Wonseok Choi

Moon Ye-Bin, Nam Hyeon-Woo, Wonseok Choi et al.

Vision language models (VLMs) perceive the world through a combination of a visual encoder and a large language model (LLM). The visual encoder, pre-trained on large-scale vision-text datasets, provides zero-shot generalization to visual data, and the LLM endows its high reasoning ability to VLMs. It leads VLMs to achieve high performance on wide benchmarks without fine-tuning, exhibiting zero or few-shot capability. However, recent studies show that VLMs are vulnerable to hallucination. This undesirable behavior degrades reliability and credibility, thereby making users unable to fully trust the output from VLMs. To enhance trustworthiness and better tackle the hallucination of VLMs, we curate a new evaluation dataset, called the BEfore-AFter hallucination dataset (BEAF), and introduce new metrics: True Understanding (TU), IGnorance (IG), StuBbornness (SB), and InDecision (ID). Unlike prior works that focus only on constructing questions and answers, the key idea of our benchmark is to manipulate visual scene information by image editing models and to design the metrics based on scene changes. This allows us to clearly assess whether VLMs correctly understand a given scene by observing the ability to perceive changes. We also visualize image-wise object relationship by virtue of our two-axis view: vision and text. Upon evaluating VLMs with our dataset, we observed that our metrics reveal different aspects of VLM hallucination that have not been reported before. Project page: \url{https://beafbench.github.io/}

Moon Ye-Bin, Nam Hyeon-Woo, Wonseok Choi et al.

Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully curate training data, but given the enormous scale of modern neural networks, this is prohibitively labor-intensive and thus impractical. Inspired by recent developments in generative models, this paper explores the potential of synthetic data to address the data imbalance problem. To be specific, our method, dubbed SYNAuG, leverages synthetic data to equalize the unbalanced distribution of training data. Our experiments demonstrate that, although a domain gap between real and synthetic data exists, training with SYNAuG followed by fine-tuning with a few real samples allows to achieve impressive performance on diverse tasks with different data imbalance issues, surpassing existing task-specific methods for the same purpose.

Nam Hyeon-Woo, Moon Ye-Bin, Wonseok Choi et al.

Vision language models (VLMs) have shown promising reasoning capabilities across various benchmarks; however, our understanding of their visual perception remains limited. In this work, we propose an eye examination process to investigate how a VLM perceives images, specifically focusing on key elements of visual recognition, from primitive color and shape to semantic levels. To this end, we introduce a dataset named LENS to guide a VLM to follow the examination and check its readiness. Once the model is ready, we conduct the examination. Through this examination, we quantify and visualize VLMs' sensitivities to color and shape, and semantic matching. Our findings reveal that VLMs have varying sensitivity to different colors while consistently showing insensitivity to green across different VLMs. Also, we found different shape sensitivity and semantic recognition depending on LLM's capacity despite using the same fixed visual encoder. Our analyses and findings have potential to inspire the design of VLMs and the pre-processing of visual input to VLMs for improving application performance.

Jusop Choi, Wonseok Choi, William Aiken et al.

Since Bitcoin appeared in 2009, over 6,000 different cryptocurrency projects have followed. The cryptocurrency world may be the only technology where a massive number of competitors offer similar services yet claim unique benefits, including scalability, fast transactions, and security. But are these projects really offering unique features and significant enhancements over their competitors? To answer this question, we conducted a large-scale empirical analysis of code maintenance activities, originality and security across 592 crypto projects. We found that about half of these projects have not been updated for the last six months; over two years, about three-quarters of them disappeared, or were reported as scams or inactive. We also investigated whether 11 security vulnerabilities patched in Bitcoin were also patched in other projects. We found that about 80% of 510 C-language-based cryptocurrency projects have at least one unpatched vulnerability, and the mean time taken to fix the vulnerability is 237.8 days. Among those 510 altcoins, we found that at least 157 altcoins are likely to have been forked from Bitcoin, about a third of them containing only slight changes from the Bitcoin version from which they were forked. As case studies, we did a deep dive into 20 altcoins (e.g., Litecoin, FujiCoin, and Feathercoin) similar to the version of Bitcoin used for the fork. About half of them did not make any technically meaningful change - failing to comply with the promises (e.g., about using Proof of Stake) made in their whitepapers.