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Found 28 papers, 13 papers with code
OPDAI at SemEval-2022 Task 11: A hybrid approach for Chinese NER using outside Wikipedia knowledge
This article describes the OPDAI submission to SemEval-2022 Task 11 on Chinese complex NER.
Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment
Kolmogorov-Arnold Network (KAN) has attracted growing interest for its strong function approximation capability.
ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL
To address this challenge, we propose a novel RObust mUltitask Tuning and collaboration mEthod (ROUTE) to improve the comprehensive capabilities of open-source LLMs for Text2SQL, thereby providing a more practical solution.
Class Balance Matters to Active Class-Incremental Learning
Then for each cluster, we employ greedy selection strategy to ensure that the Gaussian distribution of the sampled features closely matches the Gaussian distribution of all unlabeled features within the cluster.
Exploring Kolmogorov-Arnold networks for realistic image sharpness assessment
Score prediction is crucial in evaluating realistic image sharpness based on collected informative features.
Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution
Detecting and rejecting unknown out-of-distribution (OOD) samples is critical for deployed neural networks to void unreliable predictions.
ESOD: Efficient Small Object Detection on High-Resolution Images
The resulting Efficient Small Object Detection (ESOD) approach is a generic framework, which can be applied to both CNN- and ViT-based detectors to save the computation and GPU memory costs.
Structure-aware Domain Knowledge Injection for Large Language Models
Remarkably, our method demonstrates the potential of comparable improvement against the state-of-the-art MMedLM2 on MMedBench, while significantly reducing the training costs to 5%.
Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions
The key to OOD detection has two aspects: generalized feature representation and precise category description.
CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models
Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the future of automated and personalized healthcare.
IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks
Model Weight Averaging (MWA) is a technique that seeks to enhance model's performance by averaging the weights of multiple trained models.
RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images
This study introduces a novel framework for enhancing domain generalization in medical imaging, specifically focusing on utilizing unlabelled multi-view colour fundus photographs.
OPDAI at SemEval-2024 Task 6: Small LLMs can Accelerate Hallucination Detection with Weakly Supervised Data
This paper mainly describes a unified system for hallucination detection of LLMs, which wins the second prize in the model-agnostic track of the SemEval-2024 Task 6, and also achieves considerable results in the model-aware track.
INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection
Knowledge hallucination have raised widespread concerns for the security and reliability of deployed LLMs.
Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection
Most existing OOD detection methods focused on exploring advanced training skills or training-free tricks to prevent the model from yielding overconfident confidence score for unknown samples.
Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning
When progressing to a new session, pseudo-features are sampled from old-class distributions combined with training images of the current session to optimize the prompt, thus enabling the model to learn new knowledge while retaining old knowledge.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
Uncertainty-aware Unsupervised Multi-Object Tracking
The resulting accurate pseudo-tracklets boost learning the feature consistency.
Sentence-Level Relation Extraction via Contrastive Learning with Descriptive Relation Prompts
Sentence-level relation extraction aims to identify the relation between two entities for a given sentence.
Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques
In this paper, we present our solution to the Multilingual Information Retrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP 2023\footnote{https://project-miracl. github. io/}.
OPD@NL4Opt: An ensemble approach for the NER task of the optimization problem
In this paper, we present an ensemble approach for the NL4Opt competition subtask 1(NER task).
Using Deep Mixture-of-Experts to Detect Word Meaning Shift for TempoWiC
This paper mainly describes the dma submission to the TempoWiC task, which achieves a macro-F1 score of 77. 05% and attains the first place in this task.
Corticosteroid Activation of Atlantic Sea Lamprey Corticoid Receptor: Allosteric Regulation by the N-terminal Domain
Lampreys are jawless fish that evolved about 550 million years ago at the base of the vertebrate line.
A Semantic Alignment System for Multilingual Query-Product Retrieval
Our models are all trained with cross-entropy loss to classify the query-product pairs into ESCI 4 categories at first, and then we use weighted sum with the 4-class probabilities to get the score for ranking.
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection
The likelihood maps generated by the SLV module are used to supervise the feature learning of the backbone network, encouraging the network to attend to wider and more diverse areas of the image.
Dynamic Supervisor for Cross-dataset Object Detection
The application of cross-dataset training in object detection tasks is complicated because the inconsistency in the category range across datasets transforms fully supervised learning into semi-supervised learning.
Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware Association
This paper presents a new method to solve keypoint detection and instance association by using Transformer.
Ranked #10 on
Multi-Person Pose Estimation
on MS COCO
Guiding Query Position and Performing Similar Attention for Transformer-Based Detection Heads
After DETR was proposed, this novel transformer-based detection paradigm which performs several cross-attentions between object queries and feature maps for predictions has subsequently derived a series of transformer-based detection heads.
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection
In this paper, we propose a spatial likelihood voting (SLV) module to converge the proposal localizing process without any bounding box annotations.
