This work focuses on applying Gas Dynamic Analogous Exposure (GDAE) to assess exposure levels in traffic areas. The original GDAE method faced challenges in obtaining accurate vehicle collision angle parameters. To ad...
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Research on speech emotion recognition (SER) is ongoing and has numerous applications in fields like healthcare, education, and human-computer interaction. The RAVDESS dataset for SER, which includes a varied collecti...
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Hallucinations is a big shadow hanging over the rapidly evolving multimodal large language models(MLLMs), referring to that the generated text is inconsistent with the image content. To mitigate hallucinations, existi...
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Hallucinations is a big shadow hanging over the rapidly evolving multimodal large language models(MLLMs), referring to that the generated text is inconsistent with the image content. To mitigate hallucinations, existing studies mainly resort to an instruction-tuning manner that requires retraining the models with specific data. In this paper, we pave a different way, introducing a training-free method named Woodpecker. Like woodpeckers heal trees, it picks out and corrects hallucinations from the generated text. Concretely, Woodpecker consists of five stages: key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction. Implemented in a post-remedy manner, Woodpecker can easily serve different MLLMs, while being interpretable by accessing intermediate outputs of the five stages. We evaluate Woodpecker both quantitatively and qualitatively and show the huge potential of this new paradigm. On the POPE benchmark, our method obtains a 30.66%/24.33% improvement in accuracy over the baseline MiniGPT-4/mPLUG-Owl. The source code is released at https://***/BradyFU/Woodpecker.
This research paper presents a comprehensive exploration of short-term stock market trend prediction using state-of-the-art machine learning techniques, anchored in a decade-long analysis of the S&P 500 Index. Lev...
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This research delves into the intricate relationship between median income and financial literacy, exploring the socio-economic implications and the potential for predictive modeling. While financial literacy is unden...
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In recent days, Convolutional Neural Networks (CNNs) has demonstrated significant efficacy in the realm of facial recognition owing to their adeptness in extracting discerning features. This study introduces a facial ...
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The paper discusses generative artificial intelligence technologies used to improve the efficiency of fire detection in satellite images. Different detector architectures are proposed and compared in terms of accuracy...
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Recent advances in protecting node privacy on graph data and attacking graph neural networks (GNNs) gain much attention. The eye does not bring these two essential tasks together yet. Imagine an adversary can utilize ...
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Null pointer dereference raises Null Pointer Exceptions (NPEs). There are two groups of approaches to detect NPEs. Type-based approaches carry out strict type-based null safety checking. They heavily rely on annotatio...
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