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Found 64 papers, 16 papers with code
Few-Shot Object Detection with Sparse Context Transformers
Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data.
Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects
To address these issues, we propose a joint attention-guided feature fusion network (JAFFNet) for saliency detection of surface defects based on the encoder-decoder network.
Decision Fusion Network with Perception Fine-tuning for Defect Classification
In addition, we propose a perception fine-tuning module (PFM) that fine-tunes the foreground and background during the segmentation stage.
CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation
To address these issues, we propose a transformer network with multi-stage CNN (Convolutional Neural Network) feature injection for surface defect segmentation, which is a UNet-like structure named CINFormer.
Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects
To this end, we develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure.
An Emergency Disposal Decision-making Method with Human--Machine Collaboration
The GACRNN model is used to predict the state of the job nodes, locate the key nodes, and generate a machine-predicted resolution task list.
Context-Aware Block Net for Small Object Detection
State-of-the-art object detectors usually progressively downsample the input image until it is represented by small feature maps, which loses the spatial information and compromises the representation of small objects.
Ranked #1 on
Traffic Sign Detection
on TT100K
Spectral Aware Softmax for Visible-Infrared Person Re-Identification
Based on the observation and analysis of SA-Softmax, we modify the SA-Softmax with the Feature Mask and Absolute-Similarity Term to alleviate the ambiguous optimization during model training.
Long-Range Zero-Shot Generative Deep Network Quantization
We find it is because: 1) a normal generator is hard to obtain high diversity of synthetic data, since it lacks long-range information to allocate attention to global features; 2) the synthetic images aim to simulate the statistics of real data, which leads to weak intra-class heterogeneity and limited feature richness.
NoiSER: Noise is All You Need for Low-Light Image Enhancement
In this paper, we present an embarrassingly simple yet effective solution to a seemingly impossible mission, low-light image enhancement (LLIE) without access to any task-related data.
OSIC: A New One-Stage Image Captioner Coined
To obtain rich features, we use the Swin Transformer to calculate multi-level features, and then feed them into a novel dynamic multi-sight embedding module to exploit both global structure and local texture of input images.
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition
Besides, these methods directly calculate the pair-wise global self-attention equally for all the joints in both the spatial and temporal dimensions, undervaluing the effect of discriminative local joints and the short-range temporal dynamics.
FT-HID: A Large Scale RGB-D Dataset for First and Third Person Human Interaction Analysis
It has been studied either using first person vision (FPV) or third person vision (TPV).
D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights
We present a trajectory prediction approach with respect to traffic lights, D2-TPred, which uses a spatial dynamic interaction graph (SDG) and a behavior dependency graph (BDG) to handle the problem of discontinuous dependency in the spatial-temporal space.
FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring
Blind image deblurring (BID) remains a challenging and significant task.
Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering
Recent advances in Transformer architectures [1] have brought remarkable improvements to visual question answering (VQA).
Decision-making of Emergent Incident based on P-MADDPG
In this paper, we propose a P-MADDPG algorithm to solve the emergency decision-making problem of emergent incidents, which predicts the nodes where an incident may occur in the next time by GRU model and makes decisions before the incident occurs, thus solving the problem that the decision speed cannot keep up with the spreading speed.
Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control
Existing approaches of MAS are largely based on Multi-Agent Deep Reinforcement Learning (MADRL).
Deep Color Consistent Network for Low-Light Image Enhancement
Current low-light image enhancement methods can well improve the illumination.
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition
Graph convolutional networks (GCNs) based methods have achieved advanced performance on skeleton-based action recognition task.
SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction
In detail, when modeling social interaction, we propose a new \emph{social soft attention function}, which fully considers various interaction factors among pedestrians.
A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition
This paper proposes a new graph convolutional operator called central difference graph convolution (CDGC) for skeleton based action recognition.
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes
Albeit simple, the pre-trained encoder can capture the key points of an unseen point cloud and surpasses the encoder trained from scratch on downstream tasks.
Ranked #43 on
3D Part Segmentation
on ShapeNet-Part
ArtCoder: An End-to-End Method for Generating Scanning-Robust Stylized QR Codes
Quick Response (QR) code is one of the most worldwide used two-dimensional codes.
Zero-sample surface defect detection and classification based on semantic feedback neural network
Defect detection and classification technology has changed from traditional artificial visual inspection to current intelligent automated inspection, but most of the current defect detection methods are training related detection models based on a data-driven approach, taking into account the difficulty of collecting some sample data in the industrial field.
User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning
This framework leverages user interactions to retouch and rank images for aesthetic assessment based on deep reinforcement learning (DRL), and generates personalized aesthetic distribution that is more in line with the aesthetic preferences of different users.
Super-Resolution Image Reconstruction Based on Self-Calibrated Convolutional GAN
The generator consists of feature extraction and image reconstruction.
A self-adapting super-resolution structures framework for automatic design of GAN
In this paper, we introduce a new super-resolution image reconstruction generative adversarial network framework, and a Bayesian optimization method used to optimizing the hyperparameters of the generator and discriminator.
Emotional Contagion-Aware Deep Reinforcement Learning for Antagonistic Crowd Simulation
In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED).
Revisiting Local Descriptor for Improved Few-Shot Classification
Few-shot classification studies the problem of quickly adapting a deep learner to understanding novel classes based on few support images.
Multi-Agent Path Planning based on MPC and DDPG
The problem of mixed static and dynamic obstacle avoidance is essential for path planning in highly dynamic environment.
Agent-Based Campus Novel Coronavirus Infection and Control Simulation
Corona Virus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world and bringing huge influence to socioeconomic development as well as people's daily life.
Social and Information Networks Physics and Society Populations and Evolution
Probability Trajectory: One New Movement Description for Trajectory Prediction
Based on this unique description, we develop one novel trajectory prediction method, called social probability.
Trear: Transformer-based RGB-D Egocentric Action Recognition
In this paper, we propose a \textbf{Tr}ansformer-based RGB-D \textbf{e}gocentric \textbf{a}ction \textbf{r}ecognition framework, called Trear.
EC-DARTS: Inducing Equalized and Consistent Optimization Into DARTS
EC-DARTS decouples different operations based on their categories to optimize the operation weights so that the operation gap between them is shrinked.
Occlude Them All: Occlusion-Aware Attention Network for Occluded Person Re-ID
To avoid such problematic models in occluded person ReID, we propose the Occlusion-Aware Mask Network (OAMN).
TRAR: Routing the Attention Spans in Transformer for Visual Question Answering
Due to the superior ability of global dependency modeling, Transformer and its variants have become the primary choice of many vision-and-language tasks.
Transformer Guided Geometry Model for Flow-Based Unsupervised Visual Odometry
In this paper, we propose a method consisting of two camera pose estimators that deal with the information from pairwise images and a short sequence of images respectively.
An End-to-end Method for Producing Scanning-robust Stylized QR Codes
Quick Response (QR) code is one of the most worldwide used two-dimensional codes.~Traditional QR codes appear as random collections of black-and-white modules that lack visual semantics and aesthetic elements, which inspires the recent works to beautify the appearances of QR codes.
A Survey on Concept Factorization: From Shallow to Deep Representation Learning
In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing the current methods.
Memory-Augmented Relation Network for Few-Shot Learning
Metric-based few-shot learning methods concentrate on learning transferable feature embedding that generalizes well from seen categories to unseen categories under the supervision of limited number of labelled instances.
BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation
Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously.
Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network
Although supervised deep deraining networks have obtained impressive results on synthetic datasets, they still cannot obtain satisfactory results on real images due to weak generalization of rain removal capacity, i. e., the pre-trained models usually cannot handle new shapes and directions that may lead to over-derained/under-derained results.
DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking
However, in practice it is rather common to have no un-paired images in real deraining task, in such cases how to remove the rain streaks in an unsupervised way will be a very challenging task due to lack of constraints between images and hence suffering from low-quality recovery results.
Reinforcement Learning-based Visual Navigation with Information-Theoretic Regularization
The regularization maximizes the mutual information between navigation actions and visual observation transforms of an agent, thus promoting more informed navigation decisions.
Robotics
Kernelized Multiview Subspace Analysis by Self-weighted Learning
With the popularity of multimedia technology, information is always represented or transmitted from multiple views.
Personality-Aware Probabilistic Map for Trajectory Prediction of Pedestrians
We update this map dynamically based on the agents in the environment and prior trajectory of a pedestrian.
Multi-scale discriminative Region Discovery for Weakly-Supervised Object Localization
On ILSVRC 2016, the proposed method yields the Top-1 localization error of 48. 65\%, which outperforms previous results by 2. 75\%.
Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity
Clustering is an effective technique in data mining to group a set of objects in terms of some attributes.
A Natural-language-based Visual Query Approach of Uncertain Human Trajectories
Visual querying is essential for interactively exploring massive trajectory data.
Adaptive Exploration for Unsupervised Person Re-Identification
However, a problem of the adaptive selection is that, when an image has too many neighborhoods, it is more likely to attract other images as its neighborhoods.
Predicting the Results of LTL Model Checking using Multiple Machine Learning Algorithms
In this paper, we study how to predict the results of LTL model checking using some machine learning algorithms.
Abnormal Event Detection and Location for Dense Crowds using Repulsive Forces and Sparse Reconstruction
This paper proposes a method based on repulsive forces and sparse reconstruction for the detection and location of abnormal events in crowded scenes.
MDSSD: Multi-scale Deconvolutional Single Shot Detector for Small Objects
The performance of small object detection, however, is still less than satisfactory because of the deficiency of semantic information on shallow feature maps.
USAR: an Interactive User-specific Aesthetic Ranking Framework for Images
When assessing whether an image is of high or low quality, it is indispensable to take personal preference into account.
Bi-directional Graph Structure Information Model for Multi-Person Pose Estimation
The first branch predicts the confidence maps of joints and uses a geometrical transform kernel to propagate information between neighboring joints at the confidence level.
Depth Information Guided Crowd Counting for Complex Crowd Scenes
DigCrowd first uses the depth information of an image to segment the scene into a far-view region and a near-view region.
Dual-Path Convolutional Image-Text Embeddings with Instance Loss
In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space.
Ranked #1 on
Cross-Modal Retrieval
on CUHK-PEDES
Learning Correspondence Structures for Person Re-identification
We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair.
Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization
We propose a new model based on the deconvolutional networks and SAX discretization to learn the representation for multivariate time series.
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories
Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion pattern in a scene.
Tree-based Visualization and Optimization for Image Collection
The visualization of an image collection is the process of displaying a collection of images on a screen under some specific layout requirements.
Person Re-identification with Correspondence Structure Learning
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification.
