Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization

Computer Vision (CV) has yet to fully achieve the zero-shot task generalization observed in Natural Language Processing (NLP), despite following many of the milestones established in NLP, such as large transformer models, extensive pre-training, and the auto-regression paradigm, among others. In this paper, we explore the idea that CV adopts discrete and terminological task definitions (\eg, image segmentation''), which may be a key barrier to zero-shot task generalization. Our hypothesis is that without truly understanding previously-seen tasks--due to these terminological definitions--deep models struggle to generalize to novel tasks. To verify this, we introduce Explanatory Instructions, which provide an intuitive way to define CV task objectives through detailed linguistic transformations from input images to outputs. We create a large-scale dataset comprising 12 million image input to explanatory instruction to output'' triplets, and train an auto-regressive-based vision-language model (AR-based VLM) that takes both images and explanatory instructions as input. By learning to follow these instructions, the AR-based VLM achieves instruction-level zero-shot capabilities for previously-seen tasks and demonstrates strong zero-shot generalization for unseen CV tasks. Code and dataset will be openly available on our GitHub repository.

Host: Hey everyone, and welcome back to the podcast! It’s Leo here, and I’m super excited about today’s episode. We're diving into something that's fundamental to the world of research and academia, yet often remains in the background – the arXiv e-print repository. I feel like most people who aren't in research have never even heard of it, but it's a huge deal for those in the sciences.

Guest: Absolutely, Leo! It's funny how such a crucial resource can be relatively unknown to the general public. I mean, for many researchers, especially in physics, math, computer science, and related fields, it’s basically their daily newspaper. It's where they go to see the latest developments, the cutting-edge ideas, and the work that's literally shaping the future.

Host: Yeah, exactly. So, for those who might be unfamiliar, arXiv is basically a digital archive where researchers can share their pre-prints, right? Before the formal peer-review process even begins in most cases. It's like a massive online library that's constantly updated with new research papers. That really changes things when you think about it; we're not waiting for journal publication cycles anymore.

Guest: You've hit the nail on the head there, Leo. The pre-print aspect is really the game changer. Traditionally, you'd do your research, write it up, submit it to a journal, wait months, maybe even a year or more for it to be peer-reviewed, and then if it got accepted, it'd finally be published. arXiv really bypasses that whole timeframe, allowing researchers to share their findings almost instantly with the community. Think about the speed of discovery now!

Host: That's a huge benefit. It speeds things up and also facilitates a much more open exchange of ideas. Think about it - researchers can immediately start building on each other’s work without the lag of traditional publication methods. It also allows for more direct feedback, right? People can read and comment on pre-prints, which can actually help improve a research paper before it even goes through official peer review. It's like real-time collaboration on a grand scale, almost like an open source project for science.

Guest: Exactly! The feedback aspect is huge. It's not just about speeding up the publication process; it's about improving the research itself. Having other experts in your field see your work early on can really catch errors, suggest new approaches, or simply highlight things you might have missed. It’s a form of crowdsourced peer review before the official process begins. It also democratizes research to an extent because it means that researchers outside the big name institutions have more visibility.

Host: And I imagine this has had a significant impact on certain fields that move very quickly, like AI or theoretical physics? I always feel like I see the coolest stuff on arXiv and it never really makes it into news outside of the research community.

Guest: Absolutely. Especially in fields that are rapidly evolving like artificial intelligence, machine learning, and areas of theoretical physics, arXiv is critical. These fields are moving so fast that waiting for traditional publication cycles would mean that by the time something is published it might already be old news. With arXiv, researchers can be at the cutting edge and stay on top of the latest breakthroughs. Also, things that might be too niche or too theoretical for some journals can still find a home on arXiv and get the exposure they deserve. It acts as a kind of open, unfiltered stream of research, which is pretty remarkable.

Host: It’s kind of mind-blowing to think about how much information is being exchanged on there. It's not just a repository; it's a community of scholars constantly building on each other's ideas. But, I guess it comes with its own set of challenges, right? Like, how do you filter through the massive amount of content that's constantly being added? How do you know what’s good and what isn’t? It's all pre-print, so there's a level of trust required.

Guest: That’s a very valid point, Leo. With the vast volume of pre-prints being uploaded, the signal-to-noise ratio can be a challenge. It's not like a curated journal where everything is meticulously peer-reviewed. There's a bit of a 'buyer beware' situation. Researchers need to be critical and assess papers carefully. Often, people will have their own trusted networks that they follow closely, researchers whose work they know and respect. This adds a kind of social filter to the process. You also start to know the places where good research tends to be published and gravitate to them on arXiv. And the metadata is crucial here. Keywords, topic categories, and authors all play a significant role in being able to navigate the information landscape.

Host: That makes sense. It highlights the importance of researchers developing critical evaluation skills, as well as building their networks. It's not just about blindly accepting everything that comes out on arXiv. There's that extra layer of responsibility on the reader. I'm also thinking about the digital divide too - I mean, researchers everywhere in the world have access to this platform, which levels the playing field, right? But do they all have the same resources or ability to use it effectively?

Guest: That's a really important point, Leo. While arXiv does provide a more accessible platform for researchers globally, it doesn’t entirely eliminate all the inequalities in the academic world. The quality of internet access, the resources to conduct research in the first place, and even the cultural norms of academic publishing still play a significant role. Researchers in well-funded institutions with established reputations might still have an easier time gaining recognition, even on a platform like arXiv. It’s definitely a more open and equitable system, but it's not a perfect solution to all the systemic issues in academia, if you get my meaning. You need time to even learn the system.

Host: Yeah, it's like a tool that can be used for good, but we have to be aware that the tool itself doesn't solve all problems. It's about more than just access, it's about the capacity to engage effectively. Thinking about what you said before about the signal-to-noise, there must be so much information being put out. I'm wondering what mechanisms or tools people use to keep up? Like, it’s hard enough to keep up with the news in my own field, never mind wading into new scientific discoveries every day.

Guest: Absolutely, Leo. Keeping up with arXiv, especially if you are dealing with multiple overlapping fields, can feel like trying to drink from a firehose. There are a variety of strategies and tools researchers use. First, there are automated alerts. You can set up specific keywords or author alerts so you're notified when something that matches your interests is posted. Many people also use RSS feeds to stay updated, using their preferred reader. Then there’s also the power of social media; people often share papers they think are interesting on Twitter and other platforms. And, as I mentioned earlier, building networks is key. The researchers you trust might share papers that are of interest.

Host: That makes perfect sense. It's a combination of technology and human networking. And it all makes me think that this sort of pre-print model will become the dominant form of communication for scientific findings. Do you see this as a future trend, that the formal journal system is increasingly becoming less important?

Guest: That's the million-dollar question, isn't it? It's hard to say definitively, but I think it's very possible, Leo. The formal journal system has been around for centuries, and it definitely still has value. Peer review provides a level of quality control that arXiv doesn’t offer, and getting published in prestigious journals still carries a lot of weight in the academic world. However, the speed and accessibility of platforms like arXiv are undeniable. We might see more of a hybrid approach, where pre-prints become the standard for quick dissemination, while journals might focus more on the archival function and providing that stamp of formal approval.

Host: That hybrid model sounds very plausible. It’s like the best of both worlds. The speed of the pre-print with the traditional review. And looking at the actual arXiv site itself, I noticed it's supported by organizations like the Simons Foundation and Cornell University. That's interesting, right? I'd imagine there's significant infrastructure required to manage such a huge repository. It's not just about storing files, there’s metadata, search functionality, all the things we talked about earlier. There’s so much that goes into that.

Guest: You're absolutely correct, Leo. Running a platform like arXiv is no small feat. It requires significant financial resources, technical expertise, and ongoing maintenance. The support from the Simons Foundation and institutions like Cornell is critical to keeping it running. It’s essentially a public good, and the collaborative model, with member institutions contributing, is important. It is a non-profit and community-led effort, which is interesting when you consider how much research is normally handled by for-profit publishers. The infrastructure has to be top-notch for such a massive data set, too; the storage alone is a huge consideration.

Host: And it's not just storing research; they also have to deal with the different formats, like LaTeX, which a lot of scientific papers are written in. And the site also mentions issues with converting to HTML sometimes, which makes sense given the complexity of these documents. This highlights another challenge of building these types of systems, just all the different content formats and how to display them consistently. Thinking about the future, how well does the current infrastructure scale with the growth of research? Are there limits to the pre-print system?

Guest: That's a crucial question, Leo. Scalability is definitely a challenge. As more researchers worldwide contribute to the repository and the amount of data continues to grow, maintaining the speed, reliability, and efficiency of the platform will require ongoing investment. And you mentioned LaTeX, it is a good example of format challenges. It is pretty common in physics and math, but less so in other areas, and the code itself has to be processed, and converted. There are also the accessibility concerns; how easy is it for people with disabilities to use and navigate all this content? The conversion to HTML they mention on the site is a key part of that, and that’s an ongoing effort. It's not just a tech issue, it's a social and ethical consideration too.

Host: Accessibility is a key point, because the value of these systems is really limited if some groups can't effectively use them. And thinking about the sheer amount of content, it's really an information management problem on a massive scale. And that makes me wonder, how do they ensure the quality of the research itself? We talked about peer review as something distinct from pre-prints, but does arXiv have any mechanisms in place to protect against plagiarism or other academic misconduct? Or is it pretty much a free-for-all in terms of content quality?

Guest: That’s a critical question, Leo. While arXiv isn't set up as a peer-review platform, they do have certain measures in place to try and prevent fraud and misconduct. There's a moderation team that flags papers that might be problematic, like obvious plagiarism, for example. They also rely on community reporting; researchers will often call out questionable work if they notice something. I think in the end, a lot of it comes down to the self-correcting nature of the scientific community. Researchers tend to build on credible work, and work that’s fraudulent usually gets discovered pretty quickly. It's not foolproof, of course, but the community helps to police the system itself in a lot of ways. And you do see a lot of corrections, and retractions on the site, so even post publication there is that process at play.