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Found 32 papers, 9 papers with code
ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
Unlike prior chart understanding benchmarks -- where frontier models perform similarly and near saturation -- our benchmark exposes a substantial gap between model and human performance, while effectively differentiating model capabilities: although humans achieve 93% accuracy, the best-performing model Gemini-2. 5-Pro attains only 63. 0%, and the leading open-source LVLM Qwen2. 5-VL-72B-Instruct achieves only 38. 5%.
Contour Field based Elliptical Shape Prior for the Segment Anything Model
This paper proposes a new approach to integrate the prior of elliptical shapes into the deep learning-based SAM image segmentation techniques using variational methods.
SAFE: Self-Adjustment Federated Learning Framework for Remote Sensing Collaborative Perception
Experiments on real-world image classification and object segmentation datasets validate the effectiveness and reliability of the SAFE framework in complex remote sensing scenarios.
Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations
Current medical AI systems often fail to replicate real-world clinical reasoning, as they are predominantly trained and evaluated on static text and question-answer tasks.
Convex Combination Star Shape Prior for Data-driven Image Semantic Segmentation
Multi-center star shape is a prevalent object shape feature, which has proven effective in model-based image segmentation methods.
BrainMAP: Learning Multiple Activation Pathways in Brain Networks
As such, conventional GNNs struggle to learn from these pathways due to the long-range dependencies of multiple pathways.
Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral Constraints
Diffusion Transformers (DiT) have emerged as a powerful architecture for image and video generation, offering superior quality and scalability.
Understanding Synthetic Context Extension via Retrieval Heads
To defray the cost of pretraining LLMs over long contexts, recent work takes an approach of synthetic context extension: fine-tuning LLMs with synthetically generated long-context data in a post-training stage.
Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild
As Large Language Models (LLMs) excel across tasks and specialized domains, scaling LLMs based on existing models has garnered significant attention, which faces the challenge of decreasing performance when combining disparate models.
To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
Chain-of-thought (CoT) via prompting is the de facto method for eliciting reasoning capabilities from large language models (LLMs).
SDoH-GPT: Using Large Language Models to Extract Social Determinants of Health (SDoH)
Extracting social determinants of health (SDoH) from unstructured medical notes depends heavily on labor-intensive annotations, which are typically task-specific, hampering reusability and limiting sharing.
Learning to Refine with Fine-Grained Natural Language Feedback
Recent work has explored the capability of large language models (LLMs) to identify and correct errors in LLM-generated responses.
$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
Motivated by the research gap and counter-intuitive phenomenon, we propose $\texttt{MoE-RBench}$, the first comprehensive assessment of SMoE reliability from three aspects: $\textit{(i)}$ safety and hallucination, $\textit{(ii)}$ resilience to adversarial attacks, and $\textit{(iii)}$ out-of-distribution robustness.
Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports
This article argues that we can identify the events more accurately by leveraging the event taxonomy.
GraphRCG: Self-Conditioned Graph Generation
In contrast, in this work, we propose a novel self-conditioned graph generation framework designed to explicitly model graph distributions and employ these distributions to guide the generation process.
Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling
To address the specific needs of ELLs, we propose the use of DeBERTa, a state-of-the-art neural language model, for improving automated feedback tools.
Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method
In the realm of patent document analysis, assessing semantic similarity between phrases presents a significant challenge, notably amplifying the inherent complexities of Cooperative Patent Classification (CPC) research.
Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms
This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies.
Enhancing Dynamic Image Advertising with Vision-Language Pre-training
The core of DIA is a query-image matching module performing ad image retrieval and relevance modeling.
CDIDN: A Registration Model with High Deformation Impedance Capability for Long-Term Tracking of Pulmonary Lesion Dynamics
In this paper, we propose a novel registration model called Cascade-Dilation Inter-Layer Differential Network (CDIDN), which exhibits both high deformation impedance capability (DIC) and accuracy.
Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images
This paper addresses the emerging task of recognizing multiple retinal diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images.
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning
Our proposed network architecture includes the incorporation of LSTM and self-attention, which allows the trained policy to adapt to a variable number of agents.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Contrastive Psudo-supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using data augmentation
The automatic classification of radar waveform is a fundamental technique in electronic countermeasures (ECM). Recent supervised deep learning-based methods have achieved great success in a such classification task. However, those methods require enough labeled samples to work properly and in many circumstances, it is not available. To tackle this problem, in this paper, we propose a three-stages deep radar waveform clustering(DRSC) technique to automatically group the received signal samples without labels. Firstly, a pretext model is trained in a self-supervised way with the help of several data augmentation techniques to extract the class-dependent features. Next, the pseudo-supervised contrastive training is involved to further promote the separation between the extracted class-dependent features. And finally, the unsupervised problem is converted to a semi-supervised classification problem via pseudo label generation.
Adaptive Partially-Observed Sequential Change Detection and Isolation
High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications.
Deep Spatio-temporal Sparse Decomposition for Trend Prediction and Anomaly Detection in Cardiac Electrical Conduction
Electrical conduction among cardiac tissue is commonly modeled with partial differential equations, i. e., reaction-diffusion equation, where the reaction term describes cellular stimulation and diffusion term describes electrical propagation.
Short-term probabilistic photovoltaic power forecast based on deep convolutional long short-term memory network and kernel density estimation
Photovoltaic (PV) power is an important way to utilize solar energy.
Flexible Generation of Natural Language Deductions
Natural language is an attractive representation for this purpose -- it is both highly expressive and easy for humans to understand.
Measurement of tunnel coupling in a Si double quantum dot based on charge sensing
In Si quantum dots, valley degree of freedom, in particular the generally small valley splitting and the dot-dependent valley-orbit phase, adds complexities to the low-energy electron dynamics and the associated spin qubit manipulation.
Mesoscale and Nanoscale Physics Quantum Physics
Large eddy simulation/probability density function simulations of the Cambridge turbulent stratified flame series under swirling conditions
The parametric studies show that the differential diffusion has a negligible effect on the mean and r. m. s.
Fluid Dynamics
Effective Distant Supervision for Temporal Relation Extraction
A principal barrier to training temporal relation extraction models in new domains is the lack of varied, high quality examples and the challenge of collecting more.
Nutational resonances, transitional precession, and precession-averaged evolution in binary black-hole systems
We use analytic solutions for generic spin precession at 2PN order to derive Fourier series for the total and orbital angular momenta in which each term is a sinusoid with frequency $\Omega - n\omega$ for integer $n$.
General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena
