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Found 9 papers, 1 papers with code
VisualSimpleQA: A Benchmark for Decoupled Evaluation of Large Vision-Language Models in Fact-Seeking Question Answering
Large vision-language models (LVLMs) have demonstrated remarkable achievements, yet the generation of non-factual responses remains prevalent in fact-seeking question answering (QA).
PCQPR: Proactive Conversational Question Planning with Reflection
Conversational Question Generation (CQG) enhances the interactivity of conversational question-answering systems in fields such as education, customer service, and entertainment.
A Label-Free and Non-Monotonic Metric for Evaluating Denoising in Event Cameras
To overcome these limitations, we propose the first label-free and non-monotonic evaluation metric, the area of the continuous contrast curve (AOCC), which utilizes the area enclosed by event frame contrast curves across different time intervals.
SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation
Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB.
A Survey on Neural Question Generation: Methods, Applications, and Prospects
In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG), a field leveraging neural network techniques to generate relevant questions from diverse inputs like knowledge bases, texts, and images.
Diversifying Question Generation over Knowledge Base via External Natural Questions
The above insights make diversifying question generation an intriguing task, where the first challenge is evaluation metrics for diversity.
SeqXFilter: A Memory-efficient Denoising Filter for Dynamic Vision Sensors
We not only give the visual denoising effect of the filter but also use two metrics for quantitatively analyzing the filter's performance.
A Noise Filter for Dynamic Vision Sensors using Self-adjusting Threshold
Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imagers.
Emerging Technologies Signal Processing
Exploration of Input Patterns for Enhancing the Performance of Liquid State Machines
To mitigate the difficulty in effectively dealing with huge input spaces of LSM, and to find that whether input reduction can enhance LSM performance, we explore several input patterns, namely fullscale, scanline, chessboard, and patch.
