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Found 19 papers, 6 papers with code
What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities
To evaluate the diverse capabilities of virtual agents on the graph, we further present OmniEval, a multidimensional evaluation framework that includes subtask-level evaluation, graph-based metrics, and comprehensive tests across 10 capabilities.
Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents
A major challenge in student simulation is modeling the diverse learning patterns of students at various cognitive levels.
Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer
In this paper, we propose TransKT, a contrastive cross-course knowledge tracing method that leverages concept graph guided knowledge transfer to model the relationships between learning behaviors across different courses, thereby enhancing knowledge state estimation.
Adaptation Method for Misinformation Identification
To address the problems, we propose ADOSE, an Active Domain Adaptation (ADA) framework for multimodal fake news detection which actively annotates a small subset of target samples to improve detection performance.
HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation
To effectively learn the HealthGPT, we devise a comprehensive medical domain-specific comprehension and generation dataset called VL-Health.
Argumentation Computation with Large Language Models : A Benchmark Study
In this paper, we aim to investigate the capability of LLMs in determining the extensions of various abstract argumentation semantics.
Semantic Alignment for Multimodal Large Language Models
By involving the bidirectional semantic guidance between different images in the visual-token extraction process, SAM aims to enhance the preservation of linking information for coherent analysis and align the semantics of different images before feeding them into LLM.
Backpropagation-Free Multi-modal On-Device Model Adaptation via Cloud-Device Collaboration
As these systems grapple with shifting data distributions between the cloud and devices, the traditional approach of fine-tuning-based adaptation (FTA) exists the following issues: the costly and time-consuming data annotation required by FTA and the looming risk of model overfitting.
Generalization Gap in Data Augmentation: Insights from Illumination
In the field of computer vision, data augmentation is widely used to enrich the feature complexity of training datasets with deep learning techniques.
Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by Integrating Expert Knowledge and Interpretable Data-driven Intelligence
This study investigates the performance, interpretability, and clinical utility of KGDM in the diagnosis of infectious keratitis (IK), which is the leading cause of corneal blindness.
Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer
Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate. This setting neglects the more practical scenario where training data are collected from multiple sources.
Panoptic Scene Graph Generation with Semantics-Prototype Learning
To promise consistency and accuracy during the transfer process, we propose to measure the invariance of representations in each predicate class, and learn unbiased prototypes of predicates with different intensities.
Ranked #3 on
Panoptic Scene Graph Generation
on PSG Dataset
Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models
Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-training models to adapt to downstream tasks in a parameter -- and data -- efficient way, by learning the ``soft prompts'' to condition frozen pre-training models.
WINNER: Weakly-Supervised hIerarchical decompositioN and aligNment for Spatio-tEmporal Video gRounding
WINNER first builds the language decomposition tree in a bottom-up manner, upon which the structural attention mechanism and top-down feature backtracking jointly build a multi-modal decomposition tree, permitting a hierarchical understanding of unstructured videos.
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active Learning
Moreover, we treat the uncertainty score of frames in a video as a whole, and estimate the difficulty of each video, which can further relieve the burden of video selection.
BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval
In this scenario, the input image serves as an intuitive context and background for the search, while the corresponding language expressly requests new traits on how specific characteristics of the query image should be modified in order to get the intended target image.
Enhancing Fairness of Visual Attribute Predictors
The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes.
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding
To achieve effective grounding under a limited annotation budget, we investigate one-shot video grounding, and learn to ground natural language in all video frames with solely one frame labeled, in an end-to-end manner.
Geometry of the Minimum Volume Confidence Sets
Prior work showed the minimum-volume confidence sets are the level-sets of a discontinuous function defined by an exact p-value.
