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Found 16 papers, 2 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.
ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples, especially discovering challenging outliers from auxiliary outliers dataset to improve OOD detection.
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.
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.
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.
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
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.
Multi-scale Feature Aggregation for Crowd Counting
The SkipAgg module directly propagates features with small receptive fields to features with much larger receptive fields.
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.
Probability Trajectory: One New Movement Description for Trajectory Prediction
Based on this unique description, we develop one novel trajectory prediction method, called social probability.
Attention Scaling for Crowd Counting
DANet provides ASNet with attention masks related to regions of different density levels.
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.
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.
Convolution in Convolution for Network in Network
In this paper, we propose to replace dense shallow MLP with sparse shallow MLP.
Cascaded Subpatch Networks for Effective CNNs
The first one is a linear filter of spatial size $ h\times w $ and is aimed at extracting features from spatial domain.
