To address these issues, we introduce BreakGPT, the first large language model for financial breakout detection.
Pillar-based methods mainly employ randomly initialized 2D convolution neural network (ConvNet) for feature extraction and fail to enjoy the benefits from the backbone scaling and pretraining in the image domain.
In light of the success of sample mining techniques in 2D object detection, we propose a simple yet effective mining strategy for improving depth perception in 3D object detection.
In this work, given the excellent scalability of web data, we consider self-supervised pre-training on noisy web sourced image-text paired data.
LoDA and SimSeg jointly ameliorate a vanilla CLIP to produce impressive semantic segmentation results.
This work simultaneously considers the discriminability and transferability properties of deep representations in the typical supervised learning task, i. e., image classification.
However, these works require a tremendous amount of data and computational resources (e. g., billion-level web data and hundreds of GPUs), which prevent researchers with limited resources from reproduction and further exploration.
To meet these two concerns, we comprehensively evaluate a collection of existing refinements to improve the performance of PP-YOLO while almost keep the infer time unchanged.
Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object.
Ranked #73 on Object Detection on COCO test-dev
Machine learning, especially deep learning, is dramatically changing the methods associated with optical thin-film inverse design.
A joint loss is then defined as the weighted summation of cls and reg losses as the assigning indicator.
Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional real-valued embeddings which aim to distinguish subtle visual differences of fine-grained objects.
The first imbalance lies in the large number of low-quality RPN proposals, which makes the R-CNN module (i. e., post-classification layers) become highly biased towards the negative proposals in the early training stage.
To acquire the visible parts, a novel Paired-Box Model (PBM) is proposed to simultaneously predict the full and visible boxes of a pedestrian.
PS-RCNN first detects slightly/none occluded objects by an R-CNN module (referred as P-RCNN), and then suppress the detected instances by human-shaped masks so that the features of heavily occluded instances can stand out.
Ranked #2 on Object Detection on WiderPerson
This model can converge the global optimum of the optical thin film structure, this will greatly improve the design efficiency of multi-layer films.