The substantial amount of freely available video material has brought up the need for automatic methods to summarize and compactly represent the essential content. One approach would be to produce a short video skim containing the most important video segments as proposed in the video summarization task BIBREF0. Alternatively, the video content could be described using natural language sentences. Such an approach can lead to a very compact and intuitive representation and is typically referred to as video captioning in the literature BIBREF1. However, producing a single description for an entire video might be impractical for long unconstrained footage. Instead, dense video captioning BIBREF2 aims, first, at temporally localizing events and, then, at producing natural language description for each of them. Fig. FIGREF1 illustrates dense video captions for an example video sequence.Most recent works in dense video captioning formulate the captioning problem as a machine translation task, where the input is a set of features extracted from the video stream and the output is a natural language sentence. Thus, the captioning methods can be leveraged by recent developments in machine translation field, such as Transformer model BIBREF3. The main idea in the transformer is to utilise self-attention mechanism to model long-term dependencies in a sequence. We follow the recent work BIBREF4 and adopt the transformer architecture in our dense video captioning model.The vast majority of previous works are generating captions purely based on visual information BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10. However, almost all videos include an audio track, which could provide vital cues for video understanding. In particular, what is being said by people in the video, might make a crucial difference to the content description. For instance, in a scene when someone knocks the door from an opposite side, we only see the door but the audio helps us to understand that somebody is behind it and wants to enter. Therefore, it is impossible for a model to make a useful caption for it. Also, other types of videos as instruction videos, sport videos, or video lectures could be challenging for a captioning model.In contrast, we build our model to utilize video frames, raw audio signal, and the speech content in the caption generation process. To this end, we deploy automatic speech recognition (ASR) system BIBREF11 to extract time-aligned captions of what is being said (similar to subtitles) and employ it alongside with video and audio representations in the transformer model.The proposed model is assessed using the challenging ActivityNet Captions BIBREF2 benchmark dataset, where we obtain competitive results to the current state-of-the-art. The subsequent ablation studies indicate a substantial contribution from audio and speech signals. Moreover, we retrieve and perform breakdown analysis by utilizing previously unused video category tags provided with the original YouTube videos BIBREF12. The program code of our model and the evaluation approach will be made publicly available.Early works in video captioning applied rule-based models BIBREF13, BIBREF14, BIBREF15, where the idea was to identify a set of video objects and use them to fill predefined templates to generate a sentence. Later, the need for sentence templates was omitted by casting the captioning problem as a machine translation task BIBREF16. Following the success of neural models in translation systems BIBREF17, similar methods became widely popular in video captioning BIBREF18, BIBREF19, BIBREF20, BIBREF1, BIBREF21, BIBREF22, BIBREF23, BIBREF24, BIBREF25. The rationale behind this approach is to train two Recurrent Neural Networks (RNNs) in an encoder-decoder fashion. Specifically, an encoder inputs a set of video features, accumulates its hidden state, which is passed to a decoder for producing a caption.To further improve the performance of the captioning model, several methods have been proposed, including shared memory between visual and textual domains BIBREF26, BIBREF27, spatial and temporal attention BIBREF28, reinforcement learning BIBREF29, semantic tags BIBREF30, BIBREF31, other modalities BIBREF32, BIBREF33, BIBREF34, BIBREF35, and by producing a paragraph instead of one sentence BIBREF36, BIBREF1.Inspired by the idea of the dense image captioning task BIBREF37, Krishna BIBREF2 introduced a problem of dense video captioning and released a new dataset called ActivityNet Captions which leveraged the research in the field BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF38, BIBREF10. In particular, BIBREF5 adopted the idea of the context-awareness BIBREF2 and generalized the temporal event proposal module to utilize both past and future contexts as well as an attentive fusion to differentiate captions from highly overlapping events. Meanwhile, the concept of Single Shot Detector (SSD) BIBREF39 was also used to generate event proposals and reward maximization for better captioning in BIBREF6.In order to mitigate the intrinsic difficulties of RNNs to model long-term dependencies in a sequence, Zhou BIBREF4 tailored the recent idea of Transformer BIBREF3 for dense video captioning. In BIBREF7 the authors noticed that the captioning may benefit from interactions between objects in a video and developed recurrent higher-order interaction module to model these interactions. Xiong BIBREF8 noticed that many previous models produced redundant captions, and proposed to generate captions in a progressive manner, conditioned on the previous caption while applying paragraph- and sentence-level rewards. Similarly, a “bird-view” correction and two-level reward maximization for a more coherent story-telling have been employed in BIBREF9.Since the human annotation of a video with temporal boundaries and captions for each of them can be laborious, several attempts have been made to address this issue BIBREF40, BIBREF41. Specifically, BIBREF40 employed the idea of cycle-consistency to translate a set of captions to a set of temporal events without any paired annotation, while BIBREF41 automatically-collected dataset of an unparalleled-scale exploiting the structure of instructional videos.The most similar work to our captioning model is BIBREF4 that also utilizes a version of the Transformer BIBREF3 architecture. However, their model is designed solely for visual features. Instead, we believe that dense video captioning may benefit from information from other modalities.A few attempts has been made to include additional cues like audio and speech BIBREF38, BIBREF42, BIBREF43 for dense video captioning task. Rahman BIBREF38 utilized the idea of cycle-consistency BIBREF40 to build a model with visual and audio inputs. However, due to weak supervision, the system did not reach high performance. Hessel BIBREF42 and Shi BIBREF43 employ a transformer architecture BIBREF3 to encode both video frames and speech segments to generate captions for instructional (cooking) videos. Yet, the high results on a dataset which is restricted to instructional video appear to be not evidential as the speech and the captions are already very close to each other in such videos BIBREF41.In contrast to the mentioned multi-modal dense video captioning methods: (1) we present the importance of the speech and audio modalities on a domain-free dataset, (2) propose a multi-modal dense video captioning module (MDVC) which can be scaled to any number of modalities.In this section, we briefly outline the workflow of our method referred to as Multi-modal Dense Video Captioning (MDVC) which is shown in Fig. FIGREF5. The goal of our method is to temporally localize events on a video and to produce a textual description for each of them. To this end, we apply a two-stage approach.Firstly, we obtain the temporal event locations. For this task, we employ the Bidirectional Single-Stream Temporal action proposals network (Bi-SST) proposed in BIBREF5. Bi-SST applies 3D Convolution network (C3D) BIBREF44 to video frames and extracts features that are passed to subsequent bi-directional LSTM BIBREF45 network. The LSTM accumulates visual cues over time and predicts confidence scores for each location to be start/end point of an event. Finally, a set of event proposals (start/end times) is obtained and passed to the second stage for caption generation.Secondly, we generate the captions given a proposal. To produce inputs from audio, visual, and speech modalities, we use Inflated 3D convolutions (I3D) BIBREF46 for visual and VGGish network BIBREF47 for audio modalities. For speech representation as a text, we employ an external ASR system BIBREF11. To represent the text into a numerical form, we use a similar text embedding which is used for caption encoding. The features are, then, fed to individual transformer models along with the words of a caption from the previous time steps. The output of the transformer is passed into a generator which fuses the outputs from all modalities and estimates a probability distribution over the word vocabulary. After sampling the next word, the process is repeated until a special end token is obtained. Fig. FIGREF1 illustrates an example modality and the corresponding event captions.An event localization module is dedicated to generating a set of temporal regions which might contain an event. To achieve this, we employ pre-trained Bidirectional Single-Stream Temporal action proposals network (Bi-SST) proposed in BIBREF5 as it has is been shown to reach good performance in the proposal generation task.Bi-SST inputs a sequence of $T$ RGB frames from a video $V = (x_1, x_2, \dots , x_F)$ and extracts a set of 4096-d features $V^{\prime } = (f_1, f_2, \dots , f_T)$ by applying a 3D Convolution network (C3D) on non-overlapping segments of size 16 with a stride of 64 frames. To reduce the feature dimension, only 500 principal components were selected using PCA.To account for the video context, events are proposed during forward and backward passes on a video sequence $V^{\prime }$, and, then, the resulting scores are fused together to obtain the final proposal set. Specifically, during the forward pass, LSTM is used to accumulate the visual clues from the “past” context at each position $t$ which is treated as an ending point and produce confidence scores for each proposal.Afterwards, a similar procedure is performed during the backward pass where the features $V^{\prime }$ are used in a reversed order. This empowers the model to have a sense of the “future” context in a video. In contrast to the forward pass, each position is treated as a starting point of the proposal. Finally, the confidence scores from both passes are fused by multiplication of corresponding scores for each proposal at each time step, and, then, filtered according to a predefined threshold.Finally, we obtain a set of $N_V$ event proposals for caption generation $P_V=\lbrace p_j = (\text{start}_j, \text{end}_j, \text{score}_j)\rbrace _{j=1}^{N_V}$.In this section we explain the captioning based for an example modality, namely, visual. Given a video $V$ and a set of proposals $P_V$ from the event localization module, the task of the captioning module is to provide a caption for each proposal in $P_V$. In order to extract features from a video $V$, we employ I3D network BIBREF46 pre-trained on the Kinetics dataset which produces 1024-d features. The gap between the extracted features and the generated captions is filled with Transformer BIBREF3 architecture which was proven to effectively encode and decode the information in a sequence-to-sequence setting.As shown in Fig. FIGREF6, Feature Transformer architecture mainly consists of three blocks: an encoder, decoder, and generator. The encoder inputs a set of extracted features $ \mathbf {v}^j = (v_1, v_2, \dots , v_{T_j}) $ temporally corresponding to a proposal $p_j$ from $P_V$ and maps it to a sequence of internal representations $ \mathbf {z}^j = (z_1, z_2, \dots , z_{T_j}) $. The decoder is conditioned on the output of the encoder $\mathbf {z}^j$ and the embedding $ \mathbf {e}^j_{\leqslant t} = (e_1, e_2, \dots , e_t)$ of the words in a caption $ \mathbf {w}^j_{\leqslant t} = (w_1, w_2, \dots , w_t) $. It produces the representation $ \mathbf {g}^j_{\leqslant t} = (g_1, g_2, \dots , g_t) $ which, in turn, is used by the generator to model a distribution over a vocabulary for the next word $ p(w_{t+1}|\mathbf {g}^j_{\leqslant t}) $. The next word is selected greedily by obtaining the word with the highest probability until a special ending token is sampled. The captioning is initialized with a starting token. Both are added to the vocabulary.Before providing an overview of the encoder, decoder, and generator, we presenting the notion of multi-headed attention that acts as an essential part of the decoder and encoder blocks. The concept of the multi-head attention, in turn, heavily relies on dot-product attention which we describe next.The idea of the multi-headed attention rests on the scaled dot-product attention which calculates the weighted sum of values. The weights are obtained by applying the softmax function on the dot-product of each pair of rows of queries and keys scaled by $\frac{1}{\sqrt{D_k}}$. The scaling is done to prevent the softmax function from being in the small gradient regions BIBREF3. Formally the scaled dot-product attention can be represented as followswhere $Q, K, V $ are queries, keys, and values, respectively.The multi-headed attention block is used once in each encoder layer and twice in each decoder layer. The block consists of $H$ heads that allows to cooperatively account for information from several representations sub-spaces at every position while preserving the same computation complexity BIBREF3. In a transformer with dimension $D_T$, each head is defined in the following waywhere $q, k, v$ are matrices which have $D_T$ columns and the number of rows depending on the position of the multi-headed block, yet with the same number of rows for $k$ and $v$ to make the calculation in (DISPLAY_FORM11) to be feasible. The $W^{q}_h, W^{k}_h, W^{v}_h \in \mathbb {R}^{D_T \times D_k}$ are trainable projection matrices that map $q, k , v$ from $D_T$ into $D_k= \frac{D_T}{H}$, asserting $D_T$ is a multiple of $H$. The multi-head attention, in turn, is the concatenation of all attention heads mapped back into $D_T$ by trainable parameter matrix $W^o \in \mathbb {R}^{D_k \cdot H \times D_T}$:The encoder consists of $ L $ layers. The first layer inputs a set of features $ \mathbf {v}^j $ and outputs an internal representation $ \mathbf {z}_1^j \in \mathbb {R}^{T_j \times D_T} $ while each of the next layers treats the output of a previous layer as its input. Each encoder layer $l$ consist of two sub-layers: multi-headed attention and position-wise fully connected network which are explained later in this section. The input to both sub-layers are normalized using layer normalization BIBREF48, each sub-layer is surrounded by a residual connection BIBREF49 (see Fig. FIGREF6). Formally, the $l$-th encoder layer has the following definitionwhere $\text{FCN}$ is the position-wise fully connected network. Note, the multi-headed attention has identical queries, keys, and values ($ \overline{\mathbf {z}}_l^j $). Such multi-headed attention block is also referred to as self-multi-headed attention. It enables an encoder layer $l$ to account for the information from all states from the previous layer $ \mathbf {z}_{l-1}^j$. This property contrasts with the idea of RNN which accumulates only the information from the past positions.Similarly to the encoder, the decoder has $ L $ layers. At a position $t$, the decoder inputs a set of embedded words $\mathbf {e}^j_{\leqslant t}$ with the output of the encoder $\mathbf {z}^j$ and sends the output to the next layer which is conditioned on this output and, again, the encoder output $\mathbf {z}^j$. Eventually, the decoder producing its internal representation $\mathbf {g}_{\leqslant t}^j \in \mathbb {R}^{t \times D_T}$. The decoder block is similar to the encoder but has an additional sub-layer that applies multi-headed attention on the encoder output and the output of its previous sub-layer. The decoder employs the layer normalization and residual connections at all three sub-layers in the same fashion as the encoder. Specifically, the $l$-th decoder layer has the following form:where $ \mathbf {z}^j $ is the encoder output. Note, similarly to the encoder, (DISPLAY_FORM18) is a self-multi-headed attention function while the second multi-headed attention block attends on both the encoder and decoder and is also referred to as encoder-decoder attention. This block enables each layer of the decoder to attend all state of the encoder's output $ \mathbf {z}^j$.The fully connected network is used in each layer of the encoder and the decoder. It is a simple two-layer neural network that inputs $x$ with the output of the multi-head attention block, and, then, projects each row (or position) of the input $x$ from $D_T$ space onto $D_P$, $(D_P > D_T)$ and back, formally:where $W_1 \in \mathbb {R}^{D_T \times D_P}$, $W_2 \in \mathbb {R}^{D_P \times D_T}$, and biases $b_1, b_2$ are trainable parameters, $\text{ReLU}$ is a rectified linear unit.At the position $t$, the generator consumes the output of the decoder $\mathbf {g}^j_{\leqslant t}$ and produces a distribution over the vocabulary of words $p(w_{t+1}| \mathbf {g}^j_{\leqslant t})$. To obtain the distribution, the generator applies the softmax function of the output of a fully connected layer with a weight matrix $W_G \in \mathbb {R}^{D_T \times D_V}$ where $D_V$ is a vocabulary size. The word with the highest probability is selected as the next one.Since the representation of textual data is usually sparse due to a large vocabulary, the dimension of the input of a neural language model is reduced with an embedding into a dimension of a different size, namely $D_T$. Also, following BIBREF3, we multiply the embedding weights by $\sqrt{D_T}$. The position encoding is required to allow the transformer to have a sense of the order in an input sequence. We adopt the approach proposed for a transformer architecture, i. e. we add the output of the combination of sine and cosine functions to the embedded input sequence BIBREF3.In this section, we present the multi-modal dense video captioning module which, utilises visual, audio, and speech modalities. See Fig. FIGREF6 for a schematic representation of the module.For the sake of speech representation $\mathbf {s}^j = (s_1, s_2, \dots , s_{T_j^s})$, we use the text embedding of size 512-d that is similar to the one which is employed in the embedding of a caption $\mathbf {w}^j_{\leqslant t}$. To account for the audio information, given a proposal $p_j$ we extract a set of features $\mathbf {a}_j = (a_1, a_2, \dots , a_{T_j^a})$ applying the 128-d embedding layer of the pre-trained VGGish network BIBREF47 on an audio track. While the visual features $\mathbf {v}^j = (v_1, v_2, \dots v_{T_j^v}) $ are encoded with 1024-d vectors by Inflated 3D (I3D) convolutional network BIBREF46.To fuse the features, we create an encoder and a decoder for each modality with dimensions corresponding to the size of the extracted features. The outputs from all decoders are fused inside of the generator, and the distribution of a next word $w_{t+1}$ is formed.In our experimentation, we found that a simple two-layer fully-connected network applied of a matrix of concatenated features performs the best with the ReLU activation after the first layer and the softmax after the second one. Each layer of the network has a matrix of trainable weights: $W_{F_1} \in \mathbb {R}^{D_F \times D_V}$ and $W_{F_2} \in \mathbb {R}^{D_V \times D_V}$ with $D_F = 512 + 128 + 1024 $ and $D_V$ is a vocabulary size.As the training is conducted using mini-batches of size 28, the features in one modality must be of the same length so the features could be stacked into a tensor. In this regard, we pad the features and the embedded captions to match the size of the longest sample.The model is trained by optimizing the Kullback–Leibler divergence loss which measures the “distance” between the ground truth and predicted distributions and averages the values for all words in a batch ignoring the masked tokens.Since many words in the English language may have several synonyms or human annotation may contain mistakes, we undergo the model to be less certain about the predictions and apply Label Smoothing BIBREF50 with the smoothing parameter $\gamma $ on the ground truth labels to mitigate this. In particular, the ground truth distribution over the vocabulary of size $D_V$, which is usually represented as one-hot encoding vector, the identity is replaced with probability $1-\gamma $ while the rest of the values are filled with $\frac{\gamma }{D_V-1}$.During training, we exploit the teacher forcing technique which uses the ground truth sequence up to position $t$ as the input to predict the next word instead of using the sequence of predictions. As we input the whole ground truth sequence at once and predicting the next words at each position, we need to prevent the transformer from peeping for the information from the next positions as it attends to all positions of the input. To mitigate this, we apply masking inside of the self-multi-headed attention block in the decoder for each position higher than $t-1$, following BIBREF3.The details on the feature extraction and other implementation details are available in the supplementary materials.We perform our experiments using ActivityNet Captions dataset BIBREF2 that is considered as the standard benchmark for dense video captioning task. The dataset contains approximately 20k videos from YouTube and split into 50/25/25 % parts for training, validation, and testing, respectively. Each video, on average, contains 3.65 temporally localized captions, around 13.65 words each, and two minutes long. In addition, each video in the validation set is annotated twice by different annotators. We report all results using the validation set (no ground truth is provided for the test set).The dataset itself is distributed as a collection of links to YouTube videos, some of which are no longer available. Authors provide pre-computed C3D features and frames at 5fps, but these are not suitable for our experiments. At the time of writing, we found 9,167 (out of 10,009) training and 4,483 (out of 4,917) validation videos which is, roughly, 91 % of the dataset. Out of these 2,798 training and 1,374 validation videos (approx. 28 %) contain at least one speech segment. The speech content was obtained from the closed captions (CC) provided by the YouTube ASR system which can be though as subtitles.We are evaluating the performance of our model using BLEU@N BIBREF51 and METEOR BIBREF52. We regard the METEOR as our primary metric as it has been shown to be highly correlated with human judgement in a situation with a limited number of references (only one, in our case).We employ the official evaluation script provided in BIBREF53. Thus, the metrics are calculated if a proposed event and a ground truth location of a caption overlaps more than a specified temporal Intersection over Union (tIoU) and zero otherwise. All metric values are averaged for every video, and, then, for every threshold tIoU in $[0.3, 0.5, 0.7, 0.9]$. On the validation, we average the resulting scores for both validation sets. For the learned proposal setting, we report our results on at most 100 proposals per video.Notably, up to early 2017, the evaluation code had an issue which previously overestimated the performance of the algorithms in the learned proposal setting BIBREF9. Therefore, we report the results using the new evaluation code.We compare our method with five related approaches, namely Krishna BIBREF2, Wang BIBREF5, Zhou BIBREF4, Li BIBREF6, and Rahman BIBREF38. We take the performance values from the original papers, except for BIBREF6, and BIBREF4, which are taken from BIBREF9 due to the evaluation issue (see Sec. SECREF27).The lack of access to the full ActivityNet Captions dataset makes strictly fair comparison difficult as we have less training and validation videos. Nevertheless, we present our results in two set-ups: 1) full validation set with random input features for missing entries, and 2) videos with all three modalities present (video, audio, and speech). The first one is chosen to indicate the lower bound of our performance with the full dataset. Whereas, the second one (referred to as “no missings”) concentrates on the multi-modal setup, which is the main contribution of our work.The obtained results are presented in Tab. TABREF25. Our method (MDVC) achieves comparable or better performance, even though we have access to smaller training set and 9 % of the validation videos are missing (replaced with random input features). Furthermore, if all three modalities are present, our method outperforms all baseline approaches in the case of both GT and learned proposals. Notably, we outperform BIBREF4 which is also based on the transformer architecture and account for the optical flow. This shows the superior performance of our captioning module which, yet, trained on the smaller amount of data.In this section, we perform an ablation analysis highlighting the effect of different design choices of our method. For all experiments, we use the full unfiltered ActivityNet Captions validation set with ground truth event proposals.Firstly, we assess the selection of the model architecture. To this end, we implemented a version of our method where the transformer was replaced by Bidirectional Recurrent Neural Network with Gated Recurrent Units with attention (Bi-GRU), proposed in BIBREF54. To distil the effect of the change in architecture, the results are shown for visual-only models. Both Bi-GRU and the transformer input I3D features extracted from 64 RGB and optical flow frames (the final model inputs 24 frames). Finally, we set a lower bound for the feature performance by training a transformer model with random video features. Tab. TABREF32 shows the comparison. To conclude, we observe that the feature transformer-based model is not only lighter but also achieves better performance in dense video captioning task. Moreover, both method clearly surpasses the random baseline.Secondly, we evaluate the contribution of different modalities in our framework. Tab. TABREF33 contains the results for different modality configurations as well as for two feature fusion approaches. Specifically, averaging of the output probabilities and concatenation of the outputs of all modalities and applying two fully connected (FC) layers on top. We observe that audio-only model has the worst performance, followed by the visual only model, and the combination of these two. Moreover, the concatenation and FC layers result in better performance than averaging. To further assess if the performance gain is due to the additional modalities or to the extra capacity in the FC layers, we trained a visual-only model with two additional FC layers. The results indicate that such configuration performs worse than any bi-modal setup. Overall, we conclude that the final model with all three modalities performs best among all tested set-ups, which highlights the importance of multi-modal setting in dense video captioning task.Fig. FIGREF29 shows a qualitative comparison between different models in our ablation study. Moreover, we provide the corresponding captions from the best performing baseline method (Zhuo BIBREF4). We noticed the following pattern: the audio-modality produces coherent sentences and captures the concepts of speaking in the video. However, there are clear mistakes in the caption content. In contrast, the model with all three modalities manages to capture the man who speaks to the camera which is also present in the ground truth. Both visual-only MDVC and Zhuo struggle to describe the audio details.Finally, to test whether our model improves the performance in general rather than in a specific video category, we report the comparison of the different versions of MDVC per category. To this end, we retrieve the category labels from the YouTubeAPI BIBREF12 (US region) for every available ActivityNet Captions validation video. These labels are given by the user when uploading the video and roughly represent the video content type. The comparison is shown in Fig. FIGREF31. The results imply a consistent gain in performance within each category except for categories: “Film & Animation” and “Travel & Events” which might be explained by the lack of correspondence between visual and audio tracks. Specifically, the video might be accompanied by music, e. g. promotion of a resort. Also, “Film & Animation” contains cartoon-like movies which might have a realistic soundtrack while the visual track is goofy.The use of different modalities in computer vision is still an underrepresented topic and, we believe, deserves more attention. In this work, we introduced a multi-modal dense video captioning module (MDVC) and shown the importance of the audio and speech modalities for dense video captioning task. Specifically, MDVC is based on the transformer architecture which encodes the feature representation of each modality for a specific event proposal and produces a caption using the information from these modalities. The experimentation, conducted employing the ActivityNet Captions dataset, shows the superior performance of a captioning module to the visual-only models in the existing literature. Extensive ablation study verifies this conclusion. We believe that our results firmly indicate that future works in video captioning should utilize a multi-modal input.The supplementary material consists of four sections. In Section SECREF35, we provide qualitative results of the MDVC on another example video. The details on features extraction and implementation are described in Section SECREF36 and SECREF38. Finally, the comparison with other methods is shown in Section SECREF39.In Figure FIGREF34, we provide qualitative analysis of captioning on another video from ActivityNet Captions validation set to emphasize the importance of additional modalities for dense video captioning, namely, speech and audio. We compare the captioning proposed by MDVC (our model) conditioned on different sets of modalities: audio-only (A-only), visual-only (V-only), and including all modalities (S + A + V). Additionally, we provide the results of a captioning model proposed in Zhou BIBREF4 (visual only) which showed the most promising results according to METEOR.More precisely, the video (YouTube video id: EGrXaq213Oc) lasts two minutes and contains 12 human annotations. The video is an advertisement for snowboarding lessons for children. It shows examples of children successfully riding a snowboard on a hill and supportive adults that help them to learn. A lady narrates the video and appears in the shot a couple of times.Generally, we may observe that MDVC with the audio modality alone (A-only) mostly describes that a woman is speaking which is correct according to the audio content yet the details about snowboarding and children are missing. This is expectedly challenging for the network as no related sound effects to snowboarding are present. In the meantime, the visual-only MDVC grasps the content well, however, misses important details like the gender of the speaker. While the multi-modal model MDVC borrows the advantages of both which results in more accurate captions. The benefits of several modalities stand out in captions for $p_2$ and $p_{10}$ segments. Note that despite the appearance of the lady in the shot during $p_{10}$, the ground truth caption misses it yet our model manages to grasp it.Yet, some limitations of the final model could be noticed as well. In particular, the content of some proposals is dissimilar to the generated captions, e. g. the color of the jacket ($p_4$, $p_5$), or when a lady is holding a snowboard with a child on it while the model predicts that she is holding a ski ($p_7$). Also, the impressive tricks on a snowboard were guessed simply as “ridding down a hill” which is not completely erroneous but still inaccurate ($p_8$). Overall, the model makes reasonable mistakes except for proposals $p_3$ and $p_4$. Finally, the generated captions provide more general description of a scene compared to the ground truth that is detailed and specific which could be a subject for future investigation.Before training, we pre-calculate the features for both audio and visual modalities. In particular, the audio features were extracted using VGGish BIBREF47 which was trained on AudioSet BIBREF55. The input to the VGGish model is a $96\times 64$ log mel-scaled spectrogram extracted for non-overlapping $0.96$ seconds segments. The log mel-scaled spectrogram is obtained by applying Short-Time Fourier Transform on a 16 kHz mono audio track using a periodic Hann window with 25 ms length with 10 ms overlap. The output is a 128-d feature vector after an activation function and extracted before a classification layer. Therefore, the input to MDVC is a matrix with dimension $T_j^a \times 128$ where $T_j^a$ is the number of features proposal $p_j$ consists of.The visual features were extracted using I3D BIBREF46 network which inputs a set of 24 RGB and optical flow frames extracted at 25 fps. The optical flow is extracted with PWC-Net BIBREF58. First, each frame is resized such that the shortest side is 256 pixels. Then, the center region is cropped to obtain $224\times 224$ frames. Both RGB and flow stacks are passed through the corresponding branch of I3D. The output of each branch are summed together producing 1024-d features for each stack of 24 frames. Hence, the resulting matrix has the shape: $T_j^v\times 1024$, where $T_j^v$ is the number of features required for a proposal $p_j$.We use 24 frames for I3D input to temporally match with the input of the audio modality as $\frac{24}{25} = 0.96$. Also note that I3D was pre-trained on the Kinetics dataset with inputs of 64 frames, while we use 24 frames. This is a valid approach since we employ the output of the second to the last layer after activation and average it on the temporal axis.The input for speech modality is represented by temporally allocated text segments in the English language (one could think of them as subtitles). For a proposal $ p_j $, we pick all segments that both: a) end after the proposal starting point, and b) start before the proposal ending point. This provides us with sufficient coverage of what has been said during the proposal segment. Similarly to captions, each word in a speech segment is represented as a number which corresponds to the word's order number in the vocabulary and then passed through the text embedding of size 512. We omit the subtitles that describe the sound like “[Applause]” and “[Music]” as we are only interested in the effect of the speech. Therefore, the speech transformer encoder inputs matrices of shape: $T^s_j\times 512$ where $T^s_j$ is the number of words in corresponding speech for proposal $p_j$.Since no intermediate layers connecting the features and transformers are used, the dimension of the features transformers $D_T$ corresponds to the size of the extracted features: 512, 128, and 1024 for speech, audio, and visual modalities, respectively. Each feature transformer has one layer ($L$), while the internal layer in the position-wise fully-connected network has $D_P=2048$ units for all modality transformers which was found to perform optimally. We use $H=4$ heads in all multi-headed attention blocks. The captions and speech vocabulary sizes are 10,172 and 23,043, respectively.In all experiments, except for the audio-only model, we use Adam optimizer BIBREF56, a batch containing features for 28 proposals, learning rate $10^{-5}$, $\beta = (0.9, 0.99)$, smoothing parameter $\gamma = 0.7$. In the audio-only model, we apply two-layered transformer architecture with learning rate $10^{-4}$ and $\gamma = 0.2$. To regularize the weights of the model, in every experiment, Dropout BIBREF57 with $p = 0.1$ is applied to the outputs of positional encoding, in every sub-layer before adding a residual, and after the first internal layer of the multi-modal generator.During the experimentation, models were trained for 200 epochs at most and stopped the training early if for 50 consecutive epochs the average METEOR score calculated on ground truth event proposals of both validation sets has not improved. At the end of the training, we employ the best model to estimate its performance on the learned temporal proposals. Usually the training for the best models culminated by 50th epoch, e. g. the final model (MDVC (S + A + V)) was trained for 30 epochs which took, roughly, 15 hours on one consumer-type GPU (Nvidia GeForce RTX 2080 Ti). The code for training heavily relies on PyTorch framework and will be released upon publication.In Table TABREF37, we present a comparison with another body of methods BIBREF8, BIBREF9 which were not included in the main comparison as they were using Reinforcement Learning (RL) approach to directly optimize the non-differentiable metric (METEOR). We believe that our method could also benefit from these as the ablation studies BIBREF8, BIBREF9 show significant gains obtained by applying them. As it was anticipated, in general, methods which employ reinforcement learning perform better in terms of METEOR. Interestingly, our model still outperforms BIBREF8 which uses RL in the captioning module.

Q: How many category tags are considered?
A: 14 categories

Q: What domain does the dataset fall into?
A: YouTube videos

Q: What ASR system do they use?

Q: What is the state of the art?