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Found 13 papers, 7 papers with code
De-Bias for Generative Extraction in Unified NER Task
Named entity recognition (NER) is a fundamental task to recognize specific types of entities from a given sentence.
De-Biased Court's View Generation with Causality
Court{'}s view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation.
From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction
In this paper, we introduce a novel From Graph to Word Bag (FWGB) approach, which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge's reasoning process.
Enhancing Court View Generation with Knowledge Injection and Guidance
Court View Generation (CVG) is a challenging task in the field of Legal Artificial Intelligence (LegalAI), which aims to generate court views based on the plaintiff claims and the fact descriptions.
Adversarial Masking Contrastive Learning for vein recognition
To overcome this problem, in this paper, we propose an adversarial masking contrastive learning (AMCL) approach, that generates challenging samples to train a more robust contrastive learning model for the downstream palm-vein recognition task, by alternatively optimizing the encoder in the contrastive learning model and a set of latent variables.
Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration
Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems.
Attentional Local Contrast Networks for Infrared Small Target Detection
To mitigate the issue of minimal intrinsic features for pure data-driven methods, in this paper, we propose a novel model-driven deep network for infrared small target detection, which combines discriminative networks and conventional model-driven methods to make use of both labeled data and the domain knowledge.
Asymmetric Contextual Modulation for Infrared Small Target Detection
Single-frame infrared small target detection remains a challenge not only due to the scarcity of intrinsic target characteristics but also because of lacking a public dataset.
Attentional Feature Fusion
Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures.
Ranked #654 on
Image Classification
on ImageNet
Attention as Activation
Inspired by their similarity, we propose a novel type of activation units called attentional activation (ATAC) units as a unification of activation functions and attention mechanisms.
Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection
They work well on the images with homogeneous backgrounds and high-contrast targets.
Laplacian regularized low rank subspace clustering
This problem is solved in the low rank subspace clustering model which decomposes the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and gross errors.
Subspace clustering based on low rank representation and weighted nuclear norm minimization
Subspace clustering refers to the problem of segmenting a set of data points approximately drawn from a union of multiple linear subspaces.
