Search Results
Found 119 papers, 47 papers with code
Read Extensively, Focus Smartly: A Cross-document Semantic Enhancement Method for Visual Documents NER
To deal with this problem, this work proposes a cross-document semantic enhancement method, which consists of two modules: 1) To prevent distractions from irrelevant regions in the current document, we design a learnable attention mask mechanism, which is used to adaptively filter redundant information in the current document.
Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning
Recently, arbitrary-scale point cloud upsampling mechanism became increasingly popular due to its efficiency and convenience for practical applications.
Learning Expressive And Generalizable Motion Features For Face Forgery Detection
However, current sequence-based face forgery detection methods use general video classification networks directly, which discard the special and discriminative motion information for face manipulation detection.
"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach
Modern deep neural networks (DNNs) are extremely powerful; however, this comes at the price of increased depth and having more parameters per layer, making their training and inference more computationally challenging.
LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin
Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks.
MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition
Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with only knowledge bases or gazetteers and unlabeled corpus.
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds
Network embedding, a graph representation learning method illustrating network topology by mapping nodes into lower-dimension vectors, is challenging to accommodate the ever-changing dynamic graphs in practice.
Single Domain Dynamic Generalization for Iris Presentation Attack Detection
To tackle the above issues, we propose a Single Domain Dynamic Generalization (SDDG) framework, which simultaneously exploits domain-invariant and domain-specific features on a per-sample basis and learns to generalize to various unseen domains with numerous natural images.
Taxonomy Completion with Probabilistic Scorer via Box Embedding
Specifically, TaxBox consists of three components: (1) a graph aggregation module to leverage the structural information of the taxonomy and two lightweight decoders that map features to box embedding and capture complex relationships between concepts; (2) two probabilistic scorers that correspond to attachment and insertion operations and ensure the avoidance of pseudo-leaves; and (3) three learning objectives that assist the model in mapping concepts more granularly onto the box embedding space.
Multi-view Adversarial Discriminator: Mine the Non-causal Factors for Object Detection in Unseen Domains
In this work, we present an idea to remove non-causal factors from common features by multi-view adversarial training on source domains, because we observe that such insignificant non-causal factors may still be significant in other latent spaces (views) due to the multi-mode structure of data.
