Search Results for author:
Found 9 papers, 2 papers with code
SGNet: Folding Symmetrical Protein Complex with Deep Learning
Deep learning has made significant progress in protein structure prediction, advancing the development of computational biology.
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-following.
Chinese Open Instruction Generalist: A Preliminary Release
Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat. openai. com/}}.
BPFNet: A Unified Framework for Bimodal Palmprint Alignment and Fusion
Bimodal palmprint recognition leverages palmprint and palm vein images simultaneously, which achieves high accuracy by multi-model information fusion and has strong anti-falsification property.
Touchless Palmprint Recognition based on 3D Gabor Template and Block Feature Refinement
To our best knowledge, it is the largest contactless palmprint image benchmark ever collected with regard to the number of individuals and palms.
Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition
In 3D shape recognition, multi-view based methods leverage human's perspective to analyze 3D shapes and have achieved significant outcomes.
Gram Regularization for Multi-view 3D Shape Retrieval
To make up the gap, in this paper, we propose a novel regularization term called Gram regularization which reinforces the learning ability of the network by encouraging the weight kernels to extract different information on the corresponding feature map.
Rethinking Loss Design for Large-scale 3D Shape Retrieval
In this paper, we propose the Collaborative Inner Product Loss (CIP Loss) to obtain ideal shape embedding that discriminative among different categories and clustered within the same class.
Angular Triplet-Center Loss for Multi-view 3D Shape Retrieval
How to obtain the desirable representation of a 3D shape, which is discriminative across categories and polymerized within classes, is a significant challenge in 3D shape retrieval.
