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INFINIQ's AI Model Achieves Top Performance In Few-Shot Object Detection

INFINIQ's AI Model Achieves Top Performance In Few-Shot Object Detection
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South Korea , South Korean , Jae Jung , Global Digital Innovation Network , Global Centre , Image Language Similarity , Few Shot Object Detection , Image Understanding , Contrastive Language Image Pre Training , Background Negative Re Scale Loss , Computer Vision , Min Jae Jung , Jun Hyung Park ,

INFINIQ's AI Model Achieves Top Performance in Few-Shot Object Detection

/PRNewswire/ INFINIQ, a leading South Korean AI platform services company, announced a breakthrough in few-shot object detection with the publication of. ....

South Korea , South Korean , Jae Jung , Global Digital Innovation Network , Global Centre , Image Language Similarity , Few Shot Object Detection , Image Understanding , Contrastive Language Image Pre Training , Background Negative Re Scale Loss , Computer Vision , Min Jae Jung , Jun Hyung Park , Computer Amp Electronics , Artificial Intelligence , Publishing Amp Information Services , Computer Software , New Products Amp Services , Olls And Research ,

"Time-rEversed DiffusioN tEnsor Transformer: A New TENET of Few-Shot Ob" by Shan Zhang, Naila Murray et al.

In this paper, we tackle the challenging problem of Few-shot Object Detection. Existing FSOD pipelines (i) use average-pooled representations that result in information loss; and/or (ii) discard position information that can help detect object instances. Consequently, such pipelines are sensitive to large intra-class appearance and geometric variations between support and query images. To address these drawbacks, we propose a Time-rEversed diffusioN tEnsor Transformer (TENET), which i) forms high-order tensor representations that capture multi-way feature occurrences that are highly discriminative, and ii) uses a transformer that dynamically extracts correlations between the query image and the entire support set, instead of a single average-pooled support embedding. We also propose a Transformer Relation Head (TRH), equipped with higher-order representations, which encodes correlations between query regions and the entire support set, while being sensitive to the positional variabilit ....

Few Shot Object , Ensor Transformer , Transformer Relation Head , Few Shot Object Detection , Eat Diffusion Process , Igh Order Pooling , Ultiple Order Pooling ,

"Few-Shot Object Detection by Second-Order Pooling" by Shan Zhang, Dawei Luo et al.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
In this paper, we tackle a challenging problem of Few-shot Object Detection rather than recognition. We propose Power Normalizing Second-order Detector consisting of the Encoding Network (EN), the Multi-scale Feature Fusion (MFF), Second-order Pooling (SOP) with Power Normalization (PN), the Hyper Attention Region Proposal Network (HARPN) and Similarity Network (SN). EN takes support image crops and a query image per episode to produce covolutional feature maps across several layers while MFF combines them into multi-scale feature maps. SOP aggregates them per support image while PN detects the presence of visual feature instead of counting its frequency of occurrence. HARPN cross-correlates the PN pooled support features against the query feature map to match regions and produce query region proposals that are then aggregated with S ....

Similarity Network , Hyper Attention Region Proposal Network , Few Shot Object Detection , Power Normalizing Second Order Detector , Encoding Network , Multi Scale Feature Fusion , Second Order Pooling , Power Normalization ,