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Found 11 papers, 1 papers with code
Noise Learning for Text Classification: A Benchmark
However, we find that noise learning in text classification is relatively underdeveloped: 1. many methods that have been proven effective in the image domain are not explored in text classification, 2. it is difficult to conduct a fair comparison between previous studies as they do experiments in different noise settings.
Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach
3D object detection in Bird's-Eye-View (BEV) space has recently emerged as a prevalent approach in the field of autonomous driving.
Ziya2: Data-centric Learning is All LLMs Need
Although many such issues are addressed along the line of research on LLMs, an important yet practical limitation is that many studies overly pursue enlarging model sizes without comprehensively analyzing and optimizing the use of pre-training data in their learning process, as well as appropriate organization and leveraging of such data in training LLMs under cost-effective settings.
Ziya-Visual: Bilingual Large Vision-Language Model via Multi-Task Instruction Tuning
Recent advancements enlarge the capabilities of large language models (LLMs) in zero-shot image-to-text generation and understanding by integrating multi-modal inputs.
Edge Based Oriented Object Detection
In the field of remote sensing, we often utilize oriented bounding boxes (OBB) to bound the objects.
Riemannian Multiclass Logistics Regression for SPD Neural Networks
Moreover, we encompass the most popular classifier in existing SPD networks as a special case of our framework.
LabelPrompt: Effective Prompt-based Learning for Relation Classification
Specifically, associating natural language words that fill the masked token with semantic relation labels (\textit{e. g.} \textit{``org:founded\_by}'') is difficult.
Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning
We design Semantic Attention (SA) and generative Knowledge Disentanglement (KD) to learn the dependence of feasibility and contextuality, respectively.
Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence
We hope that this project will be the foundation of Chinese cognitive intelligence.
PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion
In this paper, we propose a Patch Pyramid Transformer(PPT) to effectively address the above issues. Specifically, we first design a Patch Transformer to transform the image into a sequence of patches, where transformer encoding is performed for each patch to extract local representations.
a high efficiency fully convolutional networks for pixel wise surface defect detection
Our method is composed of a segmentation stage (stage 1), a detection stage (stage 2), and a matting stage (stage 3).
