Visual C 2012 How To Program
Sherry Galeazzi
In computing, a visual programming language (visual programming system, VPL, or, VPS), also known as diagrammatic programming,[1][2] graphical programming or block coding, is a programming language that lets users create programs by manipulating program elements graphically rather than by specifying them textually.[3] A VPL allows programming with visual expressions, spatial arrangements of text and graphic symbols, used either as elements of syntax or secondary notation. For example, many VPLs are based on the idea of "boxes and arrows", where boxes or other screen objects are treated as entities, connected by arrows, lines or arcs which represent relations. VPLs are generally the basis of Low-code development platforms.
VPLs may be further classified, according to the type and extent of visual expression used, into icon-based languages, form-based languages, and diagram languages. Visual programming environments provide graphical or iconic elements which can be manipulated by users in an interactive way according to some specific spatial grammar for program construction.
As of 2005, current developments try to integrate the visual programming approach with dataflow programming languages to either have immediate access to the program state, resulting in online debugging, or automatic program generation and documentation. Dataflow languages also allow automatic parallelization, which is likely to become one of the greatest programming challenges of the future.[5]
The Visual Basic, Visual C#, Visual J# etc. languages of the Microsoft Visual Studio integrated development environment (IDE) are not visual programming languages: the representation of algorithms etc. is textual even though the IDE embellishes the editing and debugging activities with a rich user interface. A similar consideration applies to most other rapid application development environments which typically support a form designer and sometimes also have graphical tools to illustrate (but not define) control flow and data dependencies.
The following list is not mutually exclusive, as some visual programming environments may incorporate elements from multiple paradigms. The choice of visual programming paradigm often depends on the specific requirements of the application or the preferences of the users or the developers.
Most of the VPLs are designed for education or domain-specific usage where is the target users are novice programmers. But there are some research projects try to provide a general-purpose visual programming language that can be used by mainstream programmers in any software project instead of using textual programming languages like (C, C++, Java, etc.).
For example, research projects such as Envision [8][9] and PWCT[10] are designed to achieve this goal. It's common for a VPL to be developed using a textual programming language. Developing general-purpose VPLs allows the other way around. For example, a new textual programming language Compiler and Virtual Machine is developed using visual programming in 2016.[11]
Many modern video games make use of behavior trees, which are in principle a family of simple programming languages designed to model behaviors for non-player characters. The behaviors are modeled as trees, and are often edited in graphical editors.
Solving complex visual tasks such as "Who invented the musical instrument on the right?" involves a composition of skills: understanding space, recognizing instruments, and also retrieving prior knowledge. Recent work shows promise by decomposing such tasks using a large language model (LLM) into an executable program that invokes specialized vision models. However, generated programs are error-prone: they omit necessary steps, include spurious ones, and are unable to recover when the specialized models give incorrect outputs. Moreover, they require loading multiple models, incurring high latency and computation costs.
We propose Visual Program Distillation (VPD), an instruction tuning framework that produces a vision-language model (VLM) capable of solving complex visual tasks with a single forward pass. VPD distills the reasoning ability of LLMs by using them to sample multiple candidate programs, which are then executed and verified to identify a correct one. It translates each correct program into a language description of the reasoning steps, which are then distilled into a VLM.
Extensive experiments show that VPD improves the VLM's ability to count, understand spatial relations, and reason compositionally. Our VPD-trained PaLI-X outperforms all prior VLMs, achieving state-of-the-art performance across complex vision tasks, including MMBench, OK-VQA, A-OKVQA, TallyQA, POPE, and Hateful Memes. An evaluation with human annotators also confirms that VPD improves model response factuality and consistency. Finally, experiments on content moderation demonstrate that VPD is also helpful for adaptation to real-world applications with limited data.
The figure above shows the overall framework of VPD. VPD contains two stages: Program generation and verification and Distilling step-by-step. Program generation and verification contains following steps:
1. Program generation with LLM
2. Program execution with vision modules
3. Program filtering
4. Converting program execution trace into chains-of-thoughts
Given the synthesized CoT data, we fine-tune VLMs to output these chains-of-thoughts, using the same approach as in Distilling step-by-step.
So, how does VPD differ from prior approaches for generating visual instruction tuning data? Here we show examples of how LLaVA and VPD generate data. LLaVA prompts LLM with image captions and let LLM generate task inputs and outputs. However, image captions are coarse representation of images, and does not contain fine-grained attributes and relations. Bounding boxes may complement that, but LLMs are not good at reading bounding boxes, and only densely labeled datasets like COCO can be used. Also, LLM generations suffers from hallucination and spurious reasoning steps. Even GPT-4V is still not reliable enough to generate faithful reasoning steps for complex tasks. In contrast, VPD generates data by sampling programs from LLMs, and then use existing vision tools to get reasoning steps. VPD works on any images, have fine-grained visual details, and contains more factual and consistent reasoning steps.
In generalist setting, our PaLI-X-VPD sets the new state-of-the-art on all benchmarks. PaLI-3-VPD outperforms prior 13B+ VLMs on most benchmarks. Also, VPD variants outperform instruction-tuning baselines. VPD is also helpful for adaptation to real-word tasks with limited data. We experiment with VPD on Hateful Memes dataset. We sets the new SOTA for both supervised and unsupervised settings. Surprisingly, unsupervised PaLI-X-VPD even outperforms strong supervised baselines trained with 8,500 labels.
Here we show some demos of applying VPD on content moderation task Hateful Memes. There are two settings. For the unsupervised settings, we do not use any human labels. For the supervised settings, we use 8,500 "yes" and "no" labels to fine-tune the model. Our models are able to generate human-interpretable reasoning steps, and are able to detect hateful memes. The unsupervised model works surprisingly well. We also show a failure case of the unsuperivsed model.
In generalist setting, our PaLI-X-VPD sets the new state-of-the-art on all benchmarks. PaLI-3-VPD outperforms prior 13B+ VLMs on most benchmarks. Also, VPD variants outperform instruction-tuning baselines. We also see similar results on Hateful memes, in which we sets the new SOTA for both supervised and unsupervised settings. Surprisingly, unsupervised PaLI-X-VPD even outperforms strong supervised baselines trained with 8,500 labels.
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Visual programming is a sort of programming language that allows users to illustrate processes. A visual programming language enables developers to explain the process in words that are understandable to humans, as opposed to a traditional text-based computer language that forces the developer to think like a machine. The size of the gap from visual to conventional programming is determined by the visual programming tool.
Visual Programming Software has several characteristics that have helped it become a popular programming language among developers all over the world. The following are some of these characteristics:
No, the words are identical. A visual programming language (VPL) is a computer program that develops applications using graphical components and figures. A VPL uses methods to create a two- or three-dimensional software that contains graphical components, text, symbols, and icons inside its programming environment. An executable graphics language is another name for a visual programming language.
Visual programming had so much potential, but it fell short of those expectations in its early phases. They remain, nevertheless, more pertinent than ever. Visual programming may never be likely to substitute the better conventional programming languages because real-world issues need more adaptability than visual programming can provide. Hence to tackle the problems that VPL fell short of solving, low-code platforms were created. Low-code platforms, on the other hand, are aiming to simplify programming and make it accessible to citizen developers. We consider that VPL is an integral element of current software development and that it will never go out of style.
You may use Kissflow to execute Agile and DevOps best visual programming practices. It even goes a step further by incorporating shareholders in the design and development process. Kissflow combines no-code and low-code development into one integrated platform:
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