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Found 21 papers, 10 papers with code
Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation
To address these issues, we first propose a novel inter- and intra-uncertainty regularization method to measure and constrain both inter- and intra-inconsistencies in the teacher-student architecture.
Advances of Deep Learning in Protein Science: A Comprehensive Survey
Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological processes.
Graph-level Protein Representation Learning by Structure Knowledge Refinement
The mainstream method is utilizing contrastive learning to facilitate graph feature extraction, known as Graph Contrastive Learning (GCL).
MMDesign: Multi-Modality Transfer Learning for Generative Protein Design
Protein design involves generating protein sequences based on their corresponding protein backbones.
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures.
Co-modeling the Sequential and Graphical Routes for Peptide Representation Learning
Peptides are formed by the dehydration condensation of multiple amino acids.
Shadow Removal of Text Document Images Using Background Estimation and Adaptive Text Enhancement
Thirdly, we propose an adaptive text contrast enhancement strategy to generate shadow-free results with comfortable visual perception across shadow and non-shadow regions.
Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer
In this paper, we propose a \textit{simple yet effective} model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner.
Lightweight Contrastive Protein Structure-Sequence Transformation
In this work, we introduce a novel unsupervised protein structure representation pretraining with a robust protein language model.
CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment
In this work, we propose a novel contrastive visual-textual transformation for SLR, CVT-SLR, to fully explore the pretrained knowledge of both the visual and language modalities.
Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy
Learning meaningful protein representation is important for a variety of biological downstream tasks such as structure-based drug design.
Protein Language Models and Structure Prediction: Connection and Progression
The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding.
MogaNet: Multi-order Gated Aggregation Network
Notably, MogaNet hits 80. 0\% and 87. 8\% accuracy with 5. 2M and 181M parameters on ImageNet-1K, outperforming ParC-Net and ConvNeXt-L, while saving 59\% FLOPs and 17M parameters, respectively.
Ranked #1 on
Pose Estimation
on COCO val2017
Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation
Therefore, we propose to introduce additional word-level semantic knowledge of sign language linguistics to assist in improving current end-to-end neural SLT models.
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e. g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability.
A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19
Representative algorithms are described in detail, coupled with some typical techniques that are described briefly.
A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information
Research on document-level Neural Machine Translation (NMT) models has attracted increasing attention in recent years.
SparseFusion: Dynamic Human Avatar Modeling from Sparse RGBD Images
First, based on a generative human template, for every two frames having sufficient overlap, an initial pairwise alignment is performed; It is followed by a global non-rigid registration procedure, in which partial results from RGBD frames are collected into a unified 3D shape, under the guidance of correspondences from the pairwise alignment; Finally, the texture map of the reconstructed human model is optimized to deliver a clear and spatially consistent texture.
Beyond Context: Exploring Semantic Similarity for Tiny Face Detection
Tiny face detection aims to find faces with high degrees of variability in scale, resolution and occlusion in cluttered scenes.
Detailed Surface Geometry and Albedo Recovery from RGB-D Video Under Natural Illumination
In this paper we present a novel approach for depth map enhancement from an RGB-D video sequence.
Interactive Visual Hull Refinement for Specular and Transparent Object Surface Reconstruction
We discovered that these internal contours, which are results of convex parts on an object's surface, can lead to a tighter fit than the original visual hull.
