Modern detection transformers (DETRs) use a set of object queries to predict a list of
bounding boxes, sort them by their classification confidence scores, and select the top …

We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to
combine with the segment anything model (SAM). This integration enables the detection and …

In this paper, we propose a new operator, called 3D DeFormable Attention (DFA3D), for 2D-
to-3D feature lifting, which transforms multi-view 2D image features into a unified 3D space …

Few-shot object detection (FSOD) aims to detect objects with only a few training examples.
Visual feature extraction and query-support similarity learning are the two critical …

Detection models have been extensively employed for the Table Structure Recognition
(TSR) task, aiming to convert table images into structured formats by detecting table …

Emotion recognition aims to discern the emotional state of subjects within an image, relying
on subject-centric and contextual visual cues. Current approaches typically follow a two …

Popular transformer-based detectors detect objects in a one-to-one manner, where both the
bounding box and category of each object are predicted only by the single query, leading to …

Deep learning-based defect detection approaches offer great potential for end-to-end
surface defect detection. After the prevalent Convolutional Neural Network (CNN) models …

Transformer models have achieved significant mile-stones in the field of Artificial Intelligence
in recent years, primarily focusing on text processing and natural language processing …

Solar panels play a crucial role in converting solar energy into electricity, with PhotoVoltaic
(PV) modules being their core components. To ensure solar panels function well, efficient …