Video description of algorithm knowledge points that programmers must master

With the popularity of ChatGPT, people have become extremely interested in the development of the field of artificial intelligence. Many experts believe that an era of artificial intelligence will come with the rapid development of software and hardware technology. Then, as a pioneer in the field of information technology, learning artificial intelligence technology has become an inevitable topic for programmers.

Generally speaking, artificial intelligence can be divided into three research directions: computational intelligence, perceptual intelligence and cognitive intelligence.

Computational intelligence is the routine operations of computers that people are familiar with, such as numerical operations, matrix decomposition, calculus calculations, etc.

Perceptual intelligence refers to mapping signals from the physical world to the digital world through cameras, microphones or other sensor hardware devices, with the help of cutting-edge technologies such as speech recognition and image recognition, and then further improving this digital information to a level that can be Levels of cognition, such as memory, understanding, planning, decision-making, etc.

Cognitive intelligence is more similar to human thinking understanding, knowledge sharing, action collaboration or gaming, which means thinking and decision-making based on acquired information. This stage requires the use of computational intelligence, perceptual intelligence, data cleaning, image recognition and other capabilities. In addition, you also need to have an understanding of business needs and the ability to coordinate and manage dispersed data and knowledge, so that you can build strategies and make decisions based on business scenarios.

Currently, a large amount of artificial intelligence work is concentrated in the perceptual intelligence stage. For cognitive intelligence, progress is relatively slow.

In the field of cognitive intelligence, the technology closest to people’s lives is video description technology. Through video classification, object detection and other technologies in perceptual intelligence technology, we can identify what objects appear in the video. But this does not allow people to understand what the video describes. It can only mechanically describe a red-faced man, a knife and a red horse.

Video description requires identifying the objects in the video, understanding the relationships between the objects, and at the same time understanding the differences in scenes, object movements and behaviors, and combining the corresponding stored knowledge to make a description that meets the implementation . This all brings great technical challenges. It is a comprehensive technology that integrates computer vision and natural language processing, similar to translating a video into a sentence. It is not only necessary to correctly understand the video content, but also to use natural language to express the relationship between the objects in the video.

Current video content description algorithms are mainly divided into language template-based methods, retrieval-based methods and basic encoder-decoder methods. Let’s introduce them separately below.

1. Method based on language template

The method based on language template first detects the targets, attributes, actions and relationships between targets in the video through methods such as video classification or target detection. Then the detected objects are filled into the pre-determined language template according to certain rules to form a complete description sentence.

The method based on language templates is simple and intuitive, but due to the limitations of fixed templates, the generated sentences have a single grammatical structure and lack flexibility in expression forms. At the same time, this method must carry out detailed annotation work in the early stage and formulate unified category labels for each object, action, attribute, etc. contained in the video. Moreover, this method will give very different results for videos outside the template range.

2. Retrieval-based method

Retrieval-based method first needs to establish a database, and each video in the database There are corresponding statement description labels. Enter the video to be described, and then find the most similar videos in the database. After summarizing and resetting, the description sentences corresponding to the similar videos are migrated to the video to be described.

Generally speaking, the description sentences generated by the retrieval method are closer to the expression form of human natural language, and the sentence structure is more flexible. However, this method relies heavily on the size of the database. When there is a lack of videos similar to the video to be described in the database, the generated description sentence will have a large error with the video content. Both of the above methods rely heavily on complex visual processing in the early stage, and there is a problem of insufficient optimization of the language model for later generated sentences. For video description problems, both types of methods are difficult to generate high-quality sentences with accurate descriptions and diverse expressions.

3. Encoder-decoder-based method

The codec-based method is currently the mainstream method in the field of video description. This mainly benefits from the breakthrough progress made in the field of machine translation by encoding and decoding models based on deep neural networks.

The basic idea of ​​machine translation is: represent the input source sentence and target sentence in the same vector space, first use the encoder to encode the source sentence into an intermediate vector, and then use the decoder to decode the intermediate vector is the target statement.

The video description problem can essentially be regarded as a "translation" problem, that is, translating the video into natural language. This method does not require complex processing of videos in the early stage. It can directly learn the mapping relationship between videos and description languages ​​from a large amount of training data, achieve end-to-end training, and produce videos with more precise content, flexible grammar and diverse forms. describe.

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