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Found 16 papers, 5 papers with code
CMFN: Cross-Modal Fusion Network for Irregular Scene Text Recognition
However, the guidance of visual cues is ignored in the process of semantic mining, which limits the performance of the algorithm in recognizing irregular scene text.
Text Region Multiple Information Perception Network for Scene Text Detection
Segmentation-based scene text detection algorithms can handle arbitrary shape scene texts and have strong robustness and adaptability, so it has attracted wide attention.
BPDO:Boundary Points Dynamic Optimization for Arbitrary Shape Scene Text Detection
Arbitrary shape scene text detection is of great importance in scene understanding tasks.
ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving
How to model action sequences so that one can further consider the effects of different action parameters in the scenario is the bottleneck of the problem.
Multi-Granularity Alignment Domain Adaptation for Object Detection
Domain adaptive object detection is challenging due to distinctive data distribution between source domain and target domain.
LD-ConGR: A Large RGB-D Video Dataset for Long-Distance Continuous Gesture Recognition
The dataset and experimental results presented in this paper are expected to boost the research of long-distance gesture recognition.
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
Then, we conduct a comprehensive and systematic review to categorize the state-of-the-art adversarial attacks against PE malware detection, as well as corresponding defenses to increase the robustness of Windows PE malware detection.
Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark
Towards real-world prohibited item detection, we collect a large-scale dataset, named as PIDray, which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items.
Spatial Attention Pyramid Network for Unsupervised Domain Adaptation
Specifically, we first build the spatial pyramid representation to capture context information of objects at different scales.
Towards Interpretable and Robust Hand Detection via Pixel-wise Prediction
The lack of interpretability of existing CNN-based hand detection methods makes it difficult to understand the rationale behind their predictions.
Learning Semantic Neural Tree for Human Parsing
In this paper, we design a novel semantic neural tree for human parsing, which uses a tree architecture to encode physiological structure of human body, and designs a coarse to fine process in a cascade manner to generate accurate results.
SiamMan: Siamese Motion-aware Network for Visual Tracking
To reduce the impact of manually designed anchor boxes to adapt to different target motion patterns, we design the localization branch, which aims to coarsely localize the target to help the regression branch to generate accurate results.
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
Specifically, we incorporate convolutional operations along edges of the tree structure, and use the routing functions in each node to determine the root-to-leaf computational paths within the tree.
Ranked #33 on
Fine-Grained Image Classification
on Stanford Cars
Fine-Grained Image Classification
Fine-Grained Visual Categorization
Guided Attention Network for Object Detection and Counting on Drones
Object detection and counting are related but challenging problems, especially for drone based scenes with small objects and cluttered background.
Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently
Existing hand detection methods usually follow the pipeline of multiple stages with high computation cost, i. e., feature extraction, region proposal, bounding box regression, and additional layers for rotated region detection.
Data Priming Network for Automatic Check-Out
In this paper, we propose a new data priming method to solve the domain adaptation problem.
