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Found 21 papers, 3 papers with code
SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation
The Vision Transformer (ViT) has achieved notable success in computer vision, with its variants extensively validated across various downstream tasks, including semantic segmentation.
ReviveDiff: A Universal Diffusion Model for Restoring Images in Adverse Weather Conditions
Images captured in challenging environments--such as nighttime, foggy, rainy weather, and underwater--often suffer from significant degradation, resulting in a substantial loss of visual quality.
HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation
In this paper, we introduce HAFormer, a model that combines the hierarchical features extraction ability of CNNs with the global dependency modeling capability of Transformers to tackle lightweight semantic segmentation challenges.
Millimeter Wave Radar-based Human Activity Recognition for Healthcare Monitoring Robot
Finally, we deploy our system on a movable robot-mounted edge computing platform, achieving flexible healthcare monitoring in real-world scenarios.
MorphText: Deep Morphology Regularized Arbitrary-shape Scene Text Detection
Bottom-up text detection methods play an important role in arbitrary-shape scene text detection but there are two restrictions preventing them from achieving their great potential, i. e., 1) the accumulation of false text segment detections, which affects subsequent processing, and 2) the difficulty of building reliable connections between text segments.
Seeing Text in the Dark: Algorithm and Benchmark
Localizing text in low-light environments is challenging due to visual degradations.
MFPNet: Multi-scale Feature Propagation Network For Lightweight Semantic Segmentation
In contrast to the abundant research focusing on large-scale models, the progress in lightweight semantic segmentation appears to be advancing at a comparatively slower pace.
Point Clouds Are Specialized Images: A Knowledge Transfer Approach for 3D Understanding
Self-supervised representation learning (SSRL) has gained increasing attention in point cloud understanding, in addressing the challenges posed by 3D data scarcity and high annotation costs.
CARD: Semantic Segmentation with Efficient Class-Aware Regularized Decoder
Extensive experiments and ablation studies conducted on multiple benchmark datasets demonstrate that the proposed CAR can boost the accuracy of all baseline models by up to 2. 23% mIOU with superior generalization ability.
Leveraging Systematic Knowledge of 2D Transformations
The existing deep learning models suffer from out-of-distribution (o. o. d.)
CAR: Class-aware Regularizations for Semantic Segmentation
Recent segmentation methods, such as OCR and CPNet, utilizing "class level" information in addition to pixel features, have achieved notable success for boosting the accuracy of existing network modules.
Ranked #8 on
Semantic Segmentation
on PASCAL Context
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation
Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation.
Ranked #6 on
Semantic Segmentation
on COCO-Stuff test
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting
For this purpose, a classifier evaluates the density level of the input features and then passes them to the corresponding high and low crowded DAD modules.
See More Than Once -- Kernel-Sharing Atrous Convolution for Semantic Segmentation
Experiments conducted on the benchmark PASCAL VOC 2012 dataset show that the proposed sharing strategy can not only boost a network s generalization and representation abilities but also reduce the model complexity significantly.
FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognition
Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM (FC-LSTM) has played a critical role.
DENet: A Universal Network for Counting Crowd with Varying Densities and Scales
Counting people or objects with significantly varying scales and densities has attracted much interest from the research community and yet it remains an open problem.
A-CCNN: adaptive ccnn for density estimation and crowd counting
Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community.
Beyond Context: Exploring Semantic Similarity for Tiny Face Detection
Tiny face detection aims to find faces with high degrees of variability in scale, resolution and occlusion in cluttered scenes.
Characterness: An Indicator of Text in the Wild
In order to measure the characterness we develop three novel cues that are tailored for character detection, and a Bayesian method for their integration.
