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Found 45 papers, 16 papers with code
Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection
Further, we propose a learnable text prompt mechanism with the assist of a normality visual prompt to further improve the matching accuracy of video event description text and video frames.
The Double-Edged Sword of Input Perturbations to Robust Accurate Fairness
Deep neural networks (DNNs) are known to be sensitive to adversarial input perturbations, leading to a reduction in either prediction accuracy or individual fairness.
You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement
Further, we design a novel Color and Intensity Decoupling Network (CIDNet) with two branches dedicated to processing the decoupled image brightness and color in the HVI space.
Ranked #1 on
Low-Light Image Enhancement
on VV
Low-light Image Deblurring and Enhancement
Low-Light Image Enhancement
MixMobileNet: A Mixed Mobile Network for Edge Vision Applications
For global, we propose the global-feature aggregation encoder (GFAE), which employs a pooling strategy and computes the covariance matrix between channels instead of the spatial dimensions, changing the computational complexity from quadratic to linear, and this accelerates the inference of the model.
Open-Vocabulary Video Anomaly Detection
Particularly, we devise a semantic knowledge injection module to introduce semantic knowledge from large language models for the detection task, and design a novel anomaly synthesis module to generate pseudo unseen anomaly videos with the help of large vision generation models for the classification task.
A Survey of Methods for Handling Disk Data Imbalance
Class imbalance exists in many classification problems, and since the data is designed for accuracy, imbalance in data classes can lead to classification challenges with a few classes having higher misclassification costs.
An improved CTGAN for data processing method of imbalanced disk failure
To address the problem of insufficient failure data generated by disks and the imbalance between the number of normal and failure data.
Disk failure prediction based on multi-layer domain adaptive learning
Large scale data storage is susceptible to failure.
Data-level hybrid strategy selection for disk fault prediction model based on multivariate GAN
Data class imbalance is a common problem in classification problems, where minority class samples are often more important and more costly to misclassify in a classification task.
Human-centric Behavior Description in Videos: New Benchmark and Model
To address this challenge, we construct a human-centric video surveillance captioning dataset, which provides detailed descriptions of the dynamic behaviors of 7, 820 individuals.
VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection
With the benefit of dual branch, VadCLIP achieves both coarse-grained and fine-grained video anomaly detection by transferring pre-trained knowledge from CLIP to WSVAD task.
Robust Interference Mitigation techniques for Direct Position Estimation
Global Navigation Satellite System (GNSS) is pervasive in navigation and positioning applications, where precise position and time referencing estimations are required.
Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model
In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e. g., language descriptions and synchronous audios.
Jammer classification with Federated Learning
Jamming signals can jeopardize the operation of GNSS receivers until denying its operation.
RobustFair: Adversarial Evaluation through Fairness Confusion Directed Gradient Search
This matrix categorizes predictions as true fair, true biased, false fair, and false biased, and the perturbations guided by it can produce a dual impact on instances and their similar counterparts to either undermine prediction accuracy (robustness) or cause biased predictions (individual fairness).
Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations
Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance.
Video Event Restoration Based on Keyframes for Video Anomaly Detection
Video anomaly detection (VAD) is a significant computer vision problem.
Bayesian data fusion with shared priors
When data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest, data fusion becomes essential.
ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection
We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sensor discrepancies by progressively fusing the multi-scale features of camera Images and LiDAR point clouds.
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction
However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice.
Monolith: Real Time Recommendation System With Collisionless Embedding Table
In this paper, we present Monolith, a system tailored for online training.
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection
Such a module will guide our PV-RCNN++ to integrate more object-related point-wise and voxel-wise features in the pivotal areas.
Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection
Existing methods for anomaly detection based on memory-augmented autoencoder (AE) have the following drawbacks: (1) Establishing a memory bank requires additional memory space.
Cycle Self-Training for Semi-Supervised Object Detection with Distribution Consistency Reweighting
To address the coupling problem, we propose a Cycle Self-Training (CST) framework for SSOD, which consists of two teachers T1 and T2, two students S1 and S2.
Multiple Robust Learning for Recommendation
Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate.
Unsupervised High-Resolution Portrait Gaze Correction and Animation
Solving this problem using an unsupervised method remains an open problem, especially for high-resolution face images in the wild, which are not easy to annotate with gaze and head pose labels.
Accurate Fairness: Improving Individual Fairness without Trading Accuracy
We propose in this paper a new fairness criterion, accurate fairness, to align individual fairness with accuracy.
StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
However, in this paper, we show that DR methods are unstable and have unbounded bias, variance, and generalization bounds to extremely small propensities.
MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities
By comparing the similarities and differences between input samples and memory samples in the memory pool to give effective guesses about abnormal regions; In the inference phase, MemSeg directly determines the abnormal regions of the input image in an end-to-end manner.
Ranked #14 on
Anomaly Detection
on MVTec AD
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU
Our experiments show that previous scaling rules fail in the training of CTR prediction neural networks.
TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning.
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.
From Rough to Multifractal volatility: the log S-fBM model
We introduce a family of random measures $M_{H, T} (d t)$, namely log S-fBM, such that, for $H>0$, $M_{H, T}(d t) = e^{\omega_{H, T}(t)} d t$ where $\omega_{H, T}(t)$ is a Gaussian process that can be considered as a stationary version of an $H$-fractional Brownian motion.
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges
Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks.
An Intelligent Energy Management Framework for Hybrid-Electric Propulsion Systems Using Deep Reinforcement Learning
In the proposed framework, a Twin-Delayed Deep Deterministic Policy Gradient agent is trained using an extensive volume of historical load profiles to generate a generic energy management strategy.
HANet: Hierarchical Alignment Networks for Video-Text Retrieval
Based on these, we naturally construct hierarchical representations in the individual-local-global manner, where the individual level focuses on the alignment between frame and word, local level focuses on the alignment between video clip and textual context, and global level focuses on the alignment between the whole video and text.
Personalized Federated Learning over non-IID Data for Indoor Localization
Localization and tracking of objects using data-driven methods is a popular topic due to the complexity in characterizing the physics of wireless channel propagation models.
Visual Odometry with an Event Camera Using Continuous Ray Warping and Volumetric Contrast Maximization
We present a new solution to tracking and mapping with an event camera.
GCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking
As an essential component of task-oriented dialogue systems, Dialogue State Tracking (DST) takes charge of estimating user intentions and requests in dialogue contexts and extracting substantial goals (states) from user utterances to help the downstream modules to determine the next actions of dialogue systems.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision
Violence detection has been studied in computer vision for years.
Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering
However, one critical problem is that current approaches only get high accuracy for questions whose relations have been seen in the training data.
Time Difference of Arrival (TDoA) Localization Combining Weighted Least Squares and Firefly Algorithm
This paper proposes a hybrid firefly algorithm (hybrid‐FA) method, combining the weighted least squares (WLS) algorithm and FA, which can reduce computation as well as achieve high accuracy.
Multimodal Deep Network Embedding with Integrated Structure and Attribute Information
Network embedding is the process of learning low-dimensional representations for nodes in a network, while preserving node features.
Dense Object Reconstruction from RGBD Images with Embedded Deep Shape Representations
Most problems involving simultaneous localization and mapping can nowadays be solved using one of two fundamentally different approaches.
Localization length exponent in two models of quantum Hall plateau transitions
Motivated by the recent numerical studies on the Chalker-Coddington network model that found a larger-than-expected critical exponent of the localization length characterizing the integer quantum Hall plateau transitions, we revisited the exponent calculation in the continuum model and in the lattice model, both projected to the lowest Landau level or subband.
Disordered Systems and Neural Networks
