We expand Eq. 10 as: q There are three KL divergence terms in our training objective ELBO (Eq. Medium and Y elp Large datasets, we follow (Guu et al., 2018) to use a three-layer attentional LSTM Skip connections are also used between adjacent LSTM layers. We apply annealing and free-bits techniques following (Li et al., 2019) to the KL term on prototype variable, As in Section 4.3, here we show more generated examples through interpolation on MSCOCO dataset. Table 6: Qualitative examples from the MSCOCO dataset on interpolated sentence generation given the prototype.

Instruction tuning large language models (LLMs) using machine-generated instruction-following data has been shown to improve zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field.

Breakthroughs, discoveries, and DIY tips sent every weekday. In the 1999 animated film, Toy Story 2, Buzz Lightyear and his fellow sentient playthings enter an airport's'back-of-the-house' to rescue Sheriff Woody Pride from a suitcase. Together the toys encounter a world of diverging conveyor belts, inclines and declines, and seemingly endless turns, all designed to carry checked baggage from an airline ticket counter or curbside kiosk to its intended flights. According to Michael Rangole, maintenance manager for Vanderlande Industries--the company that maintains and operates the baggage handling system at California's San José Mineta International Airport (SJC)--Toy Story 2 is incredibly accurate. "San Jose's system alone has over 120 curves," says Rangole.

A yellow suitcase draws me in like a beacon. It is stacked on a dark shelf at the back of Greasby's auction house in Tooting, south London, and looks brand new, with a hard exterior and wheels that Richard Stacey, a Greasby's regular who is dressed in shorts, a plaid shirt and a cream bucket hat, tells me to test. So I test them – and they work. If I was just buying a bag, that is all I would need to know. But this isn't just a bag: the zip is locked and when I lift it, it is heavy.

We expand Eq. 10 as: q There are three KL divergence terms in our training objective ELBO (Eq. Medium and Y elp Large datasets, we follow (Guu et al., 2018) to use a three-layer attentional LSTM Skip connections are also used between adjacent LSTM layers. We apply annealing and free-bits techniques following (Li et al., 2019) to the KL term on prototype variable, As in Section 4.3, here we show more generated examples through interpolation on MSCOCO dataset. Table 6: Qualitative examples from the MSCOCO dataset on interpolated sentence generation given the prototype.

Breakthroughs, discoveries, and DIY tips sent every weekday. So although we theoretically know the rules, plenty of passengers have a story of a Transportation Security Administration (TSA) officer spotting something mortifying in their carry-on. The snafus range from embarrassing--a buzzing vibrator that sounds like a potential bomb threat--to stressful--cannabis products purchased legally, then accidentally carried into a state or country where they carry criminal charges. Some of these prohibited items seem pretty obvious, but others beg the question, How the heck did they know that was in there? If you've ever caught a glimpse of the squiggly, multicolored visual display on the X-ray scanner as you trudge to the body scanner, you may have found yourself wondering exactly how much information the Transportation Security Officers (TSOs) can decipher from it.

A couple of years ago, my wife bought my then four-year-old son a supercool set of wooden ramps, which could be combined with our furniture to create courses through which little balls could run. Building the first course was easy, but, as our ambitions grew, the difficulty level rose. Could we make the balls turn corners? What about generating enough momentum for them to go briefly uphill? Two or three times a month for the next two years, we tweaked our techniques or incorporated new elements into complicated routes.

The Canadian air travel sector has seen a significant increase in flight delays, cancellations, and other issues concerning passenger rights. Recognizing this demand, we present a chatbot to assist passengers and educate them about their rights. Our system breaks a complex user input into simple queries which are used to retrieve information from a collection of documents detailing air travel regulations. The most relevant passages from these documents are presented along with links to the original documents and the generated queries, enabling users to dissect and leverage the information for their unique circumstances. The system successfully overcomes two predominant challenges: understanding complex user inputs, and delivering accurate answers, free of hallucinations, that passengers can rely on for making informed decisions. A user study comparing the chatbot to a Google search demonstrated the chatbot's usefulness and ease of use. Beyond the primary goal of providing accurate and timely information to air passengers regarding their rights, we hope that this system will also enable further research exploring the tradeoff between the user-friendly conversational interface of chatbots and the accuracy of retrieval systems.

Instruction tuning large language models (LLMs) using machine-generated instruction-following data has been shown to improve zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. We present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and an LLM for generalpurpose visual and language understanding. To facilitate future research on visual instruction following, we construct two evaluation benchmarks with diverse and challenging application-oriented tasks. Our experiments show that LLaVA demonstrates impressive multimodal chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make GPT-4 generated visual instruction tuning data, our model, and code publicly available.