A conventional camera often suffers from over-or under-exposure when recording a real-world scene with a very high dynamic range (HDR). In contrast, a modulo camera with a Markov random field (MRF) based unwrapping algorithm can theoretically accomplish unbounded dynamic range but shows degenerate performances when there are modulus-intensity ambiguity, strong local contrast, and color misalignment. In this paper, we reformulate the modulo image unwrapping problem into a series of binary labeling problems and propose a modulo edge-aware model, named as UnModNet, to iteratively estimate the binary rollover masks of the modulo image for unwrapping. Experimental results show that our approach can generate 12-bit HDR images from 8-bit modulo images reliably, and runs much faster than the previous MRF-based algorithm thanks to the GPU acceleration.

When blockchain systems are said to be trustless, what this really means is that all the trust is put into software. Thus, there are strong incentives to ensure blockchain software is correct -- vulnerabilities here cost millions and break businesses. One of the most powerful ways of establishing software correctness is by using formal methods. Approaches based on formal methods, however, induce a significant overhead in terms of time and expertise required to successfully employ them. Our work addresses this critical disadvantage by automating the creation of a formal model -- a mathematical abstraction of the software system -- which is often a core task when employing formal methods. We perform model synthesis in three phases: we first transpile the code into model stubs; then we "fill in the blanks" using a large language model (LLM); finally, we iteratively repair the generated model, on both syntactical and semantical level. In this way, we significantly reduce the amount of time necessary to create formal models and increase accessibility of valuable software verification methods that rely on them. The practical context of our work was reducing the time-to-value of using formal models for correctness audits of smart contracts.

Weaknesses: My primary concern with this paper is that the problem it is addressing is *extremely* niche --- Modulo cameras are a somewhat obscure problem even within the realm of the computational imaging community. If I was reviewing this paper for a computational imaging/photography conference, I would be more charitable towards this paper. But this subject is unlikely to be of interest to the general NeurIPS audience, and this paper seems unlikely to reach its intended audience if presented at NeurIPS. And the specifics of this neural network architecture are so specifically tailored to this particular problem that I'm not sure what a general ML researcher could come away from this paper with, nor am I convinced that this is a problem that should be popularized with ML researchers as, again, a solution to this problem has limited practical value given that modulo cameras are still a largely hypothetical concept. My other concern with this paper (which would be a significant concern even if I were reviewing this paper in a computational imaging conference) is that the baseline evaluation is misleading.

The submission has received two positive and two negative reviews. The post-rebuttal discussion has not lead to convergence, and the opinion of the reviewers remain split. The concerns of the "negative" reviewers are: 1) The application is too niche (R1). However, the topic of the paper falls into NeurIPS call for papers, as it is related to low-level computer vision, compressed sensing, deep neural architectures. The authors rebut that the results in [55] were cherry-picked and that they use the code from [55], while fixing the parameters.

A conventional camera often suffers from over- or under-exposure when recording a real-world scene with a very high dynamic range (HDR). In contrast, a modulo camera with a Markov random field (MRF) based unwrapping algorithm can theoretically accomplish unbounded dynamic range but shows degenerate performances when there are modulus-intensity ambiguity, strong local contrast, and color misalignment. In this paper, we reformulate the modulo image unwrapping problem into a series of binary labeling problems and propose a modulo edge-aware model, named as UnModNet, to iteratively estimate the binary rollover masks of the modulo image for unwrapping. Experimental results show that our approach can generate 12-bit HDR images from 8-bit modulo images reliably, and runs much faster than the previous MRF-based algorithm thanks to the GPU acceleration.

Unwrap.ai is on a mission to make every company more customer centric. We're helping companies collect and process feedback more effectively from sources like Zendesk, App Store reviews, Reddit, and Twitter. We also build tools to help users feel heard by making it easier for them to submit feedback and follow relevant product improvements. Our founders, two ex-Amazon Alexa Product Managers, were tired of manually sifting through customer reviews, support tickets, and bugs while working on Alexa. They understood the importance of listening to customers and prioritizing their requests effectively, but simply had too much feedback to parse through.

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The scientific world is developing a knack for reading texts without opening them. Researchers in Israel and the US have conducted the first "virtual unwrapping" of a heavily damaged scroll, the En-Gedi scroll, to read its contents without destroying what's left. The team used a high-resolution volumetric scan to create a 3D model of the scroll, looked for bright pixels in the model (a sign of where the ink would be) and virtually flattened the scroll to make text segments readable. The process is slow, as you have to piece together segments and reconstruct lines of text that have been lost to the ages. However, the results were worth it in this case: the researchers discovered that this is the earliest known copy of a Pentateuchal book from the Bible (Leviticus) to be found in a Holy Ark, dating back "at least" 1,500 years.