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Found 70 papers, 15 papers with code
Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms
A/B testing, or controlled experiments, is the gold standard approach to causally compare the performance of algorithms on online platforms.
Learning to Simulate Daily Activities via Modeling Dynamic Human Needs
To enhance the fidelity and utility of the generated activity data, our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model.
Z-SSMNet: A Zonal-aware Self-Supervised Mesh Network for Prostate Cancer Detection and Diagnosis in bpMRI
However, existing state of the art AI algorithms which are based on deep learning technology are often limited to 2D images that fails to capture inter-slice correlations in 3D volumetric images.
Contactless Oxygen Monitoring with Gated Transformer
With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead.
Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects
The mainstream crowd counting methods regress density map and integrate it to obtain counting results.
Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation
In our DA framework, we retain the depth and background information during the domain feature alignment.
DeS3: Attention-driven Self and Soft Shadow Removal using ViT Similarity and Color Convergence
In this paper, we present DeS3, a method that removes hard, soft and self shadows based on the self-tuned ViT feature similarity and color convergence.
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data
However, the object-level visual grounding on RS images is still under-explored.
Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces
Online platforms often face challenges being both fair (i. e., non-discriminatory) and efficient (i. e., maximizing revenue).
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
This method also achieves competitive results for unsupervised video object segmentation tasks with the DAVIS, SegTV2, and FBMS datasets.
MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting
RGB-Thermal (RGB-T) crowd counting is a challenging task, which uses thermal images as complementary information to RGB images to deal with the decreased performance of unimodal RGB-based methods in scenes with low-illumination or similar backgrounds.
Unsupervised Learning for Human Sensing Using Radio Signals
Thus, in this paper, we explore the feasibility of adapting RGB-based unsupervised representation learning to RF signals.
Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teacher
The core reason of intrinsic scale shift being one of the most essential issues in crowd localization is that it is ubiquitous in crowd scenes and makes scale distribution chaotic.
Dual-Stage Approach Toward Hyperspectral Image Super-Resolution
Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution.
Iterative Genetic Improvement: Scaling Stochastic Program Synthesis
In cases where large programs are required for a solution, it is generally believed that {\it stochastic} search has advantages over other classes of search techniques.
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
For unsupervised saliency detection, we improve IoU for 4. 9%, 5. 2%, 12. 9% on ECSSD, DUTS, DUT-OMRON respectively compared to previous state of the art.
Ranked #1 on
Weakly-Supervised Object Localization
on CUB-200-2011
(Top-1 Localization Accuracy metric)
Optimizing LLVM Pass Sequences with Shackleton: A Linear Genetic Programming Framework
In this paper we introduce Shackleton as a generalized framework enabling the application of linear genetic programming -- a technique under the umbrella of evolutionary algorithms -- to a variety of use cases.
Targeted Supervised Contrastive Learning for Long-Tailed Recognition
This forces all classes, including minority classes, to maintain a uniform distribution in the feature space, improves class boundaries, and provides better generalization even in the presence of long-tail data.
Ranked #17 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Adaptive Shrink-Mask for Text Detection
It weakens the coupling of texts to shrink-masks, which improves the robustness of detection results.
ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition
Discriminative local theme-level and object-level representations can be selected with the DLFS module from the spatially-correlated multi-modal RGB-D features.
LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds
The rapid development in visual crowd analysis shows a trend to count people by positioning or even detecting, rather than simply summing a density map.
Investigating and Modeling the Dynamics of Long Ties
Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks.
MESSFN : a Multi-level and Enhanced Spectral-Spatial Fusion Network for Pan-sharpening
A novel Spectral-Spatial (SS) stream is established to hierarchically derive and fuse the multi-level prior spectral and spatial expertise from the MS stream and the PAN stream.
Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification
In addition, due to domain gaps between different datasets, the performance is dramatically decreased when re-ID models pre-trained on label-rich datasets (source domain) are directly applied to other unlabeled datasets (target domain).
MT: Multi-Perspective Feature Learning Network for Scene Text Detection
Text detection, the key technology for understanding scene text, has become an attractive research topic.
Task-Related Self-Supervised Learning for Remote Sensing Image Change Detection
(3) a smooth mechanism is utilized to remove some of pseudo-changes and noise.
Change Detection
Change detection for remote sensing images
+1
Weighted Hierarchical Sparse Representation for Hyperspectral Target Detection
However, background dictionary building issue and the correlation analysis of target and background dictionary issue have not been well studied.
Instance-aware Remote Sensing Image Captioning with Cross-hierarchy Attention
The spatial attention is a straightforward approach to enhance the performance for remote sensing image captioning.
Open Set Domain Recognition via Attention-Based GCN and Semantic Matching Optimization
Open set domain recognition has got the attention in recent years.
Deep feature selection-and-fusion for RGB-D semantic segmentation
Scene depth information can help visual information for more accurate semantic segmentation.
Ranked #4 on
Semantic Segmentation
on SUN-RGBD
Self-supervised spectral matching network for hyperspectral target detection
In order to learn more discriminative features, a pair-based loss is adopted to minimize the distance between target pixels while maximizing the distances between target and background.
BiP-Net: Bidirectional Perspective Strategy based Arbitrary-Shaped Text Detection Network
Specifically, a new text representation strategy is proposed to represent text contours from a top-down perspective, which can fit highly curved text contours effectively.
Bio-Inspired Representation Learning for Visual Attention Prediction
In this paper, a novel VAP method is proposed to generate visual attention map via bio-inspired representation learning.
Learning Blood Oxygen from Respiration Signals
Further, since breathing can be monitored without body contact by analyzing the radio signal in the environment, we show that oxygen too can be monitored without any wearable devices.
Addressing Feature Suppression in Unsupervised Visual Representations
However, contrastive learning is susceptible to feature suppression, i. e., it may discard important information relevant to the task of interest, and learn irrelevant features.
Learning Independent Instance Maps for Crowd Localization
Furthermore, to improve the segmentation quality for different density regions, we present a differentiable Binarization Module (BM) to output structured instance maps.
CM-Net: Concentric Mask based Arbitrary-Shaped Text Detection
Extensive experiments demonstrate the proposed CM is efficient and robust to fit arbitrary-shaped text instances, and also validate the effectiveness of MPF and constraints loss for discriminative text features recognition.
Subgroup-based Rank-1 Lattice Quasi-Monte Carlo
We theoretically prove a lower and an upper bound of the minimum pairwise distance of any non-degenerate rank-1 lattice.
Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests
Our study provides an approach that accounts for both the local structure in a user's social network via motifs as well as the assignment conditions of neighbors.
Social and Information Networks Applications
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
In this paper, we combine both to propose an Unsupervised Semantic Aggregation and Deformable Template Matching (USADTM) framework for SSL, which strives to improve the classification performance with few labeled data and then reduce the cost in data annotating.
In-Home Daily-Life Captioning Using Radio Signals
This paper aims to caption daily life --i. e., to create a textual description of people's activities and interactions with objects in their homes.
Pixel-wise Crowd Understanding via Synthetic Data
To be specific, 1) supervised crowd understanding: pre-train a crowd analysis model on the synthetic data, then fine-tune it using the real data and labels, which makes the model perform better on the real world; 2) crowd understanding via domain adaptation: translate the synthetic data to photo-realistic images, then train the model on translated data and labels.
Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training
In order to remove the non-uniform blur of images captured from dynamic scenes, many deep learning based methods design deep networks for large receptive fields and strong fitting capabilities, or use multi-scale strategy to deblur image on different scales gradually.
Video Frame Interpolation via Residue Refinement
Video frame interpolation achieves temporal super-resolution by generating smooth transitions between frames.
Ranked #12 on
Video Frame Interpolation
on Vimeo90K
Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting
Specifically, for a specific neuron of a source model, NLT exploits few labeled target data to learn domain shift parameters.
Learning Longterm Representations for Person Re-Identification Using Radio Signals
RF signals traverse clothes and reflect off the human body; thus they can be used to extract more persistent human-identifying features like body size and shape.
Focus on Semantic Consistency for Cross-domain Crowd Understanding
According to the semantic consistency, a similar distribution in deep layer's features of the synthetic and real-world crowd area, we first introduce a semantic extractor to effectively distinguish crowd and background in high-level semantic information.
Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance
To reduce the gap, in this paper, we propose a domain-adaptation-style crowd counting method, which can effectively adapt the model from synthetic data to the specific real-world scenes.
Domain-adaptive Crowd Counting via High-quality Image Translation and Density Reconstruction
Recently, crowd counting using supervised learning achieves a remarkable improvement.
Towards a Proactive MWE Terminological Platform for Cross-Lingual Mediation in the Age of Big Data
It has broader economic implication in the Age of Big Data (Tsou et al, 2015) and Trade War, as the workload, if not, the challenges, increasingly cannot be met by currently available front-line translators.
SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting
The latter attempts to extract more discriminative features among different channels, which aids model to pay attention to the head region, the core of crowd scenes.
Gift Contagion in Online Groups: Evidence From WeChat Red Packets
Using data on 36 million online red packet gifts on China's social site WeChat, we leverage a natural experimental design to identify the social contagion of gift giving in online groups.
Efficient Batch Black-box Optimization with Deterministic Regret Bounds
In this work, we investigate black-box optimization from the perspective of frequentist kernel methods.
Marginalized Average Attentional Network for Weakly-Supervised Learning
The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion.
Ranked #7 on
Weakly Supervised Action Localization
on ActivityNet-1.3
(mAP@0.5 metric)
Weakly Supervised Action Localization
Weakly-supervised Learning
+2
VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection
Our contributions are as follows: 1) We propose a multi-resolution feature fusion network architecture which exploits densely connected deconvolution layers with skip connections, and can learn more effective features for the small size object; 2) We frame the traffic sign detection as a spatial sequence classification and regression task, and propose a vertical spatial sequence attention (VSSA) module to gain more context information for better detection performance.
A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling
Our contributions are threefold: (1) A priori s-CNNs model that learns priori location information at superpixel level is proposed to describe various objects discriminatingly; (2) A hierarchical data augmentation method is presented to alleviate dataset bias in the priori s-CNNs training stage, which improves foreground objects labeling significantly; (3) A soft restricted MRF energy function is defined to improve the priori s-CNNs model's labeling performance and reduce the over smoothness at the same time.
Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection
Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving.
Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles
In this paper, we present iDVO (inertia-embedded deep visual odometry), a self-supervised learning based monocular visual odometry (VO) for road vehicles.
Cross-Modal Message Passing for Two-stream Fusion
The classification object ensures that each modal network predicts the true action category while the competing objective encourages each modal network to outperform the other one.
Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction
3) Results of motion orientation and magnitude are adaptively weighted and fused by a Bayesian model, which makes the proposed method more robust and handle more kinds of abnormal events.
Memory-Augmented Temporal Dynamic Learning for Action Recognition
Human actions captured in video sequences contain two crucial factors for action recognition, i. e., visual appearance and motion dynamics.
Early Action Prediction with Generative Adversarial Networks
Action Prediction is aimed to determine what action is occurring in a video as early as possible, which is crucial to many online applications, such as predicting a traffic accident before it happens and detecting malicious actions in the monitoring system.
Forward Vehicle Collision Warning Based on Quick Camera Calibration
Forward Vehicle Collision Warning (FCW) is one of the most important functions for autonomous vehicles.
Tracking as A Whole: Multi-Target Tracking by Modeling Group Behavior with Sequential Detection
In the detection stage, we present a sequential detection model to deal with serious occlusions.
Learning from Synthetic Data for Crowd Counting in the Wild
Secondly, we propose two schemes that exploit the synthetic data to boost the performance of crowd counting in the wild: 1) pretrain a crowd counter on the synthetic data, then finetune it using the real data, which significantly prompts the model's performance on real data; 2) propose a crowd counting method via domain adaptation, which can free humans from heavy data annotations.
Dynamic Hierarchical Empirical Bayes: A Predictive Model Applied to Online Advertising
Predicting keywords performance, such as number of impressions, click-through rate (CTR), conversion rate (CVR), revenue per click (RPC), and cost per click (CPC), is critical for sponsored search in the online advertising industry.
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable.
Robustness Analysis of Pedestrian Detectors for Surveillance
In this study, we conduct the research on the robustness of pedestrian detection algorithms to video quality degradation.
Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
A common issue, however, is that objects of interest that are not involved in human actions are often absent in global action descriptions known as "missing label".
Ranked #3 on
Weakly Supervised Object Detection
on Charades
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously.
