You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Image captioning, \ie, generating fluent and meaningful descriptions to summarize the salient contents of an image, is a classic proxy task for comprehensive scene understanding [21]. With the release of several large scale datasets and advanced encoder-decoder frameworks, current captioning models plausibly have already achieved “super-human” performance in all accuracy-based evaluation metrics. However, many studies have indicated that these models tend to produce generic descriptions, and fail to control the caption generation process as humans, \eg, referring to different contents of interest or descriptive patterns. In order to endow the captioning models with human-like controllability, a recent surge of efforts [16, 10, 19, 78, 48, 77, 27, 20] resort to introducing extra control signals as constraints of the generated captions, called Controllable Image Captioning (CIC). As a byproduct, the CIC models can easily generate diverse descriptions by feeding different control signals. For human-like controllable image captioning, we first propose the Verb-specific Semantic Roles (VSR) as the control signal for generating customized captions. As shown in Figure 3, we formally represent a control signal VSR as:𝒱𝒮ℛ={v,<s1,n1>,…,<sm,nm>},\displaystyle\begin{aligned} \mathcal{VSR}=\{v,<s_{1},n_{1}>,...,<s_{m},n_{m}>\},\\\end{aligned}start_ROW start_CELL caligraphic_V caligraphic_S caligraphic_R = { italic_v , < italic_s start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT , italic_n start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT > , … , < italic_s start_POSTSUBSCRIPT italic_m end_POSTSUBSCRIPT , italic_n start_POSTSUBSCRIPT italic_m end_POSTSUBSCRIPT > } , end_CELL end_ROW(1)where v is a verb capturing the scope of a salient activity in the image (\eg, ride), s_{i} is a semantic role of verb v (\eg, LOC), and n_{i} is the number of interested entities in the role s_{i}. For example, for 𝒱𝒮ℛ={𝚛𝚒𝚍𝚎,<𝙰𝚛𝚐𝟶,𝟷>,<𝙰𝚛𝚐𝟷,𝟷>,<𝙻𝚘𝚌,𝟸>}\mathcal{VSR}=\{\texttt{ride},<\texttt{Arg0},\texttt{1}>,<\texttt{Arg1},\texttt{1}>,<\texttt{Loc},\texttt{2}>\}caligraphic_V caligraphic_S caligraphic_R = { ride , < Arg0 , 1 > , < Arg1 , 1 > , < Loc , 2 > }, we hope to generate a caption which not only focuses on describing the ride activity, but also contains one entity respectively in the role Arg0{}_{\text{rider}} and Arg1{}_{\text{steed}}, and two entities in the role LOC. Thus, VSR can effectively control the amount of information carried in the whole sentence and each role, \ie, the level of details. In this paper, we argued that all existing objective control signals for CIC have overlooked two indispensable characteristics: event-compatible and sample-suitable. To this end, we proposed a novel control signal called VSR. VSR consists of a verb and several semantic roles, \ie, all components are guaranteed to be event-compatible. Meanwhile, VSR only restricts the involved semantic roles, which is also sample-suitable for all the images containing the activity. We have validated the effectiveness of VSR through extensive experiments. Moving forward, we will plan to 1) design a more effective captioning model to benefit more from the VSR signals; 2) extend VSR to other controllable text generation tasks, \eg, video captioning [69]; 3) design a more general framework to cover the images without verbs. In summary, we make three contributions in this paper:1.We propose a new control signal for CIC: Verb-specific Semantic Roles (VSR). To the best of our knowledge, VSR is the first control signal to consider both event-compatible and sample-suitable requirements222When using control signals extracted from GT captions, existing control signals can always meet both requirements and generate reasonable captions. However, in more general settings (\eg, construct control signals without GT captions), the form of VSR is more human-friendly, and it is easier to construct signals which meet both requirements compared with all existing forms of control signals, which is the main advantage of VSR..2.We can learn human-like verb-specific semantic structures automatically, and abundant visualization examples demonstrate that these patterns are reasonable.3.We achieve state-of-the-art controllability on two challenging benchmarks, and generate diverse captions by using different verbs, semantic roles, or structures. Controllable Image Captioning.Compared with conventional image captioning [63, 68, 9, 25, 13], CIC is a more challenging task, which needs to consider extra constraints. Early CIC works are mostly about stylized image captioning, \ie, constraints are the linguistic styles of sentences. According to the requirements of parallel training samples, existing solutions can be divided into two types: models using parallel stylized image-caption data [41, 11, 54, 1] or not [22, 42]. Subsequently, the community gradually shifts the emphasis to controlling described contents [16, 77, 27, 10, 78, 48, 35] or structures [20, 19, 75, 76] of the sentences. In this paper, we propose a novel control signal VSR, which is the first control signal to consider both the event-compatible and sample-suitable requirements.

Question: Who is responsible for designating the control signal?
control signal is designated by each author in their work. as the authors of this paper proposed a "verb-specific semantic role" "VSR" as control signal for customized captions. while a recent surge of efforts by other works introduced extra control signals as constrains of the generated captions [16, 10, 19, 78, 48, 77, 27, 20].