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Context: Settings. To evaluate the controllability of proposed framework, we followed the conventions of prior CIC works [16, 10, 78], and utilized the VSR aligned with ground truth captions as the control signals. Specifically, we compared the proposed framework with several carefully designed baselines666All baselines use the same visual regions as models with VSRs.: 1) C-LSTM: It is a Controllable LSTM model [63]. Given the features of all grounded visual regions, it first averages all region features, and then uses an LSTM to generate the captions. 2) C-UpDn: It is a Controllable UpDn model [3], which uses an adaptive attention to generate the captions. 3) SCT [16]: It regards the set of visual regions as a control signal, and utilizes a chunk-shift captioning model to generate the captions. 4) Ours w/o verb: We ablate our model by removing the verb information in both the SSP and captioning model. 5) Ours (oracle verb): It is an ideal situation, where the captioning model directly outputs the oracle format of the verb when the attending role is the verb. Quantitative Results. The quantitative results are reported in Table 1. From Table 1, we can observe that our framework can achieve the best performance over almost all metrics and benchmarks. By comparing the two different proposal settings (i.e., GSRL and GT), we can find that the accuracy of GSRL is a major bottleneck of the whole framework. Meanwhile, the ablative model (Ours w/o verb) can only achieve slightly better performance than baseline SCT and much worse performance than our full model, which reflects the importance of the verb in semantic structure learning and caption generation. Visualizations. In Figure 5, we illustrate some examples of the generated captions. We can observe that our framework always learns a human-like semantic structure based on the VSR and grounded visual regions (e.g., Arg1thing – sit – Arg2position – LOC – MNR). According to the semantic structures, the captioning model can generate near-perfect descriptions. As a by-product, a well-trained SSP can automatically produce several verb-specific semantic structures for a set of user-interested roles, and we show some examples in Figure 6. For each verb and role set, we illustrate the top two structures by using beam search. Particularly, we are surprised to find that we can even learn some structures that never appear in original datasets (the blue tick ones).

Question: How do the authors verify that the two characteristics mentioned in the sentence are indispensable for the ideal control signal?
Authors verify their work using a conventions evaluation metrics in prior CIC works. As their quantitative results report in Table 1, you can observe that author's framework can achieve the best performance over almost all metrics and benchmarks. and as for the visualized evaluation, you can observe in Figure 5 that the author's framework always learns a human-like semantic structure based on the VSR and grounded visual regions. and according to the semantic structures, the captioning model can generate near-perfect descriptions.