
Anomaly Detection
on MVTec AD
(using extra training data)
Unsupervised Anomaly Detection
Weakly Supervised Defect Detection
Automated Pulmonary Embolism Detection from CTPA Images Using an End-to-End Convolutional Neural Network
We have evaluated our approach using the 20 CTPA test dataset from the PE challenge, achieving a sensitivity of 78. 9%, 80. 7% and 80. 7% at 2 false positives per volume at 0mm, 2mm and 5mm localization error, which is superior to the state-of-the-art methods.
Towards Understanding the Data Dependency of Mixup-style Training
Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tasks when compared to standard training.
Towards Demystifying Representation Learning with Non-contrastive Self-supervision
Non-contrastive methods of self-supervised learning (such as BYOL and SimSiam) learn representations by minimizing the distance between two views of the same image.
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization
To facilitate the learning with only normal images, we propose a new pretext task called non-contrastive learning for the fine alignment stage.
Ranked #27 on
Anomaly Detection
on MVTec AD
(using extra training data)
Inductive Lottery Ticket Learning for Graph Neural Networks
In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity.
Time-aware Path Reasoning on Knowledge Graph for Recommendation
In this work, we propose a novel Time-aware Path reasoning for Recommendation (TPRec for short) method, which leverages the potential of temporal information to offer better recommendation with plausible explanations.
Exploring Lottery Ticket Hypothesis in Media Recommender Systems
We get inspirations from the recently proposed lottery ticket hypothesis (LTH), which argues that the dense and over-parameterized model contains a much smaller and sparser sub-model that can reach comparable performance to the full model.
Visual Boundary Knowledge Translation for Foreground Segmentation
To this end, we propose a Translation Segmentation Network (Trans-Net), which comprises a segmentation network and two boundary discriminators.
Contrastive Learning for Cold-Start Recommendation
It aims to maximize the mutual dependencies between item content and collaborative signals.
Exploring Stronger Feature for Temporal Action Localization
Temporal action localization aims to localize starting and ending time with action category.
OadTR: Online Action Detection with Transformers
Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure.
Ranked #4 on
Online Action Detection
on TVSeries
Proposal Relation Network for Temporal Action Detection
We calculate the detection results by assigning the proposals with corresponding classification results.
Ranked #1 on
Temporal Action Localization
on ActivityNet-1.3
(using extra training data)
Weakly-Supervised Temporal Action Localization Through Local-Global Background Modeling
Then our proposed Local-Global Background Modeling Network (LGBM-Net) is trained to localize instances by using only video-level labels based on Multi-Instance Learning (MIL).
Weakly-supervised Learning
Weakly-supervised Temporal Action Localization
+1
Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion
In this paper, we explore combining deep learning with expert patterns in an explainable fashion.
Relation Modeling in Spatio-Temporal Action Localization
This paper presents our solution to the AVA-Kinetics Crossover Challenge of ActivityNet workshop at CVPR 2021.
Ranked #2 on
Spatio-Temporal Action Localization
on AVA-Kinetics
(using extra training data)
A Stronger Baseline for Ego-Centric Action Detection
This technical report analyzes an egocentric video action detection method we used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop.
Understanding Deflation Process in Over-parametrized Tensor Decomposition
In this paper we study the training dynamics for gradient flow on over-parametrized tensor decomposition problems.
Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition
In this paper, we present empirical results for training a stronger video vision transformer on the EPIC-KITCHENS-100 Action Recognition dataset.
Deconfounded Recommendation for Alleviating Bias Amplification
In this work, we scrutinize the cause-effect factors for bias amplification, identifying the main reason lies in the confounder effect of imbalanced item distribution on user representation and prediction score.
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance
Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs).
Self-Supervised Learning for Semi-Supervised Temporal Action Proposal
In this paper, we focus on applying the power of self-supervised methods to improve semi-supervised action proposal generation.
Ranked #2 on
Semi-Supervised Action Detection
on THUMOS' 14
Self-Supervised Learning
Semi-Supervised Action Detection
+1
Temporal Context Aggregation Network for Temporal Action Proposal Refinement
In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement.
Ranked #3 on
Temporal Action Localization
on ActivityNet-1.3
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks
Real-world data usually have high dimensionality and it is important to mitigate the curse of dimensionality.
Learning Intents behind Interactions with Knowledge Graph for Recommendation
In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN).
Causal Screening to Interpret Graph Neural Networks
In this work, we focus on the causal interpretability in GNNs and propose a method, Causal Screening, from the perspective of cause-effect.
Beyond Lazy Training for Over-parameterized Tensor Decomposition
We show that in a lazy training regime (similar to the NTK regime for neural networks) one needs at least $m = \Omega(d^{l-1})$, while a variant of gradient descent can find an approximate tensor when $m = O^*(r^{2. 5l}\log d)$.
Self-supervised Graph Learning for Recommendation
In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation.
Bias and Debias in Recommender System: A Survey and Future Directions
This motivates us to provide a systematic survey of existing work on RS biases.
Multi-Level Temporal Pyramid Network for Action Detection
By this means, the proposed MLTPN can learn rich and discriminative features for different action instances with different durations.
Disentangled Graph Collaborative Filtering
Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations.
Interactive Path Reasoning on Graph for Conversational Recommendation
Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user preference.
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
Solving this problem using a learning-to-learn approach -- using meta-gradient descent on a meta-objective based on the trajectory that the optimizer generates -- was recently shown to be effective.
CBR-Net: Cascade Boundary Refinement Network for Action Detection: Submission to ActivityNet Challenge 2020 (Task 1)
In this report, we present our solution for the task of temporal action localization (detection) (task 1) in ActivityNet Challenge 2020.
Temporal Fusion Network for Temporal Action Localization:Submission to ActivityNet Challenge 2020 (Task E)
This technical report analyzes a temporal action localization method we used in the HACS competition which is hosted in Activitynet Challenge 2020. The goal of our task is to locate the start time and end time of the action in the untrimmed video, and predict action category. Firstly, we utilize the video-level feature information to train multiple video-level action classification models.
Hierarchical Fashion Graph Network for Personalized Outfit Recommendation
Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities. Distinct from other scenarios (e. g., social networking or content sharing) which recommend a single item (e. g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items. Hence, performing high-quality personalized outfit recommendation should satisfy two requirements -- 1) the nice compatibility of fashion items and 2) the consistence with user preference.
Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement
The core of VP model is to decompose the light source into light intensity and light spatial distribution to describe the perception process of HVS, offering refinement estimation of illumination and reflectance.
XTDrone: A Customizable Multi-Rotor UAVs Simulation Platform
A customizable multi-rotor UAVs simulation platform based on ROS, Gazebo and PX4 is presented.
Robotics
Reinforced Negative Sampling over Knowledge Graph for Recommendation
Properly handling missing data is a fundamental challenge in recommendation.
Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning
In this paper, we propose an iterative algorithm to learn such pairwise relations, which consists of two branches, a unary segmentation network which learns the label probabilities for each pixel, and a pairwise affinity network which learns affinity matrix and refines the probability map generated from the unary network.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Bilinear Graph Neural Network with Neighbor Interactions
We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.
Ranked #5 on
Recommendation Systems
on Yelp2018
Graph Convolution Machine for Context-aware Recommender System
The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph.
Multiple Sample Clustering
The clustering algorithms that view each object data as a single sample drawn from a certain distribution, Gaussian distribution, for example, has been a hot topic for decades.
Quantum-enhanced least-square support vector machine: simplified quantum algorithm and sparse solutions
We also incorporate kernel methods into the above quantum algorithms, which uses both exponential growth Hilbert space of qubits and infinite dimensionality of continuous variable for quantum feature maps.
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
Mode connectivity is a surprising phenomenon in the loss landscape of deep nets.
Neural Graph Collaborative Filtering
Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.
Ranked #6 on
Link Prediction
on MovieLens 25M
KGAT: Knowledge Graph Attention Network for Recommendation
To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.
Ranked #2 on
Link Prediction
on Yelp
Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization
Variance reduction techniques like SVRG provide simple and fast algorithms for optimizing a convex finite-sum objective.
A Survey on Face Data Augmentation
The quality and size of training set have great impact on the results of deep learning-based face related tasks.
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
In this paper, we jointly learn the model of recommendation and knowledge graph completion.
Ranked #1 on
Knowledge Graph Completion
on DBbook2014
A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation
This paper presents a novel framework for simultaneously implementing localization and segmentation, which are two of the most important vision-based tasks for robotics.
Explainable Reasoning over Knowledge Graphs for Recommendation
Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest.
Deep Item-based Collaborative Filtering for Top-N Recommendation
In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items.
Learning Two-layer Neural Networks with Symmetric Inputs
We give a new algorithm for learning a two-layer neural network under a general class of input distributions.
Temporal Relational Ranking for Stock Prediction
Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.
Outer Product-based Neural Collaborative Filtering
In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering.
Structure-aware Generative Network for 3D-Shape Modeling
The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code.
Graphics
Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features
Then in the top-down step, the refined object regions are used as supervision to train the segmentation network and to predict object masks.
General Classification
Weakly supervised Semantic Segmentation
+1
Item Silk Road: Recommending Items from Information Domains to Social Users
In this work, we address the problem of cross-domain social recommendation, i. e., recommending relevant items of information domains to potential users of social networks.
Ranked #2 on
Recommendation Systems
on Epinions
Uncovering Group Level Insights with Accordant Clustering
Clustering is a widely-used data mining tool, which aims to discover partitions of similar items in data.
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection.
A Multilevel Coordinate Search Algorithm for Well Placement, Control and Joint Optimization
For the joint optimization problem we compare the performance of the simultaneous and sequential procedures and show the utility of the latter.
Improving Object Proposals With Multi-Thresholding Straddling Expansion
Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification.
On Constrained Spectral Clustering and Its Applications
Furthermore, by inheriting the objective function from spectral clustering and encoding the constraints explicitly, much of the existing analysis of unconstrained spectral clustering techniques remains valid for our formulation.
