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+# Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
+
+Zhenhailong Wang1∗, Manling $\mathbf { L i } ^ { 1 }$ ∗, Ruochen $\mathbf { X } \mathbf { u } ^ { 2 }$ , Luowei Zhou2†, Jie Lei3, Xudong Lin4, Shuohang $\mathbf { W a n g } ^ { 2 }$ , Ziyi $\mathbf { Y a n g } ^ { 2 }$ , Chenguang $\mathbf { Z } \mathbf { h } \mathbf { u } ^ { 2 }$ , Derek Hoiem1, Shih-Fu Chang4, Mohit Bansal3, Heng Ji1 1UIUC 2MSR 3UNC 4Columbia University {wangz3,hengji}@illinois.edu
+
+# Abstract
+
+The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples. Existing few-shot video-language learners focus exclusively on the encoder, resulting in the absence of a video-totext decoder to handle generative tasks. Video captioners have been pretrained on large-scale video-language datasets, but they rely heavily on finetuning and lack the ability to generate text for unseen tasks in a few-shot setting. We propose VidIL, a few-shot Video-language Learner via Image and Language models, which demonstrates strong performance on few-shot video-to-text tasks without the necessity of pretraining or finetuning on any video datasets. We use image-language models to translate the video content into frame captions, object, attribute, and event phrases, and compose them into a temporal-aware template. We then instruct a language model, with a prompt containing a few in-context examples, to generate a target output from the composed content. The flexibility of prompting allows the model to capture any form of text input, such as automatic speech recognition (ASR) transcripts. Our experiments demonstrate the power of language models in understanding videos on a wide variety of video-language tasks, including video captioning, video question answering, video caption retrieval, and video future event prediction. Especially, on video future event prediction, our few-shot model significantly outperforms state-of-the-art supervised models trained on large-scale video datasets. Code and processed data are publicly available for research purposes at https://github.com/MikeWangWZHL/VidIL.
+
+# 1 Introduction
+
+One major gap between artificial intelligence and human intelligence lies in their abilities to generalize and perform well on new tasks with limited annotations. Recent advances in large-scale pre-trained generative language models [45, 6, 71, 24] have shown promising few-shot capabilities [72, 43, 63] in understanding natural language. However, few-shot video-language understanding is still in its infancy. A particular limitation of most recent video-language frameworks [28, 21, 61, 68, 67, 25, 64, 34] is that they are encoder-only, which means they do not have the ability to generate text from videos for purposes such as captioning [62, 57], question answering [60], and future prediction [23]. Meanwhile, unified video-language models [36, 49] that are capable of language decoding still rely heavily on finetuning using a large number of manually annotated video-text pairs, therefore cannot adapt quickly to unseen tasks. Few-shot video-to-text decoding is challenging because the natural language supervision for learning video-language representation is typically based on subtitles and automatic speech recognition (ASR) transcripts [39, 68], which differ significantly from downstream tasks in terms of distribution and may have poor semantic alignment across vision and text modalities.
+
+We propose to address this problem by harnessing the few-shot power of frozen large-scale language models, such as InstructGPT [40]. Our inspiration is derived from the fact that humans are excellent visual storytellers [15], with the ability to piece together a coherent story from a few isolated images. To mimic this, we propose VidIL, a few-shot Video-language Learner via Image and Language models, to use image models to provide information about the visual content in the video (as well as optionally use ASR to represent speech), and then we instruct language models to generate a video-based summary, answer, or other target output for diverse video-language tasks.
+
+The main challenge of understanding videos is that, videos contain rich semantics and temporal content at multiple granularities. Unlike static images which depict objects, attributes and events in a snapshot, the temporal dimension of videos further conveys the state changes of the objects, actions, and events. For example, in Figure 1, the individual frame captions of the video clip only describe static visual features such as "a person holding a green object in hand". In contrast, a correct video-level description would be "a woman makes realistic looking leaves and flowers for a cake", which involves reasoning over a collection of objects and events that occur at different timestamps in the video clip, such as "cake decorating" and "flowered design". Hence, to inform video-level description and queries, we need to represent all of this information and its temporal ordering.
+
+![](images/b5f930daf9032229a0d0a4e399381948a20cacaccc02903750ded88494988f0e.jpg)  
+Figure 1: Multiple levels of information in videos.
+
+To address the unique challenges of videos, we propose to decompose a video into three levels: the video output, frame captions, and visual tokens (including objects, events, attributes). One major benefit from this hierarchical video representation is that we can separate the visual and temporal dimensions of a video. We leverage frozen image-language foundational models at lower levels to collect salient visual features from the sparsely sampled frames. Specifically, we first leverage a pretrained image-language contrastive model CLIP [44] to perform visual tokenization, based on the similarity score between frames and tokens of objects, events and attributes. The tokenization is done under the guidance of semantics role labeling [14], which provides us with candidate events with involved objects and related attributes. Next, in order to capture the overall semantics at the frame level, we employ the pretrained image captioner in the image-language model BLIP [26] to obtain frame captions. Finally, we instruct a pretrained large language model using in-context learning [40, 13, 51, 48] to interpret visual tokens and frame captions into the target textual output. In detail, we temporally order visual tokens and frame captions using specially designed prompts such as “First...Then...Finally”, to instruct the pretrained language model to track the changes of objects, events, attributes and frame semantics along the temporal dimension.
+
+Without pretraining or finetuning on any video datasets, we show that our approach outperforms both video-language and image-language state-of-the-art baselines on few-shot video captioning and question answering tasks. Moreover, on video-language event prediction, our approach significantly outperforms fully-supervised models while using only 10 labeled examples. We further demonstrate that our generative model can benefit broader video-language understanding tasks, such as text-video retrieval, via pseudo label generation. Additionally, we show that our model is highly flexible in adding new modalities, such as ASR transcripts.
+
+# 2 Related Work
+
+# 2.1 Image-Language Models and Their Applications on Video-Language Tasks
+
+Large-scale image-language pretraining models optimize image-text matching through contrastive learning [44, 17] and multimodal fusion [65, 27, 58, 66, 35, 52, 8, 29, 73, 70, 18, 16]. Recently,
+
+BLIP [26] proposes a bootstrapping image-language pretraining framework with a captioner and a filterer which has shown promising performance on various image-language tasks. However, video-language pretraining [25, 36, 28, 38, 3, 1, 42, 33] is still hindered by noisy and domain-specific video datasets [74, 22, 39]. Naturally, researchers start to explore transferring the rich knowledge from image models to videos. Different from the traditional way of representing videos by 3D dense features [12], recent work [21, 25] proves that sparse sampling is an effective way to represent videos, which facilitates applying pre-trained image-language models to video-language tasks [37, 11]. Specifically, the image-language model BLIP [26] sets new state-of-the-art on zero-shot retrieval-style video-language tasks, such as video retrieval and video question answering. However, for generationstyle tasks such as domain-specific video captioning, video-language model UniVL [36] still leads the performance but highly rely on fine-tuning. In this work, we extend the idea of leveraging image-language models to a wide variety of video-to-text generation tasks. We further connect imagelanguage models with language models which empowers our model with strong generalization ability. We show that the knowledge from both image-language pretraining and language-only pretraining can benefit video-language understanding in various aspects.
+
+# 2.2 Unifying MultiModal Tasks with Language Models
+
+The community has paid much attention to connecting different modalities with a unified representation recently. Text-only generation models, such as T5 [46], have been extended to vision-language tasks by text generation conditioned on visual features [9, 53, 50, 75, 55]. In order to fully leverage the generalization power from pretained language models, [63] represents images using text in a fully symbolic way. [32] includes more modalities such as video and audio, but requires annotated video-text data to jointly training the language model with the video and audio tokenizer. In this work, we propose a temporal-aware hierarchical representation for describing a video textually. To our knowledge, we are the first work to leverage prompting a frozen language model for tackling few-shot video-language tasks with a unified textual representation. Concurrent work Socratic [69] uses a zero-shot language-based world-state history to represent long videos with given time stamps, while our model can quickly adapt to different video and text distributions with few examples. Furthermore, we show that by injecting temporal markers to the prompt we can make a pre-trained language model understand fine-grained temporal dynamics in video events. Compared with the concurrent work Flamingo [2], which requires dedicated vision-language post-pretraining, our framework does not require to pretrain or finetune on any video data. Our framework is simple and highly modulated where all the components are publicly available. Additionally, our framework is more flexible on adding new modalities, e.g., automatic speech recognition, without the need for complex redesigning.
+
+# 3 Method
+
+We propose a hierarchical video representation framework which decomposes a video into three levels, i.e., visual token level, frame level and video level. The motivation is to separate the spatial and temporal dimension of a video in order to leverage image-language and language-only foundation models, such as CLIP [44] and GPT-3 [6]. All three levels use a unified textual representation which enables us to leverage the powerful few-shot ability from pretrained language models.
+
+# 3.1 Frame Level: Image Captioning
+
+Following [21] we first perform sparse sampling to obtain several video frames. Unless otherwise specified, we sample 4 frames for frame level and 8 frames for visual token level. We then feed each frame into a pre-trained image-language model to obtain frame level captions. An example can be found in the blue part of Figure 2. In our experiments, we use BLIP [26], a recent image-language framework containing both image-grounded encoder and decoder, for generating frame captions. We follow [26] to do both captioning and filtering on each frame. However, as mentioned in Section 1, videos contain rich semantics and temporal contents at multiple granularities. It is not enough to generate video-level target text such as video captions solely based on frame captions. Thus, we further perform visual tokenization for each frame to capture features at a finer granularity.
+
+![](images/c144de0cb595e241110b349a7da85b61029ef363498acc4c08b0c7647b08efae.jpg)  
+Figure 2: Overview of VidIL framework. We represent a video in a unified textural representation containing three semantic levels: visual token level, frame level, and video level. At visual token level, we extract salient objects, events, attributes for each sampled frame. At frame level, we perform image captioning and filtering. At video level, we construct video representation by aggregating the visual tokens, frame captions and other text modalities such as ASR, using a few-shot temporalaware prompt. We then feed the prompt to a pre-trained language model together with task-specific instructions to generate target text for a variety of video-language tasks. Examples of the full prompt for different tasks can be found in Appendix ??.
+
+# 3.2 Visual Token Level: Structure-Aware Visual Tokenization
+
+At this level, we aim to extract the textual representations of salient visual token types, such as objects, events and attributes. We found that pre-defined classes for classification, such as those in ImageNet [10], are far from enough for covering the rich semantics in open-domain videos. Thus, instead of using classification-based methods for visual tokenization as in previous work [32, 63], we adopt a retrieval-based visual tokenization approach by leveraging pre-trained contrastive imagelanguage models. Given a visual token vocabulary which contains all candidate object, event, and attribute text phrases, we compute the image embedding of a frame and the text embeddings of the candidate visual tokens using a contrastive multi-modal encoder, CLIP [44]. We then select top 5 visual tokens per frame based on the cosine similarity of the image and text embeddings. An example of the extracted object tokens can be found in the green part of Figure 2.
+
+Unlike in images where objects and attributes already cover most visual features, events are more informative in videos. In order to discover events from video frames, we construct our own event vocabulary by extracting event structures from Visual Genome [19] synsets3 using Semantic Role Labeling. Specifically, we first select the phrases that contain at least one verb and one argument as events. Then we remove highly similar events based on their sentence similarity using SentenceBERT [47] embeddings. For object vocabulary, we adopt OpenImage [20] full classes $( { \sim } 2 0 \mathbf { k } )$ , instead of using the visually groundable subset $( \sim 6 0 0 )$ as in concurrent work [69]. We found that using large but noisy vocabulary is more effective than using small but clean vocabulary in our retrieval-based setting with CLIP. For attribute vocabulary, we adopt visual genome attribute synset. In Section 4.6, we provide ablation study on the impact of different types of visual tokens. The statistics of visual token vocabulary can be found in Appendix Table ??.
+
+![](images/331592c36e0944e6b5f6973236f3089e33d229d668d68f0224a0f67fdb784fba.jpg)  
+Figure 3: Temporal-aware prompt successfully distinguishes the Sunset and Sunrise scenarios based on the temporal ordering change of objects and frame captions, while the static prompt fails.
+
+# 3.3 Video Level: Temporal-Aware Few-shot Prompting
+
+Once we obtain the textual representation from frame level and visual token level, the final step is to put the pieces together to generate a video level target text. The goal is to build a model that can be quickly adapted to any video-to-text generation task with only a few examples. To this end, we propose to leverage large-scale pre-trained language models, such as GPT-3 [6], with a temporal-aware few-shot prompt. As shown in Figure 2, our framework can be readily applied to various video-to-text generation tasks, such as video captioning and video question answering, with a shared prompt template. The proposed prompting strategy enables a language model to attend to the lower level visual information as well as taking into account the temporal ordering.
+
+Here, we use the video captioning task depicted in Figure 2 to illustrate the details. The few-shot prompt consists of three parts: instruction, few-shot context, and task query. The instruction is a concise description of the generation task, e.g., "Generate a video caption based on the objects, events, attributes and frame captions. Example:", which is proved to be effective in zero-shot and few-shot settings [6, 59]. The few-shot context contains the selected in-context examples as well as the test video instance. Each video instance is represented by the aggregated visual tokens4, e.g., "Objects: First, bath toy. Then,..." the frame captions, such as "Frame Captions: First, a toddler playing in a bathtub filled with toys. Then,...", and the ASR inputs if available, e.g., "Subtitle:<ASR Transcript>". Finally, the task query is a task-specific suffix indicating the target text format, e.g. "Video Caption:". For in-context examples (omitted here for simplicity), the task query is followed by ground truth annotation, while for the test instance, the generation starts at the end of the task query.
+
+Formally, we denote the instruction line as $\mathbf { t }$ , few-shot context as c, the task query as q, and the target text as $\mathbf { y }$ , where $\mathbf { y } = ( y _ { 1 } , y _ { 2 } , . . . , y _ { L } )$ . The generation of the next target token $y _ { l }$ can be modeled as:
+
+$$
+y _ { l } = \mathop { \arg \operatorname* { m a x } } _ { y } p ( y | \mathbf { s } , \mathbf { c } , \mathbf { q } , y _ { < l } )
+$$
+
+In order to capture the temporal dynamics between frames and visual tokens, we further propose to inject temporal markers to the prompt. As shown in the few-shot context in Figure 2, each visual token and frame caption is prefixed with a natural language phrase indicating its temporal ordering, e.g., "First,","Then,", and "Finally,". We found adding the temporal marker can make the language model conditioned on not only literal but also temporal information of the context. We show an example in Figure 3, where we compare our temporal-aware prompt with a static prompt on video captioning using InstructGPT. Again, the in-context examples are omitted here, which can be found in Appendix ??. In this example, the only difference between these two contexts is the ordering of the visual tokens and the frame captions. For the context on the left, where "sun moving" appears before "night sky", we are expected to see a story talking about sunset, while for the context on the right, we are expected to see sunrise. We can see the static prompt generates captions about sunset for both contexts, while the temporal-aware prompt can capture temporal ordering correctly and generate sunrise for the context on the right.
+
+# 4 Experiments
+
+# 4.1 Experimental Setup
+
+To comprehensively evaluate our model, we show results on four video-language understanding tasks in few-shot settings: video captioning, video question answering (QA), video-language event prediction, and text-video retrieval. We compare our approach with state-of-the-art approaches on five benchmarks, i.e, MSR-VTT [62], MSVD [7], VaTeX [57], YouCook2 [74], and VLEP [23]. The statistics of the datasets can be found in Table 1. For more details please refer to Appendix ??.
+
+Implementation Details. We use CLIP-L/146 as our default encoder for visual tokenization. We adopt BLIP captioning checkpoint7 finetuned on COCO [31] for frame captioning. We use InstructGPT [40] as our default language model for generating text conditioned on the few-shot prompt. To construct event vocabulary, we use the semantic role labeling model from AllenNLP8. The experiments are conducted on 2 NVIDIA V100 (16GB) GPUs. All few-shot
+
+Table 1: Statistics of datasets in our experiments   
+
+<table><tr><td>Dataset</td><td>Task</td><td>Split Count # train /#eval</td></tr><tr><td>MSR-VTT[62]</td><td>Captioning; QA</td><td>6,513 /2.990</td></tr><tr><td>MSR-VTT[62]</td><td>Retrieval</td><td>7,010 / 1,000</td></tr><tr><td>MSVD [7]</td><td>Question Answering</td><td>30,933 /13,157</td></tr><tr><td>VaTeX v1.15 [57]</td><td>Captioning; Retrieval</td><td>25,991/6.000</td></tr><tr><td>YouCook2[74]</td><td>Captioning</td><td>10,337 /3,492</td></tr><tr><td>VLEP [23]</td><td>Event Prediction</td><td>20,142/4,192</td></tr></table>
+
+finetuning on baselines and semi-supervised training are performed on 2 Nvidia V100 16G GPUs.
+
+In-context Example Selection. From our preliminary experiments, we find that the generation performance is sensitive to the quality of in-context examples. For example, for QA tasks such as MSVD-QA where the annotations are automatically generated, the <question, answer> pair in randomly selected in-context examples can be only weakly-correlated with the video context. Thus, instead of using a fixed prompt for each query, we dynamically filter out the irrelevant in-context examples. Specifically, given a randomly sampled $M .$ -shot support set from the training set, we select a subset of $N _ { ☉ }$ -shots as in-context examples based on their SentenceBERT [47] similarities with text queries. Furthermore, we reorder the selected examples in ascending order based on the similarity score to account for the recency bias [72] in large language models. For QA tasks, we choose the most relevant in-context examples by comparing with questions. While for captioning task, we compare with frame captions. If not otherwise specified, we use $M { = } I O$ and $N { = } 5$ , which we consider as 10-shot training.
+
+# 4.2 Few-shot Video Captioning
+
+We report BLEU-4 [41], ROUGE-L [30], METEOR [5], and CIDEr [54] scores on three video captioning benchmarks covering both open-domain (MSR-VTT, VaTeX) and domain-specific (YouCook2) videos. We compare with both state-of-the-art video captioner (UniVL [36]) and image captioner (BLIP [26]). In order to implement the BLIP baseline for few-shot video captioning, we extend the approach used for text-video retrieval evaluation in [26] to video-language training. Specifically, we concatenate the visual features of sampled frames and then feed them into the image-grounded text-encoder to compute the language modeling loss. This is equivalent to stitching the sampled frames into a large image and then feeding it to BLIP for image captioning. We found that this simple approach results in very strong baselines.
+
+As shown in Table 2, existing methods have strong bias on certain datasets. For example, UniVL performs well on YouCook2 but fails on MSR-VTT and VaTeX, while BLIP performs the opposite. This is because UniVL is pretrained on HowTo100M which favors instructional videos, i.e., YouCook2, while BLIP is pre-training on image-caption pairs which favors description-style captions, i.e., MSR-VTT and VaTeX. On the contrary, our model performs competitively on both open-domain and instructional videos, and significantly outperforms the baselines on the average CIDEr score across all three benchmarks. This indicates that by leveraging language models, we can maintain strong few-shot ability regardless of the video domain or the target caption distribution.
+
+Table 2: 10-shot video captioning results. ♠ indicates concurrent work. The reported Flamingo [2] results are using 16 shots. #VideoPT represents the number of videos used for pre-training. B-4, R-L, $M$ , $C$ represents BLEU-4, ROUGE-L, METEOR and CIDEr. Avg $C$ represents the average CIDEr score across all available benchmarks. ASR indicates whether the model has access to the ASR subtitles. $B L I P$ and $B L I P _ { c a p }$ use the pretrained checkpoint and the finetuned checkpoint on COCO captioning. All results are averaged over three random seeds.   
+
+<table><tr><td rowspan="2">Method</td><td rowspan="2">#VideOpr ASR</td><td rowspan="2"></td><td rowspan="2">B-4R-LM</td><td colspan="3">MSR-VTTCaption</td><td colspan="3">YouCook2 Caption C</td><td rowspan="2"></td><td colspan="3">VaTex Caption</td><td rowspan="2">Avg C</td></tr><tr><td></td><td></td><td>C</td><td>B-4R-LM</td><td></td><td></td><td>B-4 R-L M</td><td></td><td>C</td></tr><tr><td>Few-shot</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>UniVL</td><td>1.2M</td><td>No</td><td>2.1 22.5 9.5</td><td></td><td></td><td>3.6</td><td>3.3</td><td>25.3 11.6</td><td>34.1</td><td>1.7</td><td>15.7</td><td>8.0</td><td>2.1</td><td>13.3</td></tr><tr><td>BLIP</td><td>0</td><td>No</td><td>27.7 43.0 23.0 39.5</td><td></td><td></td><td></td><td>0.7</td><td>9.0 3.4</td><td>11.5</td><td>13.5</td><td>39.5</td><td>15.4</td><td>20.7</td><td>23.9</td></tr><tr><td>BLIPcap</td><td>0</td><td>No</td><td>21.648.022.7 30.2</td><td></td><td></td><td></td><td>3.7</td><td>8.6 3.8</td><td>9.4</td><td>20.7</td><td>41.5</td><td>17.4</td><td>28.9</td><td>22.8</td></tr><tr><td>VidIL(ours)</td><td>0</td><td>No</td><td>26.0 51.7 24.7 36.3</td><td></td><td></td><td></td><td>2.6</td><td>22.9 9.5</td><td>27.0</td><td>22.2</td><td></td><td>43.620.0</td><td>36.7</td><td>33.3</td></tr><tr><td>UniVL</td><td>1.2M</td><td>Yes</td><td>-</td><td></td><td>=</td><td>-</td><td>4.3</td><td>26.4 12.2</td><td>48.6</td><td>2.7</td><td>17.7</td><td>10.2</td><td>3.4</td><td>26.0</td></tr><tr><td>VidIL(ours)</td><td>0</td><td>Yes</td><td></td><td></td><td></td><td></td><td>10.7 35.9</td><td></td><td>19.4 111.6</td><td></td><td>23.2 44.2 20.6 38.9</td><td></td><td></td><td>75.3</td></tr><tr><td>Flamingo-3B(16)</td><td>27M</td><td>No</td><td></td><td></td><td></td><td></td><td></td><td></td><td>73.2</td><td></td><td></td><td></td><td>57.1</td><td></td></tr><tr><td>Flamingo-80B(16) 27M</td><td></td><td>No</td><td></td><td></td><td></td><td></td><td></td><td></td><td>84.2</td><td></td><td></td><td></td><td>62.8</td><td>=</td></tr><tr><td>Fine-tuning</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>UniVL</td><td>1.2M</td><td>No</td><td>42.0 61.0 29.0 50.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td>[11.2 40.1 17.6 127.0|22.8 38.6 22.3 33.4</td><td></td><td></td><td></td><td>70.2</td></tr><tr><td>UniVL</td><td>1.2M</td><td>Yes</td><td>=</td><td></td><td></td><td></td><td></td><td></td><td></td><td>16.6 45.7 21.6176.823.7 39.3 22.7 35.6</td><td></td><td></td><td></td><td>106.2</td></tr></table>
+
+As discussed in Section 1, video captions describe the content in various semantic levels. The N-gram based metric may not fairly reflect the models’ performance in capturing the video-caption alignment. We further verify this hypothesis in Section 4.5. Thus, in addition to automatic metrics, we include qualitative examples illustrated in Figure 4. More examples are in Appendix ??.
+
+Additionally, for most existing methods and also concurrent work, e.g., Flamingo [2], adding a new modality often requires a dedicated model redesign or retraining. However, the nature of our framework, where we use a unified textual representation for each level, makes it highly flexible for incorporating new modalities. As shown in row 6 in Table, our model can effectively utilize extra information from ASR to obtain significantly better few-shot performance on certain datasets such as YouCook2.
+
+![](images/42488e5e146be037d7c57abcc48a78f8af1cf75e6517bc50d3e1b32bfd715b6a.jpg)
+
+Figure 4: Qualitative examples on video captioning. Grey boxes contain part of the video representation from our model. Blue boxes contain caption generation from different models. Green boxes contain ground truth annotations. Bold green text highlights the correct information that is not captured in baseline outputs which can be reasoned from our visual tokens and frame captions.
+
+# 4.3 Few-shot Video Question Answering
+
+We compare the test accuracy of our approach with few-shot pretrained BLIP, $\mathsf { B L I P } _ { V Q A }$ [26], and concurrent work Flamingo [2] on two video question answering benchmarks, MSR-VTT_QA and MSVD_QA. $\mathsf { B L I P } _ { V Q A }$ represents finetuned BLIP on VQA [4] dataset, which is the previous SOTA on zero/few-shot video question answering. In order to have fairer comparison with $\mathsf { B L I P } _ { V Q A }$ , we reduce the shot number to 5 and report the average accuracy on three sets of randomly selected 5-shot examples. As shown in Table 3, our method outperforms previous SOTA by a large margin. Comparing with concurrent work Flamingo, which is post-pretrained on a large number of video-text data, our model is training-free and did not observe any video data. However, with only imagelanguage and language-only knowledge, our 5-shot model is able to outperform 8-shot Flamingo-3B and achieve on-par performance with 4-shot Flamingo-80B.
+
+Table 3: Video QA results. $\mathsf { B L I P } _ { V Q A }$ is finetuned on VQA [4]. ♠ indicates concurrent work. PT, FT indicates pretraining and finetuning.   
+
+<table><tr><td>Method</td><td>#videoPT</td><td>#videoFT</td><td>MSR-VTT</td><td>MSVD</td></tr><tr><td>BLIP</td><td>0</td><td>O-shot</td><td>0.55</td><td>0.45</td></tr><tr><td>BLIP</td><td>0</td><td>5-shot</td><td>0.84</td><td>0.53</td></tr><tr><td>BLIPvQA [26]</td><td>0</td><td>O-shot</td><td>19.2</td><td>35.2</td></tr><tr><td>VidIL(ours)</td><td>0</td><td>5-shot</td><td>21.2</td><td>39.1</td></tr><tr><td>Flamingo-3B [2]</td><td>27M</td><td>4-shot</td><td>14.9</td><td>33.0</td></tr><tr><td>Flamingo-3B_[2]</td><td>27M</td><td>8-shot</td><td>19.6</td><td>37.0</td></tr><tr><td>Flamingo-80B [2]</td><td>27M</td><td>4-shot</td><td>23.9</td><td>41.7</td></tr><tr><td>Flamingo-80B [2]</td><td>27M</td><td>8-shot</td><td>27.6</td><td>45.5</td></tr><tr><td>ALPRO [25]</td><td>2M</td><td>full-shot</td><td>42.1</td><td>45.9</td></tr></table>
+
+Table 4: Accuracy $( \% )$ on VLEP hidden test set.   
+
+<table><tr><td>Method</td><td>#videoFT</td><td>Acc</td></tr><tr><td>VLEP [23] MERLOT[68]</td><td>20142 20142</td><td>67.5 68.4</td></tr><tr><td>VidIL(ours)</td><td>10-shot</td><td>72.0</td></tr><tr><td>Human</td><td>-</td><td>90.5</td></tr></table>
+
+# 4.4 Few-shot Video-Language Event Prediction
+
+In this section, we show that our model not only can answer questions about the video visual features but also answering "What is more likely to happen next?". Given a video with associated subtitle transcript as premise, the video-language event prediction (VLEP) task is to predict the most likely future event. The original VLEP [23] paper formulates the problem as a binary classification problem where the model will be chosen from two possible future event candidates. Instead, we formulate this problem as another video-to-text generation problem to fit into our framework. Figure 5 depicts an example with the same format as in Figure 2. Similar to the evaluation setting in QA, the generated free-form text will first be mapped to one of the two candidate answers using SentenceBert [47], and then calculate the accuracy. In Table 4, we report accuracy on the hidden test set of VLEP [23]. To our surprise, our 10-shot model outperforms state-of-the-art fully-supervised baseline, i.e., MERLOT [68], by a large margin $( \sim 4 \% )$ . This shows that our model has strong few-shot ability not only on videolanguage understanding but also on prediction. Since event prediction tasks rely heavily on temporal ordering, we show that with the proposed temporal-aware prompting, language models can be guided to capture temporal dynamics between historical and future events.
+
+![](images/337717ccf8591fcb87cdc2bcb90b9982ea93bafdcb263be40f57ceb4066b57ea.jpg)  
+Figure 5: Prompt for VLEP task.
+
+# 4.5 Semi-supervised Text-Video Retrieval
+
+In addition to video-to-text generation tasks, we show that a broader range of video-language tasks can benefit from our few-shot video captioner from a data perspective. Here, we consider a lowbudget semi-supervised setting where we only have a few labeled video-caption pairs and a large amount of unlabeled videos. The idea is to leverage our video captioner to generate pseudo labels for training any given vision-language models. As a case study, we evaluate on two text-video retrieval benchmarks, i.e., MSR-VTT and VaTeX. We use greedy decoding to generate pseudo caption for each video in the training set. We then train an identical base model, i.e., BLIP, using different pseudo labeled data as well as ground truth annotations. We report Recall $@$ 1 and 5 for both video-to-text and text-to-video retrieval. Table 5 shows that through training on our pseudo labels, we can achieve significant improvements compared with zero-shot BLIP. We also show that the performance gain is not simply a result of training on more data, since finetuning on the pseudo labels generated by other baselines (UniVL, BLIP) is less effective and can even hurt the performance. Furthermore, on MSR-VTT Recall $@$ 5 we can even achieve comparable performance against BLIP model finetuned on full ground truth annotations.
+
+Table 5: Semi-supervised text-video retrieval with 10 labeled examples. $\mathrm { V _ { l a b e l } }$ or $\mathrm { \Delta V _ { u n l a b e l } }$ are the number of labeled and unlabeled videos, respectively. $t \_ R I$ and $t \_ R$ denote video-to-text Recall $@ 1$ and 5. $\nu \_ R I$ and $\nu \_ R 5$ denote text-to-video Recall $@ 1$ and 5.   
+
+<table><tr><td rowspan="2">Model</td><td rowspan="2">Pseudo Label</td><td colspan="5">MSR-VTTRetrieval</td><td colspan="5">VaTexRetrieval</td></tr><tr><td>Vlabel/Vunlabel t_R1t_R5v_R1</td><td></td><td></td><td></td><td>v_R5</td><td>Vlabel/Vunlabel t_R1t_R5v_R1</td><td></td><td></td><td></td><td>v_R5</td></tr><tr><td>BLIP</td><td></td><td></td><td>33.2</td><td>57.2</td><td>40.5</td><td>62.8</td><td></td><td>28.2</td><td>53.4</td><td>34.0</td><td>58.6</td></tr><tr><td>BLIP</td><td>UniVL</td><td>10 /7010</td><td>33.1</td><td>57.3</td><td>33.6</td><td>57.7</td><td>10 /22685</td><td>25.5</td><td>47.7</td><td>26.1</td><td>49.1</td></tr><tr><td>BLIP</td><td>BLIP</td><td>10/7010</td><td>35.6</td><td>60.8</td><td>39.8</td><td>60.4</td><td>10/22685</td><td>26.3</td><td>50.5</td><td>29.3</td><td>53.6</td></tr><tr><td>BLIP</td><td>BLIPcap</td><td>10/7010</td><td>35.3</td><td>58.0</td><td>39.1</td><td>63.3</td><td>10/22685</td><td>23.9</td><td>46.8</td><td>27.5</td><td>49.7</td></tr><tr><td>BLIP</td><td>VidIL(ours)</td><td>10/7010</td><td>39.6</td><td>64.5</td><td>40.8</td><td>65.2</td><td>10 /22685</td><td>33.3</td><td>59.1</td><td>33.7</td><td>59.5</td></tr><tr><td>BLIP</td><td>Ground Truth</td><td>7010/0</td><td>43.6</td><td>66.2</td><td>43.1</td><td>67.2</td><td>22685/0</td><td>40.1</td><td>66.4</td><td>40.1</td><td>66.6</td></tr><tr><td>ALPRO [25]</td><td>Ground Truth</td><td>140200/0</td><td>32.0</td><td>60.6</td><td>33.9</td><td>60.7</td><td></td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>DRL [56]</td><td>Ground Truth</td><td>180000/0</td><td>54.1</td><td>77.4</td><td>52.9</td><td>78.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></table>
+
+Another interesting observation is that, compared with the video captioning results in Table 2, we found that the gain of our model over baselines on text-video retrieval is more visible than on captioning. A key factor in performing well on text-video retrieval tasks is to learn a good video-text multi-modal alignment. This result shows that our pseudo labels capture richer video-text alignment that can benefit the retrieval-style downstream task. The N-gram based generation metrics, e.g., BLEU, may not be able to fully reflect the alignment information, due to the variety of semantic levels in video captions. Furthermore, from a data perspective, our video captioner can be viewed as a data augmentation tool which is capable of generating or augmenting any open-domain videolanguage pretraining datasets with minimal human effort. As a result, we can potentially improve video-language pretraining by constructing a cleaner and more diverse video-text corpus.
+
+Table 6: Impact of visual tokens and temporal dimension.   
+
+<table><tr><td colspan="2">Video Representation</td><td>Avg↑ Std↓</td><td></td></tr><tr><td rowspan="3">Visual Token</td><td>Frame Frame+Object</td><td>39.6 40.3</td><td>3.7 2.9</td></tr><tr><td>Frame+Object+Event</td><td>39.9</td><td>2.8</td></tr><tr><td>Frame+Object+Attibute Frame+Object+Event+Attribute</td><td>40.9 40.8</td><td>2.9 2.4</td></tr><tr><td>Temporal</td><td></td><td>Reduce to one frame Reverse temporal order</td><td>38.5 40.7</td><td>2.4 1.7</td></tr></table>
+
+Table 7: Impact of shot selection. #ICE indicates the number of in-context examples in the prompt. Details of in-context example selection are in the Appendix.   
+
+<table><tr><td>#shot</td><td colspan="3">w/o selection</td><td colspan="3">w/ selection</td></tr><tr><td></td><td>#ICE</td><td>Avg↑</td><td>Std</td><td>#ICE</td><td>Avg↑</td><td>Std↓</td></tr><tr><td>5</td><td>5</td><td>38.4</td><td>2.1</td><td>5</td><td>40.4</td><td>1.2</td></tr><tr><td>10</td><td>10</td><td>41.3</td><td>3.6</td><td>5</td><td>40.8</td><td>2.4</td></tr><tr><td>20</td><td>20</td><td>42.6</td><td>3.3</td><td>5</td><td>42.2</td><td>2.0</td></tr><tr><td>30</td><td>30</td><td>40.0</td><td>2.9</td><td>5</td><td>41.1</td><td>1.9</td></tr></table>
+
+# 4.6 Ablation Studies
+
+We perform comprehensive ablation studies on our few-shot prompt including the impact of different video representation, number of shots and in-context selection. All the ablation results are evaluated on MSVD_QA validation set, and we report the mean and standard deviation of each setting on three sets of randomly sampled shots. For the cases with in-context example selection, we further select 5 examples as in-context examples from the sampled shots, while for the cases without in-context selection, all shots will be feed into the prompt. In Table 6, we show adding visual tokens consistently improves not only the model accuracy but also the model variance. A lower standard deviation indicates that the model is less sensitive to the few-shot sampling.
+
+To further demonstrate the impact of the additional temporal dimension of videos, we perform two ablations on the "Frame+Object+Event+Attribute" setting. First, we reduce the number of frame captions and visual tokens to be one9 for each video. We found that the performance drops significantly compared with using the default four frames, which indicates the model’s ability to incorporate information from multiple timestamps. Further, we found that fine-grained temporal modeling is rarely required for performing well on current video-language benchmarks. As shown in the ablation result where we reverse the order of all visual tokens and frame captions, the performance decreased only marginally, which indicates that current benchmarks may not be sufficient in reflecting the benefits from better temporal ordering.
+
+In Table 7, we first show that, with the same context length, namely, 5 in-context examples, in-context example selection significantly increases the performance as well as the robustness. At 10-shot, and 20-shot, directly fitting more shots into the prompt results in better performance. In-context selection achieves slightly lower performance but with significantly better efficiency due to shorter context. Interestingly, at 30-shot, in-context selection with 5 examples outperforms directly adding all 30 shots into the prompt. This is showing that in-context selection can help the model utilize a larger number noisy video examples. Nevertheless, we still observe that the benefit of adding more shots saturated at around 20 to 30 shots, even if with in-context selection. we view this as a remaining challenging on how to make language models benefit from longer contexts.
+
+# 5 Conclusions, Limitations and Future Work
+
+This paper proposes VidIL, a few-shot Video-language Learner via Image and Language models. It demonstrates the strong ability of large-scale language models on performing video-to-text tasks when frame features are provided as unified text representations using image-language models. We propose a temporal order aware prompt by decomposing videos into a hierarchical structure, which is able to plug in multiple levels of frame features, along with speech transcripts. Without pretraining on videos, our model outperforms vision-language models learned from large-scale video datasets on a variety of few-shot tasks, such as domain-specific captioning, question answering, and future event prediction. One limitation of using unified textual representation is that we might lose low-level visual features which can be essential for some specific tasks, such as fine-grained spatial visual question answering. We also observe that current video-language benchmarks rarely require explicit temporal tracking on the frames and visual tokens. Future work will focus on leveraging large-scale language models for learning script knowledge from long videos where temporal dynamics are better emphasized.
+
+# 6 Broader Impact
+
+An open-domain few-shot video-language learner has a wide range of beneficial applications for society, such as automatically detecting violent or mature content in videos and helping people with vision impairment understand videos. However, since the language model is pretrained on massive internet-scale text data, there might be unexpected output that can have potential negative impact on the society, such as bias against people of a certain gender, race or sexuality. Future work and dedicated collaboration from the community are needed to alleviate the potential negative societal impact of large language models.
+
+# Acknowledgements
+
+We thank the anonymous reviewers helpful suggestions. This research is based upon work supported in part by U.S. DARPA AIDA Program No. FA8750-18-2-0014 and U.S. DARPA KAIROS Program Nos. FA8750-19-2-1004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
+
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+        "type": "text",
+        "text": "Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners ",
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+        "text": "Zhenhailong Wang1∗, Manling $\\mathbf { L i } ^ { 1 }$ ∗, Ruochen $\\mathbf { X } \\mathbf { u } ^ { 2 }$ , Luowei Zhou2†, Jie Lei3, Xudong Lin4, Shuohang $\\mathbf { W a n g } ^ { 2 }$ , Ziyi $\\mathbf { Y a n g } ^ { 2 }$ , Chenguang $\\mathbf { Z } \\mathbf { h } \\mathbf { u } ^ { 2 }$ , Derek Hoiem1, Shih-Fu Chang4, Mohit Bansal3, Heng Ji1 1UIUC 2MSR 3UNC 4Columbia University {wangz3,hengji}@illinois.edu ",
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+        "text": "Abstract ",
+        "text_level": 1,
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+        "type": "text",
+        "text": "The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples. Existing few-shot video-language learners focus exclusively on the encoder, resulting in the absence of a video-totext decoder to handle generative tasks. Video captioners have been pretrained on large-scale video-language datasets, but they rely heavily on finetuning and lack the ability to generate text for unseen tasks in a few-shot setting. We propose VidIL, a few-shot Video-language Learner via Image and Language models, which demonstrates strong performance on few-shot video-to-text tasks without the necessity of pretraining or finetuning on any video datasets. We use image-language models to translate the video content into frame captions, object, attribute, and event phrases, and compose them into a temporal-aware template. We then instruct a language model, with a prompt containing a few in-context examples, to generate a target output from the composed content. The flexibility of prompting allows the model to capture any form of text input, such as automatic speech recognition (ASR) transcripts. Our experiments demonstrate the power of language models in understanding videos on a wide variety of video-language tasks, including video captioning, video question answering, video caption retrieval, and video future event prediction. Especially, on video future event prediction, our few-shot model significantly outperforms state-of-the-art supervised models trained on large-scale video datasets. Code and processed data are publicly available for research purposes at https://github.com/MikeWangWZHL/VidIL. ",
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+        "text": "1 Introduction ",
+        "text_level": 1,
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+    },
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+        "type": "text",
+        "text": "One major gap between artificial intelligence and human intelligence lies in their abilities to generalize and perform well on new tasks with limited annotations. Recent advances in large-scale pre-trained generative language models [45, 6, 71, 24] have shown promising few-shot capabilities [72, 43, 63] in understanding natural language. However, few-shot video-language understanding is still in its infancy. A particular limitation of most recent video-language frameworks [28, 21, 61, 68, 67, 25, 64, 34] is that they are encoder-only, which means they do not have the ability to generate text from videos for purposes such as captioning [62, 57], question answering [60], and future prediction [23]. Meanwhile, unified video-language models [36, 49] that are capable of language decoding still rely heavily on finetuning using a large number of manually annotated video-text pairs, therefore cannot adapt quickly to unseen tasks. Few-shot video-to-text decoding is challenging because the natural language supervision for learning video-language representation is typically based on subtitles and automatic speech recognition (ASR) transcripts [39, 68], which differ significantly from downstream tasks in terms of distribution and may have poor semantic alignment across vision and text modalities. ",
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+        "type": "text",
+        "text": "We propose to address this problem by harnessing the few-shot power of frozen large-scale language models, such as InstructGPT [40]. Our inspiration is derived from the fact that humans are excellent visual storytellers [15], with the ability to piece together a coherent story from a few isolated images. To mimic this, we propose VidIL, a few-shot Video-language Learner via Image and Language models, to use image models to provide information about the visual content in the video (as well as optionally use ASR to represent speech), and then we instruct language models to generate a video-based summary, answer, or other target output for diverse video-language tasks. ",
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+        "type": "text",
+        "text": "The main challenge of understanding videos is that, videos contain rich semantics and temporal content at multiple granularities. Unlike static images which depict objects, attributes and events in a snapshot, the temporal dimension of videos further conveys the state changes of the objects, actions, and events. For example, in Figure 1, the individual frame captions of the video clip only describe static visual features such as \"a person holding a green object in hand\". In contrast, a correct video-level description would be \"a woman makes realistic looking leaves and flowers for a cake\", which involves reasoning over a collection of objects and events that occur at different timestamps in the video clip, such as \"cake decorating\" and \"flowered design\". Hence, to inform video-level description and queries, we need to represent all of this information and its temporal ordering. ",
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+    {
+        "type": "image",
+        "img_path": "images/b5f930daf9032229a0d0a4e399381948a20cacaccc02903750ded88494988f0e.jpg",
+        "image_caption": [
+            "Figure 1: Multiple levels of information in videos. "
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "To address the unique challenges of videos, we propose to decompose a video into three levels: the video output, frame captions, and visual tokens (including objects, events, attributes). One major benefit from this hierarchical video representation is that we can separate the visual and temporal dimensions of a video. We leverage frozen image-language foundational models at lower levels to collect salient visual features from the sparsely sampled frames. Specifically, we first leverage a pretrained image-language contrastive model CLIP [44] to perform visual tokenization, based on the similarity score between frames and tokens of objects, events and attributes. The tokenization is done under the guidance of semantics role labeling [14], which provides us with candidate events with involved objects and related attributes. Next, in order to capture the overall semantics at the frame level, we employ the pretrained image captioner in the image-language model BLIP [26] to obtain frame captions. Finally, we instruct a pretrained large language model using in-context learning [40, 13, 51, 48] to interpret visual tokens and frame captions into the target textual output. In detail, we temporally order visual tokens and frame captions using specially designed prompts such as “First...Then...Finally”, to instruct the pretrained language model to track the changes of objects, events, attributes and frame semantics along the temporal dimension. ",
+        "bbox": [
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+        "page_idx": 1
+    },
+    {
+        "type": "text",
+        "text": "Without pretraining or finetuning on any video datasets, we show that our approach outperforms both video-language and image-language state-of-the-art baselines on few-shot video captioning and question answering tasks. Moreover, on video-language event prediction, our approach significantly outperforms fully-supervised models while using only 10 labeled examples. We further demonstrate that our generative model can benefit broader video-language understanding tasks, such as text-video retrieval, via pseudo label generation. Additionally, we show that our model is highly flexible in adding new modalities, such as ASR transcripts. ",
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+    {
+        "type": "text",
+        "text": "2 Related Work ",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "2.1 Image-Language Models and Their Applications on Video-Language Tasks ",
+        "text_level": 1,
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "Large-scale image-language pretraining models optimize image-text matching through contrastive learning [44, 17] and multimodal fusion [65, 27, 58, 66, 35, 52, 8, 29, 73, 70, 18, 16]. Recently, ",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "BLIP [26] proposes a bootstrapping image-language pretraining framework with a captioner and a filterer which has shown promising performance on various image-language tasks. However, video-language pretraining [25, 36, 28, 38, 3, 1, 42, 33] is still hindered by noisy and domain-specific video datasets [74, 22, 39]. Naturally, researchers start to explore transferring the rich knowledge from image models to videos. Different from the traditional way of representing videos by 3D dense features [12], recent work [21, 25] proves that sparse sampling is an effective way to represent videos, which facilitates applying pre-trained image-language models to video-language tasks [37, 11]. Specifically, the image-language model BLIP [26] sets new state-of-the-art on zero-shot retrieval-style video-language tasks, such as video retrieval and video question answering. However, for generationstyle tasks such as domain-specific video captioning, video-language model UniVL [36] still leads the performance but highly rely on fine-tuning. In this work, we extend the idea of leveraging image-language models to a wide variety of video-to-text generation tasks. We further connect imagelanguage models with language models which empowers our model with strong generalization ability. We show that the knowledge from both image-language pretraining and language-only pretraining can benefit video-language understanding in various aspects. ",
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+    {
+        "type": "text",
+        "text": "2.2 Unifying MultiModal Tasks with Language Models ",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "The community has paid much attention to connecting different modalities with a unified representation recently. Text-only generation models, such as T5 [46], have been extended to vision-language tasks by text generation conditioned on visual features [9, 53, 50, 75, 55]. In order to fully leverage the generalization power from pretained language models, [63] represents images using text in a fully symbolic way. [32] includes more modalities such as video and audio, but requires annotated video-text data to jointly training the language model with the video and audio tokenizer. In this work, we propose a temporal-aware hierarchical representation for describing a video textually. To our knowledge, we are the first work to leverage prompting a frozen language model for tackling few-shot video-language tasks with a unified textual representation. Concurrent work Socratic [69] uses a zero-shot language-based world-state history to represent long videos with given time stamps, while our model can quickly adapt to different video and text distributions with few examples. Furthermore, we show that by injecting temporal markers to the prompt we can make a pre-trained language model understand fine-grained temporal dynamics in video events. Compared with the concurrent work Flamingo [2], which requires dedicated vision-language post-pretraining, our framework does not require to pretrain or finetune on any video data. Our framework is simple and highly modulated where all the components are publicly available. Additionally, our framework is more flexible on adding new modalities, e.g., automatic speech recognition, without the need for complex redesigning. ",
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+    },
+    {
+        "type": "text",
+        "text": "3 Method ",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "We propose a hierarchical video representation framework which decomposes a video into three levels, i.e., visual token level, frame level and video level. The motivation is to separate the spatial and temporal dimension of a video in order to leverage image-language and language-only foundation models, such as CLIP [44] and GPT-3 [6]. All three levels use a unified textual representation which enables us to leverage the powerful few-shot ability from pretrained language models. ",
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+    {
+        "type": "text",
+        "text": "3.1 Frame Level: Image Captioning ",
+        "text_level": 1,
+        "bbox": [
+            176,
+            756,
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+        "page_idx": 2
+    },
+    {
+        "type": "text",
+        "text": "Following [21] we first perform sparse sampling to obtain several video frames. Unless otherwise specified, we sample 4 frames for frame level and 8 frames for visual token level. We then feed each frame into a pre-trained image-language model to obtain frame level captions. An example can be found in the blue part of Figure 2. In our experiments, we use BLIP [26], a recent image-language framework containing both image-grounded encoder and decoder, for generating frame captions. We follow [26] to do both captioning and filtering on each frame. However, as mentioned in Section 1, videos contain rich semantics and temporal contents at multiple granularities. It is not enough to generate video-level target text such as video captions solely based on frame captions. Thus, we further perform visual tokenization for each frame to capture features at a finer granularity. ",
+        "bbox": [
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+        "page_idx": 2
+    },
+    {
+        "type": "image",
+        "img_path": "images/c144de0cb595e241110b349a7da85b61029ef363498acc4c08b0c7647b08efae.jpg",
+        "image_caption": [
+            "Figure 2: Overview of VidIL framework. We represent a video in a unified textural representation containing three semantic levels: visual token level, frame level, and video level. At visual token level, we extract salient objects, events, attributes for each sampled frame. At frame level, we perform image captioning and filtering. At video level, we construct video representation by aggregating the visual tokens, frame captions and other text modalities such as ASR, using a few-shot temporalaware prompt. We then feed the prompt to a pre-trained language model together with task-specific instructions to generate target text for a variety of video-language tasks. Examples of the full prompt for different tasks can be found in Appendix ??. "
+        ],
+        "image_footnote": [],
+        "bbox": [
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+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "3.2 Visual Token Level: Structure-Aware Visual Tokenization ",
+        "text_level": 1,
+        "bbox": [
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+        ],
+        "page_idx": 3
+    },
+    {
+        "type": "text",
+        "text": "At this level, we aim to extract the textual representations of salient visual token types, such as objects, events and attributes. We found that pre-defined classes for classification, such as those in ImageNet [10], are far from enough for covering the rich semantics in open-domain videos. Thus, instead of using classification-based methods for visual tokenization as in previous work [32, 63], we adopt a retrieval-based visual tokenization approach by leveraging pre-trained contrastive imagelanguage models. Given a visual token vocabulary which contains all candidate object, event, and attribute text phrases, we compute the image embedding of a frame and the text embeddings of the candidate visual tokens using a contrastive multi-modal encoder, CLIP [44]. We then select top 5 visual tokens per frame based on the cosine similarity of the image and text embeddings. An example of the extracted object tokens can be found in the green part of Figure 2. ",
+        "bbox": [
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+        "type": "text",
+        "text": "Unlike in images where objects and attributes already cover most visual features, events are more informative in videos. In order to discover events from video frames, we construct our own event vocabulary by extracting event structures from Visual Genome [19] synsets3 using Semantic Role Labeling. Specifically, we first select the phrases that contain at least one verb and one argument as events. Then we remove highly similar events based on their sentence similarity using SentenceBERT [47] embeddings. For object vocabulary, we adopt OpenImage [20] full classes $( { \\sim } 2 0 \\mathbf { k } )$ , instead of using the visually groundable subset $( \\sim 6 0 0 )$ as in concurrent work [69]. We found that using large but noisy vocabulary is more effective than using small but clean vocabulary in our retrieval-based setting with CLIP. For attribute vocabulary, we adopt visual genome attribute synset. In Section 4.6, we provide ablation study on the impact of different types of visual tokens. The statistics of visual token vocabulary can be found in Appendix Table ??. ",
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+    {
+        "type": "image",
+        "img_path": "images/331592c36e0944e6b5f6973236f3089e33d229d668d68f0224a0f67fdb784fba.jpg",
+        "image_caption": [
+            "Figure 3: Temporal-aware prompt successfully distinguishes the Sunset and Sunrise scenarios based on the temporal ordering change of objects and frame captions, while the static prompt fails. "
+        ],
+        "image_footnote": [],
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+    {
+        "type": "text",
+        "text": "3.3 Video Level: Temporal-Aware Few-shot Prompting ",
+        "text_level": 1,
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+        "text": "Once we obtain the textual representation from frame level and visual token level, the final step is to put the pieces together to generate a video level target text. The goal is to build a model that can be quickly adapted to any video-to-text generation task with only a few examples. To this end, we propose to leverage large-scale pre-trained language models, such as GPT-3 [6], with a temporal-aware few-shot prompt. As shown in Figure 2, our framework can be readily applied to various video-to-text generation tasks, such as video captioning and video question answering, with a shared prompt template. The proposed prompting strategy enables a language model to attend to the lower level visual information as well as taking into account the temporal ordering. ",
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+        "type": "text",
+        "text": "Here, we use the video captioning task depicted in Figure 2 to illustrate the details. The few-shot prompt consists of three parts: instruction, few-shot context, and task query. The instruction is a concise description of the generation task, e.g., \"Generate a video caption based on the objects, events, attributes and frame captions. Example:\", which is proved to be effective in zero-shot and few-shot settings [6, 59]. The few-shot context contains the selected in-context examples as well as the test video instance. Each video instance is represented by the aggregated visual tokens4, e.g., \"Objects: First, bath toy. Then,...\" the frame captions, such as \"Frame Captions: First, a toddler playing in a bathtub filled with toys. Then,...\", and the ASR inputs if available, e.g., \"Subtitle:<ASR Transcript>\". Finally, the task query is a task-specific suffix indicating the target text format, e.g. \"Video Caption:\". For in-context examples (omitted here for simplicity), the task query is followed by ground truth annotation, while for the test instance, the generation starts at the end of the task query. ",
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+        "type": "text",
+        "text": "Formally, we denote the instruction line as $\\mathbf { t }$ , few-shot context as c, the task query as q, and the target text as $\\mathbf { y }$ , where $\\mathbf { y } = ( y _ { 1 } , y _ { 2 } , . . . , y _ { L } )$ . The generation of the next target token $y _ { l }$ can be modeled as: ",
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+    {
+        "type": "equation",
+        "img_path": "images/bc7c063980fb4da70b5248c3574a151aaa1f1ceeacd34ba3434960d3c7181041.jpg",
+        "text": "$$\ny _ { l } = \\mathop { \\arg \\operatorname* { m a x } } _ { y } p ( y | \\mathbf { s } , \\mathbf { c } , \\mathbf { q } , y _ { < l } )\n$$",
+        "text_format": "latex",
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+    {
+        "type": "text",
+        "text": "In order to capture the temporal dynamics between frames and visual tokens, we further propose to inject temporal markers to the prompt. As shown in the few-shot context in Figure 2, each visual token and frame caption is prefixed with a natural language phrase indicating its temporal ordering, e.g., \"First,\",\"Then,\", and \"Finally,\". We found adding the temporal marker can make the language model conditioned on not only literal but also temporal information of the context. We show an example in Figure 3, where we compare our temporal-aware prompt with a static prompt on video captioning using InstructGPT. Again, the in-context examples are omitted here, which can be found in Appendix ??. In this example, the only difference between these two contexts is the ordering of the visual tokens and the frame captions. For the context on the left, where \"sun moving\" appears before \"night sky\", we are expected to see a story talking about sunset, while for the context on the right, we are expected to see sunrise. We can see the static prompt generates captions about sunset for both contexts, while the temporal-aware prompt can capture temporal ordering correctly and generate sunrise for the context on the right. ",
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+    {
+        "type": "text",
+        "text": "4 Experiments ",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "4.1 Experimental Setup ",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "To comprehensively evaluate our model, we show results on four video-language understanding tasks in few-shot settings: video captioning, video question answering (QA), video-language event prediction, and text-video retrieval. We compare our approach with state-of-the-art approaches on five benchmarks, i.e, MSR-VTT [62], MSVD [7], VaTeX [57], YouCook2 [74], and VLEP [23]. The statistics of the datasets can be found in Table 1. For more details please refer to Appendix ??. ",
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+    {
+        "type": "text",
+        "text": "Implementation Details. We use CLIP-L/146 as our default encoder for visual tokenization. We adopt BLIP captioning checkpoint7 finetuned on COCO [31] for frame captioning. We use InstructGPT [40] as our default language model for generating text conditioned on the few-shot prompt. To construct event vocabulary, we use the semantic role labeling model from AllenNLP8. The experiments are conducted on 2 NVIDIA V100 (16GB) GPUs. All few-shot ",
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+    {
+        "type": "table",
+        "img_path": "images/92dd6fadc5a80d33d4e99b29ba373e80a80afa39fcabf656f24d4c71b6eab48f.jpg",
+        "table_caption": [
+            "Table 1: Statistics of datasets in our experiments "
+        ],
+        "table_footnote": [
+            "finetuning on baselines and semi-supervised training are performed on 2 Nvidia V100 16G GPUs. "
+        ],
+        "table_body": "<table><tr><td>Dataset</td><td>Task</td><td>Split Count # train /#eval</td></tr><tr><td>MSR-VTT[62]</td><td>Captioning; QA</td><td>6,513 /2.990</td></tr><tr><td>MSR-VTT[62]</td><td>Retrieval</td><td>7,010 / 1,000</td></tr><tr><td>MSVD [7]</td><td>Question Answering</td><td>30,933 /13,157</td></tr><tr><td>VaTeX v1.15 [57]</td><td>Captioning; Retrieval</td><td>25,991/6.000</td></tr><tr><td>YouCook2[74]</td><td>Captioning</td><td>10,337 /3,492</td></tr><tr><td>VLEP [23]</td><td>Event Prediction</td><td>20,142/4,192</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "In-context Example Selection. From our preliminary experiments, we find that the generation performance is sensitive to the quality of in-context examples. For example, for QA tasks such as MSVD-QA where the annotations are automatically generated, the <question, answer> pair in randomly selected in-context examples can be only weakly-correlated with the video context. Thus, instead of using a fixed prompt for each query, we dynamically filter out the irrelevant in-context examples. Specifically, given a randomly sampled $M .$ -shot support set from the training set, we select a subset of $N _ { ☉ }$ -shots as in-context examples based on their SentenceBERT [47] similarities with text queries. Furthermore, we reorder the selected examples in ascending order based on the similarity score to account for the recency bias [72] in large language models. For QA tasks, we choose the most relevant in-context examples by comparing with questions. While for captioning task, we compare with frame captions. If not otherwise specified, we use $M { = } I O$ and $N { = } 5$ , which we consider as 10-shot training. ",
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+    {
+        "type": "text",
+        "text": "4.2 Few-shot Video Captioning ",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "We report BLEU-4 [41], ROUGE-L [30], METEOR [5], and CIDEr [54] scores on three video captioning benchmarks covering both open-domain (MSR-VTT, VaTeX) and domain-specific (YouCook2) videos. We compare with both state-of-the-art video captioner (UniVL [36]) and image captioner (BLIP [26]). In order to implement the BLIP baseline for few-shot video captioning, we extend the approach used for text-video retrieval evaluation in [26] to video-language training. Specifically, we concatenate the visual features of sampled frames and then feed them into the image-grounded text-encoder to compute the language modeling loss. This is equivalent to stitching the sampled frames into a large image and then feeding it to BLIP for image captioning. We found that this simple approach results in very strong baselines. ",
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+    {
+        "type": "text",
+        "text": "As shown in Table 2, existing methods have strong bias on certain datasets. For example, UniVL performs well on YouCook2 but fails on MSR-VTT and VaTeX, while BLIP performs the opposite. This is because UniVL is pretrained on HowTo100M which favors instructional videos, i.e., YouCook2, while BLIP is pre-training on image-caption pairs which favors description-style captions, i.e., MSR-VTT and VaTeX. On the contrary, our model performs competitively on both open-domain and instructional videos, and significantly outperforms the baselines on the average CIDEr score across all three benchmarks. This indicates that by leveraging language models, we can maintain strong few-shot ability regardless of the video domain or the target caption distribution. ",
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+    {
+        "type": "table",
+        "img_path": "images/bdc0755cadf49e276175a4fa571481c0553518341285949af409281797b235b2.jpg",
+        "table_caption": [
+            "Table 2: 10-shot video captioning results. ♠ indicates concurrent work. The reported Flamingo [2] results are using 16 shots. #VideoPT represents the number of videos used for pre-training. B-4, R-L, $M$ , $C$ represents BLEU-4, ROUGE-L, METEOR and CIDEr. Avg $C$ represents the average CIDEr score across all available benchmarks. ASR indicates whether the model has access to the ASR subtitles. $B L I P$ and $B L I P _ { c a p }$ use the pretrained checkpoint and the finetuned checkpoint on COCO captioning. All results are averaged over three random seeds. "
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Method</td><td rowspan=\"2\">#VideOpr ASR</td><td rowspan=\"2\"></td><td rowspan=\"2\">B-4R-LM</td><td colspan=\"3\">MSR-VTTCaption</td><td colspan=\"3\">YouCook2 Caption C</td><td rowspan=\"2\"></td><td colspan=\"3\">VaTex Caption</td><td rowspan=\"2\">Avg C</td></tr><tr><td></td><td></td><td>C</td><td>B-4R-LM</td><td></td><td></td><td>B-4 R-L M</td><td></td><td>C</td></tr><tr><td>Few-shot</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>UniVL</td><td>1.2M</td><td>No</td><td>2.1 22.5 9.5</td><td></td><td></td><td>3.6</td><td>3.3</td><td>25.3 11.6</td><td>34.1</td><td>1.7</td><td>15.7</td><td>8.0</td><td>2.1</td><td>13.3</td></tr><tr><td>BLIP</td><td>0</td><td>No</td><td>27.7 43.0 23.0 39.5</td><td></td><td></td><td></td><td>0.7</td><td>9.0 3.4</td><td>11.5</td><td>13.5</td><td>39.5</td><td>15.4</td><td>20.7</td><td>23.9</td></tr><tr><td>BLIPcap</td><td>0</td><td>No</td><td>21.648.022.7 30.2</td><td></td><td></td><td></td><td>3.7</td><td>8.6 3.8</td><td>9.4</td><td>20.7</td><td>41.5</td><td>17.4</td><td>28.9</td><td>22.8</td></tr><tr><td>VidIL(ours)</td><td>0</td><td>No</td><td>26.0 51.7 24.7 36.3</td><td></td><td></td><td></td><td>2.6</td><td>22.9 9.5</td><td>27.0</td><td>22.2</td><td></td><td>43.620.0</td><td>36.7</td><td>33.3</td></tr><tr><td>UniVL</td><td>1.2M</td><td>Yes</td><td>-</td><td></td><td>=</td><td>-</td><td>4.3</td><td>26.4 12.2</td><td>48.6</td><td>2.7</td><td>17.7</td><td>10.2</td><td>3.4</td><td>26.0</td></tr><tr><td>VidIL(ours)</td><td>0</td><td>Yes</td><td></td><td></td><td></td><td></td><td>10.7 35.9</td><td></td><td>19.4 111.6</td><td></td><td>23.2 44.2 20.6 38.9</td><td></td><td></td><td>75.3</td></tr><tr><td>Flamingo-3B(16)</td><td>27M</td><td>No</td><td></td><td></td><td></td><td></td><td></td><td></td><td>73.2</td><td></td><td></td><td></td><td>57.1</td><td></td></tr><tr><td>Flamingo-80B(16) 27M</td><td></td><td>No</td><td></td><td></td><td></td><td></td><td></td><td></td><td>84.2</td><td></td><td></td><td></td><td>62.8</td><td>=</td></tr><tr><td>Fine-tuning</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>UniVL</td><td>1.2M</td><td>No</td><td>42.0 61.0 29.0 50.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td>[11.2 40.1 17.6 127.0|22.8 38.6 22.3 33.4</td><td></td><td></td><td></td><td>70.2</td></tr><tr><td>UniVL</td><td>1.2M</td><td>Yes</td><td>=</td><td></td><td></td><td></td><td></td><td></td><td></td><td>16.6 45.7 21.6176.823.7 39.3 22.7 35.6</td><td></td><td></td><td></td><td>106.2</td></tr></table>",
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+        "page_idx": 6
+    },
+    {
+        "type": "text",
+        "text": "As discussed in Section 1, video captions describe the content in various semantic levels. The N-gram based metric may not fairly reflect the models’ performance in capturing the video-caption alignment. We further verify this hypothesis in Section 4.5. Thus, in addition to automatic metrics, we include qualitative examples illustrated in Figure 4. More examples are in Appendix ??. ",
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+    },
+    {
+        "type": "text",
+        "text": "Additionally, for most existing methods and also concurrent work, e.g., Flamingo [2], adding a new modality often requires a dedicated model redesign or retraining. However, the nature of our framework, where we use a unified textual representation for each level, makes it highly flexible for incorporating new modalities. As shown in row 6 in Table, our model can effectively utilize extra information from ASR to obtain significantly better few-shot performance on certain datasets such as YouCook2. ",
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+    {
+        "type": "image",
+        "img_path": "images/42488e5e146be037d7c57abcc48a78f8af1cf75e6517bc50d3e1b32bfd715b6a.jpg",
+        "image_caption": [],
+        "image_footnote": [],
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+    {
+        "type": "text",
+        "text": "Figure 4: Qualitative examples on video captioning. Grey boxes contain part of the video representation from our model. Blue boxes contain caption generation from different models. Green boxes contain ground truth annotations. Bold green text highlights the correct information that is not captured in baseline outputs which can be reasoned from our visual tokens and frame captions. ",
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+    {
+        "type": "text",
+        "text": "4.3 Few-shot Video Question Answering ",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "We compare the test accuracy of our approach with few-shot pretrained BLIP, $\\mathsf { B L I P } _ { V Q A }$ [26], and concurrent work Flamingo [2] on two video question answering benchmarks, MSR-VTT_QA and MSVD_QA. $\\mathsf { B L I P } _ { V Q A }$ represents finetuned BLIP on VQA [4] dataset, which is the previous SOTA on zero/few-shot video question answering. In order to have fairer comparison with $\\mathsf { B L I P } _ { V Q A }$ , we reduce the shot number to 5 and report the average accuracy on three sets of randomly selected 5-shot examples. As shown in Table 3, our method outperforms previous SOTA by a large margin. Comparing with concurrent work Flamingo, which is post-pretrained on a large number of video-text data, our model is training-free and did not observe any video data. However, with only imagelanguage and language-only knowledge, our 5-shot model is able to outperform 8-shot Flamingo-3B and achieve on-par performance with 4-shot Flamingo-80B. ",
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+        "page_idx": 6
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+    {
+        "type": "text",
+        "text": "",
+        "bbox": [
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+        "page_idx": 7
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+    {
+        "type": "table",
+        "img_path": "images/80c20213acc3c9f54437b9d3f7c1181c8608963e7d4b561375c9c1f29b5cc5cb.jpg",
+        "table_caption": [
+            "Table 3: Video QA results. $\\mathsf { B L I P } _ { V Q A }$ is finetuned on VQA [4]. ♠ indicates concurrent work. PT, FT indicates pretraining and finetuning. "
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>#videoPT</td><td>#videoFT</td><td>MSR-VTT</td><td>MSVD</td></tr><tr><td>BLIP</td><td>0</td><td>O-shot</td><td>0.55</td><td>0.45</td></tr><tr><td>BLIP</td><td>0</td><td>5-shot</td><td>0.84</td><td>0.53</td></tr><tr><td>BLIPvQA [26]</td><td>0</td><td>O-shot</td><td>19.2</td><td>35.2</td></tr><tr><td>VidIL(ours)</td><td>0</td><td>5-shot</td><td>21.2</td><td>39.1</td></tr><tr><td>Flamingo-3B [2]</td><td>27M</td><td>4-shot</td><td>14.9</td><td>33.0</td></tr><tr><td>Flamingo-3B_[2]</td><td>27M</td><td>8-shot</td><td>19.6</td><td>37.0</td></tr><tr><td>Flamingo-80B [2]</td><td>27M</td><td>4-shot</td><td>23.9</td><td>41.7</td></tr><tr><td>Flamingo-80B [2]</td><td>27M</td><td>8-shot</td><td>27.6</td><td>45.5</td></tr><tr><td>ALPRO [25]</td><td>2M</td><td>full-shot</td><td>42.1</td><td>45.9</td></tr></table>",
+        "bbox": [
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+        ],
+        "page_idx": 7
+    },
+    {
+        "type": "table",
+        "img_path": "images/73514530509fe457864a28260c85bbc7f3012661dc1bd0d43e115e6c87eefb5c.jpg",
+        "table_caption": [
+            "Table 4: Accuracy $( \\% )$ on VLEP hidden test set. "
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>Method</td><td>#videoFT</td><td>Acc</td></tr><tr><td>VLEP [23] MERLOT[68]</td><td>20142 20142</td><td>67.5 68.4</td></tr><tr><td>VidIL(ours)</td><td>10-shot</td><td>72.0</td></tr><tr><td>Human</td><td>-</td><td>90.5</td></tr></table>",
+        "bbox": [
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+    },
+    {
+        "type": "text",
+        "text": "4.4 Few-shot Video-Language Event Prediction ",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "In this section, we show that our model not only can answer questions about the video visual features but also answering \"What is more likely to happen next?\". Given a video with associated subtitle transcript as premise, the video-language event prediction (VLEP) task is to predict the most likely future event. The original VLEP [23] paper formulates the problem as a binary classification problem where the model will be chosen from two possible future event candidates. Instead, we formulate this problem as another video-to-text generation problem to fit into our framework. Figure 5 depicts an example with the same format as in Figure 2. Similar to the evaluation setting in QA, the generated free-form text will first be mapped to one of the two candidate answers using SentenceBert [47], and then calculate the accuracy. In Table 4, we report accuracy on the hidden test set of VLEP [23]. To our surprise, our 10-shot model outperforms state-of-the-art fully-supervised baseline, i.e., MERLOT [68], by a large margin $( \\sim 4 \\% )$ . This shows that our model has strong few-shot ability not only on videolanguage understanding but also on prediction. Since event prediction tasks rely heavily on temporal ordering, we show that with the proposed temporal-aware prompting, language models can be guided to capture temporal dynamics between historical and future events. ",
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+    },
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+        "type": "image",
+        "img_path": "images/337717ccf8591fcb87cdc2bcb90b9982ea93bafdcb263be40f57ceb4066b57ea.jpg",
+        "image_caption": [
+            "Figure 5: Prompt for VLEP task. "
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+        "type": "text",
+        "text": "4.5 Semi-supervised Text-Video Retrieval ",
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+        "text": "In addition to video-to-text generation tasks, we show that a broader range of video-language tasks can benefit from our few-shot video captioner from a data perspective. Here, we consider a lowbudget semi-supervised setting where we only have a few labeled video-caption pairs and a large amount of unlabeled videos. The idea is to leverage our video captioner to generate pseudo labels for training any given vision-language models. As a case study, we evaluate on two text-video retrieval benchmarks, i.e., MSR-VTT and VaTeX. We use greedy decoding to generate pseudo caption for each video in the training set. We then train an identical base model, i.e., BLIP, using different pseudo labeled data as well as ground truth annotations. We report Recall $@$ 1 and 5 for both video-to-text and text-to-video retrieval. Table 5 shows that through training on our pseudo labels, we can achieve significant improvements compared with zero-shot BLIP. We also show that the performance gain is not simply a result of training on more data, since finetuning on the pseudo labels generated by other baselines (UniVL, BLIP) is less effective and can even hurt the performance. Furthermore, on MSR-VTT Recall $@$ 5 we can even achieve comparable performance against BLIP model finetuned on full ground truth annotations. ",
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+        "table_caption": [
+            "Table 5: Semi-supervised text-video retrieval with 10 labeled examples. $\\mathrm { V _ { l a b e l } }$ or $\\mathrm { \\Delta V _ { u n l a b e l } }$ are the number of labeled and unlabeled videos, respectively. $t \\_ R I$ and $t \\_ R$ denote video-to-text Recall $@ 1$ and 5. $\\nu \\_ R I$ and $\\nu \\_ R 5$ denote text-to-video Recall $@ 1$ and 5. "
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td rowspan=\"2\">Model</td><td rowspan=\"2\">Pseudo Label</td><td colspan=\"5\">MSR-VTTRetrieval</td><td colspan=\"5\">VaTexRetrieval</td></tr><tr><td>Vlabel/Vunlabel t_R1t_R5v_R1</td><td></td><td></td><td></td><td>v_R5</td><td>Vlabel/Vunlabel t_R1t_R5v_R1</td><td></td><td></td><td></td><td>v_R5</td></tr><tr><td>BLIP</td><td></td><td></td><td>33.2</td><td>57.2</td><td>40.5</td><td>62.8</td><td></td><td>28.2</td><td>53.4</td><td>34.0</td><td>58.6</td></tr><tr><td>BLIP</td><td>UniVL</td><td>10 /7010</td><td>33.1</td><td>57.3</td><td>33.6</td><td>57.7</td><td>10 /22685</td><td>25.5</td><td>47.7</td><td>26.1</td><td>49.1</td></tr><tr><td>BLIP</td><td>BLIP</td><td>10/7010</td><td>35.6</td><td>60.8</td><td>39.8</td><td>60.4</td><td>10/22685</td><td>26.3</td><td>50.5</td><td>29.3</td><td>53.6</td></tr><tr><td>BLIP</td><td>BLIPcap</td><td>10/7010</td><td>35.3</td><td>58.0</td><td>39.1</td><td>63.3</td><td>10/22685</td><td>23.9</td><td>46.8</td><td>27.5</td><td>49.7</td></tr><tr><td>BLIP</td><td>VidIL(ours)</td><td>10/7010</td><td>39.6</td><td>64.5</td><td>40.8</td><td>65.2</td><td>10 /22685</td><td>33.3</td><td>59.1</td><td>33.7</td><td>59.5</td></tr><tr><td>BLIP</td><td>Ground Truth</td><td>7010/0</td><td>43.6</td><td>66.2</td><td>43.1</td><td>67.2</td><td>22685/0</td><td>40.1</td><td>66.4</td><td>40.1</td><td>66.6</td></tr><tr><td>ALPRO [25]</td><td>Ground Truth</td><td>140200/0</td><td>32.0</td><td>60.6</td><td>33.9</td><td>60.7</td><td></td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>DRL [56]</td><td>Ground Truth</td><td>180000/0</td><td>54.1</td><td>77.4</td><td>52.9</td><td>78.5</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></table>",
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+        "text": "Another interesting observation is that, compared with the video captioning results in Table 2, we found that the gain of our model over baselines on text-video retrieval is more visible than on captioning. A key factor in performing well on text-video retrieval tasks is to learn a good video-text multi-modal alignment. This result shows that our pseudo labels capture richer video-text alignment that can benefit the retrieval-style downstream task. The N-gram based generation metrics, e.g., BLEU, may not be able to fully reflect the alignment information, due to the variety of semantic levels in video captions. Furthermore, from a data perspective, our video captioner can be viewed as a data augmentation tool which is capable of generating or augmenting any open-domain videolanguage pretraining datasets with minimal human effort. As a result, we can potentially improve video-language pretraining by constructing a cleaner and more diverse video-text corpus. ",
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+        "type": "table",
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+        "table_caption": [
+            "Table 6: Impact of visual tokens and temporal dimension. "
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td colspan=\"2\">Video Representation</td><td>Avg↑ Std↓</td><td></td></tr><tr><td rowspan=\"3\">Visual Token</td><td>Frame Frame+Object</td><td>39.6 40.3</td><td>3.7 2.9</td></tr><tr><td>Frame+Object+Event</td><td>39.9</td><td>2.8</td></tr><tr><td>Frame+Object+Attibute Frame+Object+Event+Attribute</td><td>40.9 40.8</td><td>2.9 2.4</td></tr><tr><td>Temporal</td><td></td><td>Reduce to one frame Reverse temporal order</td><td>38.5 40.7</td><td>2.4 1.7</td></tr></table>",
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+        "table_caption": [
+            "Table 7: Impact of shot selection. #ICE indicates the number of in-context examples in the prompt. Details of in-context example selection are in the Appendix. "
+        ],
+        "table_footnote": [],
+        "table_body": "<table><tr><td>#shot</td><td colspan=\"3\">w/o selection</td><td colspan=\"3\">w/ selection</td></tr><tr><td></td><td>#ICE</td><td>Avg↑</td><td>Std</td><td>#ICE</td><td>Avg↑</td><td>Std↓</td></tr><tr><td>5</td><td>5</td><td>38.4</td><td>2.1</td><td>5</td><td>40.4</td><td>1.2</td></tr><tr><td>10</td><td>10</td><td>41.3</td><td>3.6</td><td>5</td><td>40.8</td><td>2.4</td></tr><tr><td>20</td><td>20</td><td>42.6</td><td>3.3</td><td>5</td><td>42.2</td><td>2.0</td></tr><tr><td>30</td><td>30</td><td>40.0</td><td>2.9</td><td>5</td><td>41.1</td><td>1.9</td></tr></table>",
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+    {
+        "type": "text",
+        "text": "4.6 Ablation Studies ",
+        "text_level": 1,
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+        "text": "We perform comprehensive ablation studies on our few-shot prompt including the impact of different video representation, number of shots and in-context selection. All the ablation results are evaluated on MSVD_QA validation set, and we report the mean and standard deviation of each setting on three sets of randomly sampled shots. For the cases with in-context example selection, we further select 5 examples as in-context examples from the sampled shots, while for the cases without in-context selection, all shots will be feed into the prompt. In Table 6, we show adding visual tokens consistently improves not only the model accuracy but also the model variance. A lower standard deviation indicates that the model is less sensitive to the few-shot sampling. ",
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+        "type": "text",
+        "text": "To further demonstrate the impact of the additional temporal dimension of videos, we perform two ablations on the \"Frame+Object+Event+Attribute\" setting. First, we reduce the number of frame captions and visual tokens to be one9 for each video. We found that the performance drops significantly compared with using the default four frames, which indicates the model’s ability to incorporate information from multiple timestamps. Further, we found that fine-grained temporal modeling is rarely required for performing well on current video-language benchmarks. As shown in the ablation result where we reverse the order of all visual tokens and frame captions, the performance decreased only marginally, which indicates that current benchmarks may not be sufficient in reflecting the benefits from better temporal ordering. ",
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+        "type": "text",
+        "text": "In Table 7, we first show that, with the same context length, namely, 5 in-context examples, in-context example selection significantly increases the performance as well as the robustness. At 10-shot, and 20-shot, directly fitting more shots into the prompt results in better performance. In-context selection achieves slightly lower performance but with significantly better efficiency due to shorter context. Interestingly, at 30-shot, in-context selection with 5 examples outperforms directly adding all 30 shots into the prompt. This is showing that in-context selection can help the model utilize a larger number noisy video examples. Nevertheless, we still observe that the benefit of adding more shots saturated at around 20 to 30 shots, even if with in-context selection. we view this as a remaining challenging on how to make language models benefit from longer contexts. ",
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+    {
+        "type": "text",
+        "text": "5 Conclusions, Limitations and Future Work ",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "This paper proposes VidIL, a few-shot Video-language Learner via Image and Language models. It demonstrates the strong ability of large-scale language models on performing video-to-text tasks when frame features are provided as unified text representations using image-language models. We propose a temporal order aware prompt by decomposing videos into a hierarchical structure, which is able to plug in multiple levels of frame features, along with speech transcripts. Without pretraining on videos, our model outperforms vision-language models learned from large-scale video datasets on a variety of few-shot tasks, such as domain-specific captioning, question answering, and future event prediction. One limitation of using unified textual representation is that we might lose low-level visual features which can be essential for some specific tasks, such as fine-grained spatial visual question answering. We also observe that current video-language benchmarks rarely require explicit temporal tracking on the frames and visual tokens. Future work will focus on leveraging large-scale language models for learning script knowledge from long videos where temporal dynamics are better emphasized. ",
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+    {
+        "type": "text",
+        "text": "6 Broader Impact ",
+        "text_level": 1,
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+    {
+        "type": "text",
+        "text": "An open-domain few-shot video-language learner has a wide range of beneficial applications for society, such as automatically detecting violent or mature content in videos and helping people with vision impairment understand videos. However, since the language model is pretrained on massive internet-scale text data, there might be unexpected output that can have potential negative impact on the society, such as bias against people of a certain gender, race or sexuality. Future work and dedicated collaboration from the community are needed to alleviate the potential negative societal impact of large language models. ",
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+    {
+        "type": "text",
+        "text": "Acknowledgements ",
+        "text_level": 1,
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+        "type": "text",
+        "text": "We thank the anonymous reviewers helpful suggestions. This research is based upon work supported in part by U.S. DARPA AIDA Program No. FA8750-18-2-0014 and U.S. DARPA KAIROS Program Nos. FA8750-19-2-1004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. ",
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+# MASTERING VISUAL CONTINUOUS CONTROL: IMPROVED DATA-AUGMENTED REINFORCEMENT LEARNING
+
+Anonymous authors Paper under double-blind review
+
+# ABSTRACT
+
+We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data augmentation to learn directly from pixels. We introduce several improvements that yield state-of-the-art results on the DeepMind Control Suite. Notably, DrQ-v2 is able to solve complex humanoid locomotion tasks directly from pixel observations, previously unattained by model-free RL. DrQ-v2 is conceptually simple, easy to implement, and provides significantly better computational footprint compared to prior work, with the majority of tasks taking just 8 hours to train on a single GPU. Finally, DrQ-v2’s implementation is publicly released to provide RL practitioners with a strong and computationally efficient baseline.
+
+# 1 INTRODUCTION
+
+Creating sample-efficient continuous control methods that observe high-dimensional images has been a long standing challenge in reinforcement learning (RL) . Over the last three years, the RL community has made significant headway on this problem, improving sample-efficiency significantly. The key insight to solving visual control is the learning of better low-dimensional representations, either through autoencoders (Yarats et al., 2019; Finn et al., 2015), variational inference (Hafner et al., 2018; 2019; Lee et al., 2019), contrastive learning (Srinivas et al., 2020; Yarats et al., 2021a), self-prediction (Schwarzer et al., 2020b), or data augmentations (Yarats et al., 2021b; Laskin et al., 2020). However, current state-of-the-art model-free methods are still limited in three ways. First, they are unable to solve the more challenging visual control problems such as quadruped and humanoid locomotion. Second, they often require significant computational resources, i.e. lengthy training times using distributed multi-GPU infrastructure. Lastly, it is often unclear how different design choices affect overall system performance.
+
+![](images/e84129af41009f6a5914fde2b150e79150a0bba702766235dd57287b087195b6.jpg)  
+Figure $1 : \mathrm { D r Q - v } 2$ demonstrates significantly better sample efficiency and computational footprint compared to state-of-the-art model-free methods for visual continuous control while being conceptually simple and easy to implement. (Left two) Average performance results across 12 challenging tasks from the DeepMind Control Suite (the set of tasks can be seen in Figure 8). (Right two) Performance on the Humanoid Walk task from visual input, previously unsolved by model-free methods. In both cases we report sample complexity and wall-clock time axes for evaluation, with time being measured on a single GPU machine and using official implementations for each method.
+
+In this paper we present DrQ-v2, a simple model-free algorithm that builds on the idea of using data augmentations (Yarats et al., 2021b; Laskin et al., 2020) to solve hard visual control problems. Most notably, it is the first model-free method that solves complex humanoid tasks directly from pixels. Compared to previous state-of-the-art model-free methods, DrQ-v2 provides significant improvements in sample efficiency across tasks from the DeepMind Control Suite (Tassa et al., 2018). Conceptually simple, DrQ-v2 is also computationally efficient, which allows solving most tasks in DeepMind Control Suite in just 8 hours on a single GPU (see Figure 1). Recently, a model-based method, DreamerV2 (Hafner et al., 2020) was also shown to solve visual continuous control problems and it was first to solve the humanoid locomotion problem from pixels. While our model-free $\mathrm { D r Q - v } 2$ matches DreamerV2 in terms sample efficiency and performance, it does so $4 \times$ faster in terms of wall-clock time to train. We believe this makes DrQ-v2 a more accessible approach to support research in visual continuous control and it reinforces the question on whether model-free or model-based is the more suitable approach to solve this type of tasks.
+
+$\mathrm { D r Q - v } 2$ , which is detailed in Section 3, improves upon DrQ (Yarats et al., 2021b) by making several algorithmic changes: (i) switching the base RL algorithm from SAC (Haarnoja et al., 2018a) to DDPG (Lillicrap et al., 2015a) with clipped double Q-learning from TD3 (Fujimoto et al., 2018), (ii) this allows us straightforwardly incorporating multi-step return, (iii) adding bilinear interpolation to the random shift image augmentation, (iv) introducing an exploration schedule, (v) selecting better hyper-parameters including a larger capacity of the replay buffer. A careful ablation study of these design choices is presented in Section 4.4. Furthermore, we re-examine the original implementation of DrQ and identify several computational bottlenecks such as replay buffer management, data augmentation processing, batch size, and frequency of learning updates (see Section 3.2). To remedy these, we have developed a new implementation that both achieves better performance and trains around 3.5 times faster with respect to wall-clock time than the previous implementation on the same hardware with an increase in environment frame throughput (FPS) from 28 to 96 (i.e., it takes $1 0 ^ { 6 } / 9 6 / 3 6 0 0 \approx 2 . 9$ hours to train for 1M environment steps). DrQ-v2’s implementation is available at https://anonymous.4open.science/r/drqv2.
+
+# 2 BACKGROUND
+
+# 2.1 REINFORCEMENT LEARNING FROM IMAGES
+
+We formulate image-based control as an infinite-horizon Markov Decision Process (MDP) (Bellman, 1957). Generally, in such a setting, an image rendering of the system is not sufficient to perfectly describe the system’s underlying state. To this end and per common practice (Mnih et al., 2013), we approximate the current state of the system by stacking three consecutive prior observations. With this in mind, such MDP can be described as a tuple $( \mathcal { X } , \mathcal { A } , P , R , \gamma , d _ { 0 } )$ , where $\mathcal { X }$ is the state space (a three-stack of image observations), $\mathcal { A }$ is the action space, $P : \mathcal { X } \times \mathcal { A }  \Delta ( \mathcal { X } )$ is the transition function1 that defines a probability distribution over the next state given the current state and action, $R : \mathcal { X } \times \mathcal { A }  [ 0 , 1 ]$ is the reward function, $\gamma \in [ 0 , 1 )$ is a discount factor, and $d _ { 0 } \in \Delta ( { \mathcal { X } } )$ is the distribution of the initial state $\scriptstyle { \mathbf { { \mathit { x } } } } _ { 0 }$ . The goal is to find a policy $\pi : \mathcal { X }  \Delta ( \mathcal { A } )$ that maximizes the expected discounted sum of rewards $\mathbb { E } _ { \pi } \big [ \sum _ { t = 0 } ^ { \infty } \gamma ^ { t } r _ { t } \big ]$ , where $\mathbf { x } _ { 0 } \sim d _ { 0 }$ , and $\forall t$ we have $\mathbf { \boldsymbol { a } } _ { t } \sim \pi ( \cdot | \mathbf { \boldsymbol { x } } _ { t } )$ $\mathbf { \boldsymbol { x } } _ { t + 1 } \sim P ( \cdot | \mathbf { \boldsymbol { x } } _ { t } , \mathbf { \boldsymbol { a } } _ { t } )$ , and $r _ { t } = R ( \pmb { x } _ { t } , \pmb { a } _ { t } ) \overline { { \mathbf { \phi } } }$ .
+
+# 2.2 DEEP DETERMINISTIC POLICY GRADIENT
+
+Deep Deterministic Policy Gradient (DDPG) (Lillicrap et al., 2015a) is an actor-critic algorithm for continuous control that concurrently learns a Q-function $Q _ { \theta }$ and a deterministic policy $\pi _ { \phi }$ . For this, DDPG uses Q-learning (Watkins and Dayan, 1992) to learn $Q _ { \theta }$ by minimizing the one-step Bellman residual $J _ { \theta } ( \mathcal { D } ) = \mathbb { E } _ { ( \pmb { x } _ { t } , \underline { { a } } _ { t } , r _ { t } , \pmb { x } _ { t + 1 } ) \sim \mathcal { D } } [ \big ( Q _ { \theta } ( \pmb { x } _ { t } , \underline { { a } } _ { t } ) _ {  } r _ { t } - \underline { { \gamma Q _ { \theta } } } ( \pmb { x } _ { t + 1 } , \pi _ { \phi } ( \pmb { x } _ { t + 1 } ) ) ^ { 2 } \big ]$ . The policy $\pi _ { \phi }$ is learned by employing Deterministic Policy Gradient (DPG) (Silver et al., 2014) and maximizing $J _ { \phi } ( \mathcal { D } ) = \mathbb { E } _ { \pmb { x } _ { t } \sim \mathcal { D } } [ Q _ { \theta } ( \pmb { x } _ { t } , \pi _ { \phi } ( \pmb { x } _ { t } ) ) ]$ , so $\pi _ { \phi } ( \pmb { x } _ { t } )$ approximates argmax $\mathbf { \Sigma } _ { \alpha } Q _ { \theta } ( \pmb { x } _ { t } , \pmb { a } )$ . Here, $\mathcal { D }$ is a replay buffer of environment transitions and $\bar { \theta }$ is an exponential moving average of the weights. DDPG is amenable to incorporate $n$ -step returns (Watkins, 1989; eng and Williams, 1996) when estimating TD error beyond a single step (Barth-Maron et al., 2018). In practice, $n$ -step returns allow for faster reward propagation and has been previously used in policy gradient and Q-learning methods (Mnih et al., 2016b; Barth-Maron et al., 2018; Hessel et al., 2017).
+
+![](images/549fe695324b96cc93df2881f3296d0b6333a6e41e473a27a33b6bfc84cf2f08.jpg)  
+Figure 2: (Left): $\mathrm { D r Q - v } 2$ is an off-policy actor-critic algorithm for image-based RL. It alleviates encoder overfitting by applying random shift augmentation to pixel observations sampled from the replay buffer. (Right): Examples of walking and standing behaviors learned by $\mathrm { D r Q - v } 2$ for a complex humanoid agent from DMC (Tassa et al., 2018) with 21 and 54 dimensional action and state spaces, respectively. DrQ-v2 does not have access to the internal state of the environment, only observing three consecutive pixel frames at a time. Despite this imperfect observational channel, our agent still manages to solve the tasks. To the best of our knowledge, this is the first successful demonstration by a model-free method, using pixel-based inputs of these tasks.
+
+# 2.3 DATA AUGMENTATION IN REINFORCEMENT LEARNING
+
+Recently, it has been shown that data augmentation techniques, commonplace in Computer Vision, are also important for achieving the state-of-the-art performance in image-based RL (Yarats et al., 2021b; Laskin et al., 2020). For example, the state-of-the-art algorithm for visual RL, DrQ (Yarats et al., 2021b) builds on top of Soft Actor-Critic (Haarnoja et al., 2018a), a model-free actor-critic algorithm, by adding a convolutional encoder and data augmentation in the form of random shifts. The use of such data augmentations now forms an essential component of several recent visual RL algorithms (Srinivas et al., 2020; Raileanu et al., 2020; Yarats et al., 2021a; Stooke et al., 2020; Hansen and Wang, 2021; Schwarzer et al., 2020b).
+
+# 3 DRQ-V2: IMPROVED DATA-AUGMENTED REINFORCEMENT LEARNING
+
+In this section, we describe DrQ-v2, a simple model-free actor-critic RL algorithm for image-based continuous control, that builds upon DrQ.
+
+# 3.1 ALGORITHMIC DETAILS
+
+Image Augmentation As in DrQ we apply random shifts image augmentation to pixel observations of the environment. In the settings of visual continuous control by DMC, this augmentation can be instantiated by first padding each side of $8 4 \times 8 4$ observation rendering by 4 pixels (by repeating boundary pixels), and then selecting a random $8 4 \times 8 4$ crop, yielding the original image shifted by $\pm 4$ pixels. We also find it useful to apply bilinear interpolation on top of the shifted image (i.e, we replace each pixel value with the average of the four nearest pixel values). In our experiments, this modification provides an additional performance boost across the board.
+
+Image Encoder The augmented image observation is then embedded into a low-dimensional latent vector by applying a convolutional encoder. We use the same encoder architecture as in DrQ, which first was introduced introduced in SAC-AE (Yarats et al., 2019). This process can be succinctly summarized as $\pmb { h } = f _ { \xi } ( \mathrm { a u g } ( \pmb { x } ) )$ , where $f _ { \xi }$ is the encoder, aug is the random shifts augmentation, and $_ { \textbf { \em x } }$ is the original image observation.
+
+Actor-Critic Algorithm We use DDPG (Lillicrap et al., 2015a) as a backbone actor-critic RL algorithm and, similarly to Barth-Maron et al. (2018), augment it with $n$ -step returns to estimate TD error. This results into faster reward propagation and overall learning progress (Mnih et al., 2016a).
+
+While some methods (Hafner et al., 2020) employ more sophisticated techniques such as $\mathrm { T D } ( \lambda )$ or Retrace $( \lambda )$ (Munos et al., 2016), they are often computationally demanding when $n$ is large. We find that using simple $n$ -step returns, without an importance sampling correction, strikes a good balance between performance and efficiency. We also employ clipped double Q-learning (Fujimoto et al., 2018) to reduce overestimation bias in the target value. Practically, this requires training two Qfunctions $Q _ { \theta _ { 1 } }$ and $Q _ { \theta _ { 2 } }$ . For this, we sample a mini-batch of transitions $\tau = ( \mathbf { x } _ { t } , \mathbf { a } _ { t } , r _ { t : t + n - 1 } , \mathbf { x } _ { t + n } )$ from the replay buffer $\mathcal { D }$ and compute the following two losses:
+
+$$
+\begin{array} { r } { \mathcal { L } _ { \boldsymbol { \theta } _ { k } , \boldsymbol { \xi } } ( \mathcal { D } ) = \mathbb { E } _ { \tau \sim \mathcal { D } } \big [ ( Q _ { \boldsymbol { \theta } _ { k } } ( h _ { t } , \boldsymbol { a } _ { t } ) - y ) ^ { 2 } \big ] \quad \forall k \in \{ 1 , 2 \} , } \end{array}
+$$
+
+with the TD target $y$ defined as:
+
+$$
+y = \sum _ { i = 0 } ^ { n - 1 } \gamma ^ { i } r _ { t + i } + \gamma ^ { n } \operatorname* { m i n } _ { k = 1 , 2 } Q _ { \bar { \theta } _ { k } } ( h _ { t + n } , \mathbf { a } _ { t + n } ) ,
+$$
+
+where $\pmb { h } _ { t } = f _ { \xi } ( \mathrm { a u g } ( \pmb { x } _ { t } ) )$ , ${ h _ { t + n } } = f _ { \xi } ( \mathrm { a u g } ( { { \bf x } _ { t + n } } ) )$ , $a _ { t + n } = \pi _ { \phi } ( h _ { t + n } ) + \epsilon , \bar { \theta } _ { 1 }$ and ${ \bar { \theta } _ { 2 } }$ are the slowmoving weights for the Q target networks. We note, that in contrast to DrQ, we do not employ a target network for the encoder $f _ { \xi }$ and always use the most recent weights $\xi$ to embed $\mathbf { \Delta } _ { \mathbf { \mathcal { X } } _ { t } }$ and $\mathbf { \Delta } _ { \mathbf { x } _ { t + n } }$ . The exploration noise $\epsilon$ is sampled from $\mathrm { c l i p } ( \mathcal { N } ( 0 , \sigma ^ { 2 } ) , - c , c )$ similar to TD3 (Fujimoto et al., 2018), with the exception of decaying $\sigma$ , which we describe below. Finally, we train the deterministic actor $\pi _ { \phi }$ using DPG with the following loss:
+
+$$
+\mathcal { L } _ { \phi } ( \mathcal { D } ) = - \mathbb { E } _ { x _ { t } \sim \mathcal { D } } \big [ \operatorname* { m i n } _ { k = 1 , 2 } Q _ { \theta _ { k } } ( h _ { t } , \pmb { a } _ { t } ) \big ] ,
+$$
+
+where $\pmb { h } _ { t } = f _ { \xi } ( \mathrm { a u g } ( \pmb { x } _ { t } ) )$ , ${ \pmb a } _ { t } = \pi _ { \phi } ( { \pmb h } _ { t } ) + \epsilon$ , and $\epsilon \sim \mathrm { c l i p } ( \mathcal { N } ( 0 , \sigma ^ { 2 } ) , - c , c )$ . Similar to $_ \mathrm { D r Q }$ , we do not use actor’s gradients to update the encoder’s parameters $\xi$ .
+
+Scheduled Exploration Noise Empirically, we observe that it is helpful to have different levels of exploration at different stages of learning. At the beginning of training we want the agent to be more stochastic and explore the environment more effectively, while at the later stages of training, when the agent has already identified promising behaviors, it is better to be more deterministic and master those behaviors. Similar to Amos et al. (2020), we instantiate this idea by using linear decay $\sigma ( t )$ for the variance $\sigma ^ { 2 }$ of the exploration noise defined as:
+
+$$
+\sigma ( t ) = \sigma _ { \mathrm { i n i t } } + ( 1 - \mathrm { m i n } ( \frac { t } { T } , 1 ) ) ( \sigma _ { \mathrm { f i n a l } } - \sigma _ { \mathrm { i n i t } } ) ,
+$$
+
+where $\sigma _ { \mathrm { i n i t } }$ and $\sigma _ { \mathrm { f i n a l } }$ are the initial and final values for standard deviation, and $T$ is the decay horizon.
+
+Key Hyper-Parameters We conduct an extensive hyper-parameter search and identify several hyper-parameter changes compared to DrQ. The three most important hyper-parameters are: (i) the size of the replay buffer, (ii) mini-batch size, and (iii) learning rate. Specifically, we use a 10 times larger replay buffer than DrQ. We also use a smaller mini-batch size of 256 without any noticeable performance degradation. This is in contrast to CURL (Srinivas et al., 2020) and $\mathrm { D r Q }$ (Yarats et al., 2021b) that both use a larger batch size of 512 to attain more stable training in the expense of computational efficiency. Finally, we find that using smaller learning rate of $1 \times 1 0 ^ { - 4 }$ , rather than DrQ’s learning rate of $\mathrm { i \times 1 0 ^ { - 3 } }$ , results into more stable training without any loss in learning speed.
+
+# 3.2 IMPLEMENTATION DETAILS
+
+Faster Image Augmentation We replace DrQ’s random shifts augmentation (i.e., kornia.augmentation.RandomCrop) by a custom implementation that uses flowfield image sampling provided in PyTorch (i.e., grid_sample). This is done for two reasons. First, we noticed that Kornia’s implementation does not fully utilize GPU pipelining since it has some intermediate CPU to GPU data transferring which breaks the computational flow. Second, using grid_sample allows straightforward addition of bilinear interpolation. Our custom random shifts augmentation improves training throughput by a factor of 2.
+
+Faster Replay Buffer Another computational bottleneck of $\mathrm { D r Q }$ was the replay buffer. The specific implementation had poor memory management which resulted in slow CPU to GPU data transfer, which also restricted the number of image-based transitions that could be stored. We reimplemented the replay buffer to address these issues which led to a ten-fold increase in storage capacity and faster data transfer. More details are available in our open-source release. We note that the improved training speed of DrQ-v2 was key to solving humanoid tasks as it enabled much faster experimentation.
+
+# 4 EXPERIMENTS
+
+In this section we provide empirical evaluation of $\mathrm { D r Q - v } 2$ on an extensive set of visual continuous control tasks from DMC (Tassa et al., 2018). We first present comparison to prior methods, both model-free and model-based, in terms of sample efficiency and wall-clock time. We then present a large scale ablation study that guided the final version of DrQ-v2.
+
+# 4.1 SETUP
+
+Environments We consider a set of MuJoCo tasks (Todorov et al., 2012) provided by DMC (Tassa et al., 2018), a widely used benchmark for continous control. DMC offers environments of various difficulty, ranging from the simple control problems such as the single degree of freedom (DOF) pendulum and cartpool, to the control of complex multi-joint bodies such as the humanoid (21 DOF). We consider learning from pixels. In this setting, environment observations are stacks of 3 consecutive RGB images of size $8 4 \times 8 4$ , stacked along the channel dimension to enable inference of dynamic information like velocity and acceleration. In total, we consider 24 different tasks, which we group into three buckets, easy, medium, and hard, according to the sample complexity to reach near-optimal performance (see Appendix B). Our motivation for this is to encourage RL practitioners to focus on the medium and hard tasks and stop using the easy tasks for evaluation, as they are mostly solved at this point and may no longer provide any valuable signal in comparing different methods.
+
+Training Details For all tasks in the suite an episode corresponds to 1000 steps, where a per-step reward is in the unit interval [0, 1]. This upper bounds the episode return to 1000 making it easier to compute aggregated performance measures across tasks. To facilitate fair wall-clock time comparison all algorithms are trained on the same hardware (i.e., a single NVIDIA V100 GPU machine) and evaluated with the same periodicity of 20000 environment steps. Each evaluation query averages episode returns over 10 episodes. Per common practice (Hafner et al., 2019), we employ action repeat of 2 and measure sample complexity in the environment steps, rather than the actor steps. In all the figures we plot the mean performance over 10 seeds together with the shaded regions which represent $9 \hat { 5 } \%$ confidence intervals. A full list of hyper-parameters can be found in Appendix E.
+
+Comparison Axes In many real-world applications, taking a step in the environment incurs significant computational cost making sample efficiency a critical feature of an RL algorithm. It is hence important to compare RL algorithms in terms of their sample efficiency. We facilitate this comparison by computing an algorithm’s performance measured by episode return with respect to environment steps. On the other end, striving low sample complexity often comes at the cost of a poor computational efficiency. Unfortunately, recent deep RL literature has paid very little attention to this important axis, which has led to skyrocketing hardware requirements. Such a trend has made it virtually impossible for an RL practitioner with modest hardware capacity to contribute to advancements in image-based RL, leaving research in this area to a few well-equipped labs. To democratize research in visual RL, we additionally propose to compare the agents in terms of wall-clock training time given the same single GPU hardware. We note that it is possible to adapt DrQ-v2 to a distributed setup, as has been done for DDPG in prior work (Barth-Maron et al., 2018; Hoffman et al., 2020).
+
+# 4.2 COMPARISON TO MODEL-FREE METHODS
+
+Baselines We compare our method to several state-of-the-art model-free algorithms for visual RL including CURL (Srinivas et al., 2020), DrQ (Yarats et al., 2021b), and vanilla SAC (Haarnoja et al., 2018a) augmented with the convolutional encoder from SAC-AE (Yarats et al., 2019). Vanilla SAC is a weak baseline and only included as a ground point to showcase the recent progress in visual RL.
+
+![](images/ecbef5ca3f3d3542fd76d49b66dfb726dde336b060b81096054addb1a0048ea7.jpg)  
+Figure 3: We compare $\mathrm { D r Q - v } 2$ on a subset of continuous control tasks that offer various challenges, including complex dynamics, sparse rewards, hard exploration, and more. (a) $\mathrm { D r Q - v } 2$ demonstrates favorable sample efficiency and comfortably outperforms leading model-free baselines, as well as requiring less wall-clock training image (b).
+
+Sample Efficiency Axis We present results on several medium and hard tasks in Figure 3a. Full results can be found in Appendix (Figure 6, Figure 8, and Figure 10). Our empirical study reveals that $\mathrm { D r Q - v } 2$ outperforms prior model-free methods in terms of sample efficiency across the three benchmarks with different levels of difficulty. Importantly, DrQ-v2’s advantage is more pronounced on harder tasks (i.e., acrobot, quadruped, and humanoid), where exploration is especially challenging. Finally, DrQ-v2 solves the DMC humanoid locomotion tasks directly from pixels, which, to the best of our knowledge, is the first successful demonstration of such feat by a model-free method.
+
+Compute Efficiency Axis To facilitate a fair comparison in terms of sheer wall-clock training time, besides employee the identical training protocol (see Section 4.1), we also use the same mini-batch size of 256 for each agent. In Figure 13, we evaluate $\mathrm { D r Q - v } 2$ on a subset of DMC tasks for the sake of brevity only, and note that the demonstrated results can be easily extrapolated to the other tasks given the linear dependency between training time and sample complexity. In our benchmarks, $\mathrm { D r Q - v } 2$ is able to achieve a throughput of 96 FPS, which favorably compares to DrQ’s 28 FPS (a $3 . 4 \times$ increase), and CURL’s 16 FPS (a $6 \times$ increase) throughputs. Practically, $\mathrm { D r Q - v } 2$ solves easy, medium, and hard tasks within 2.9, 8.6, and 86 hours respectively. Full results can be found in Appendix (Figure 7, Figure 9, and Figure 11).
+
+# 4.3 COMPARISON TO MODEL-BASED METHODS
+
+Baseline To see how $\mathrm { D r Q - v } 2$ stacks up against model-based methods, which tend to achieve better sample complexity in expense of a larger computational footprint, we also compare to recent and unpublished2 improvements to Dreamer-v2 (Hafner et al., 2020), a leading model-based approach for visual continuous control. The recent update shows that the model-based approach can solve the DMC humanoid tasks directly from pixel inputs. The open-source implementation of Dreamer-v2 (https://github.com/danijar/dreamerv2) only provides learning curves for Humanoid Walk. For this reason we run their code to obtain results on other DMC tasks. To limit hardware requirements of compute-expensive Dreamer-v2, we only run it on a subset of 12 out of 24 considered tasks. This subset, however, overlaps with all the three (i.e. easy, medium, and hard) benchmarks.
+
+![](images/545d212f4784001ae204bbbd2da7b19024aaeec84e241d0466ac5d3246eb06bc.jpg)  
+Figure 4: Model-based Dreamer-v2 needs to train a world model and thus performs more computations during training than model-free DrQ-v2. Still, (a) $\mathrm { D r Q - v } 2$ is able to match Dreamer-v2’s sample efficiency, while $\mathbf { ( b ) }$ requiring much less wall-clock training time.
+
+Sample Efficiency Axis Our empirical study in Figure 4a reveals that in many cases, DrQ-v2, despite being a model-free method, can rival sample efficiency of state-of-the-art model-based Dreamer-v2. We note, however, that on several tasks (for example Acrobot Swingup) Dreamer-v2 outperforms DrQ-v2. We leave investigation of such discrepancy for future work. Full results are provided in Appendix D (Figure 12).
+
+Compute Efficiency Axis A different picture emerges if comparison is done with respect to wallclock training time. Dreamer-v2, being a model-based method, performs significantly more floating point operations to reach its sample efficiency. In our benchmarks, Dreamer-v2 records a throughput of 24 FPS, which is $4 \times$ less than DrQ-v2’s throughput of 96 FPS, measured on the same hardware. In Figure 4b we plot learning curves against wall-clock time and observe that $\mathrm { D r Q - v } 2$ takes less time to solve the tasks. Full results can be found in Appendix (Figure 13).
+
+# 4.4 ABLATION STUDY
+
+In this section we present an extensive ablation study that guided us to the final version of $\mathrm { D r Q - v } 2$ Here, for brevity we only discuss experiments that were most impactful and omit others that did not pan out. For computational reasons, we only ablate on 3 different control tasks of various difficulty levels. Our findings are summarized in Figure 5 and detailed below.
+
+Switching from SAC to DDPG DrQ (Yarats et al., 2021b) leverages SAC (Haarnoja et al., 2018a) as the backbone RL algorithm. While it has been demonstrated by many works, including the original manuscripts (Haarnoja et al., 2018a;b) that SAC is superior to DDPG (Lillicrap et al., 2015b), our careful examination identifies two shortcomings that preclude SAC (within DrQ) to solve hard exploration-wise image-based tasks. First, the automatic entropy adjustment strategy, introduced in Haarnoja et al. (2018b), is inadequate and in some cases leads to a premature entropy collapse.
+
+![](images/ba1a4435887f6483f6e5539af32ee4c9abf61350f7e4d286d35d2b17585b6381.jpg)  
+(a) DrQ (dotted silver) relies on SAC as a base RL algorithm. Replacing SAC with DDPG results in a significant performance gain (blue).
+
+![](images/8cefa7b04095a5ad9cc5e618bf568a2e8cb599d743766c5d5f19dd90a7f61e8b.jpg)  
+(b) DDPG straightforwardly incorporates $n$ -step returns, a critical tool for exploration. We observe that the 3 (blue) and 5 (red) steps variants provide additional improvements to the previous version that uses single step TD-targets (silver). Going forward, we adopt 3-step returns (blue).
+
+![](images/7344d8d594db9770f16e401999ae3c88c9529605098182fbc253dd13972aed74.jpg)  
+(c) Increasing the size of the replay buffer (B) improves performance, over the original $1 0 ^ { 5 }$ used by DrQ (silver). Going forward, we use a buffer size of $1 \dot { 0 } ^ { 6 }$ (red).
+
+(d) Finally, a decaying schedule for the variance of the exploration noise (blue) helps on hard exploration tasks, versus the fixed variance variant (silver).
+
+![](images/9ac2122386f89d3dc6ae3ea32bbd49484f830769555b1e994b96ad2c05cfd549.jpg)  
+Figure 5: An ablation study that led us to the final version of $\mathrm { D r Q - v } 2$ . We incrementally show each of the four key improvements to $\mathrm { D r Q }$ that collectively form DrQ-v2. The silver dotted curves in the first row show the original DrQ. In subsequent rows they show progressive improvements, using the optimal choice from the previous rows (i.e., the silver curve in the third row shows DrQ with a DDPG base RL algorithm and 3-step returns). The red and blue curves show the effect of individual modifications. In the last row the blue curve corresponds to DrQ-v2.
+
+This prevents the agent from finding more optimal behaviors due to the insufficient exploration. In Figure 5a, we empirically verify our intuition and, indeed, observe that DDPG demonstrates better exploration properties than SAC. Here, DDPG uses constant $\sigma = 0 . 2$ for the exploration noise.
+
+N-step Returns The second issue concerns the inability of soft Q-learning to incorporate $n$ -step returns to estimate TD error in a straightforward manner. The reason for this is that computing a target value for soft Q-function requires estimating per-step entropy of the policy, which is challenging to do for large $n$ in the off-policy regime. In contrast, DDPG does not require estimating per-step entropy to compute targets and is more amenable for $n$ -step returns. In Figure 5b we demonstrate that estimating TD error with $n$ -step returns improves sample efficiency over vanilla DDPG. We select 3-step returns as a sensible choice for our method.
+
+Replay Buffer Size We hypothesize that a larger replay buffer plays an important role in circumventing the catastrophic forgetting problem (Fedus et al., 2020). This issue is especially prominent in tasks with more diverse initial state distributions (i.e., reacher or humanoid tasks), where the vast variety of possible behaviors requires significantly larger memory. We confirm this intuition by ablating the size of the replay buffer in Figure 5c, where we observe that a buffer size of 1M helps to improve performance on Reacher Hard considerably.
+
+Scheduled Exploration Noise Finally, we demonstrate that it is useful to decay the variance of the exploration noise over the course of training according to Equation (3). In Figure 5d, we compare two versions of our algorithm, where the first variant uses a fixed standard deviation of $\sigma = 0 . 2$ , while the second variant employes the decaying schedule $\sigma ( t )$ , with parameters $\sigma _ { \mathrm { i n i t } } = 1 . 0$ , $\sigma _ { \mathrm { f i n a l } } = 0 . 1$ , and $T = 5 0 0 0 0 0$ . Having the exploration noise to decay linearly over time turns out to be helpful and provide an additional performance boost, which was especially useful for solving humanoid tasks.
+
+# 5 RELATED WORK
+
+Visual Reinforcement Learning Successes of visual representation learning in computer vision (Vincent et al., 2008; Doersch et al., 2015; Wang and Gupta, 2015; Noroozi and Favaro, 2016; Zhang et al., 2017; Gidaris et al., 2018) has inspired successes in visual RL, where coherent representations are learned alongside RL. Works such as SAC-AE (Yarats et al., 2019), PlaNet (Hafner et al., 2018), and SLAC (Lee et al., 2019), demonstrated how auto-encoders (Finn et al., 2015) could improve visual RL. Following this, other self-supervised objectives such as contrastive learning in CURL (Srinivas et al., 2020) and ATC (Stooke et al., 2020), self-prediction in SPR (Schwarzer et al., 2020a), contrastive cluster assignment in Proto-RL (Yarats et al., 2021a), and augmented data in DrQ (Yarats et al., 2021b) and RAD (Laskin et al., 2020), have significantly bridged the gap between state-based and image-based RL. Future prediction objectives (Hafner et al., 2018; 2019; Yan et al., 2020; Finn et al., 2015; Pinto et al., 2016; Agrawal et al., 2016) and other auxiliary objectives (Jaderberg et al., 2016; Zhan et al., 2020; Young et al., 2020; Chen et al., 2020) have shown improvements on a variety of problems ranging from gameplay, continuous control, and robotics. In the context of visual control settings, clever use of augmented data (Yarats et al., 2021b; Laskin et al., 2020) currently produces state-of-the-art results on visual tasks from DMC (Tassa et al., 2018).
+
+Humanoid Control The humanoid control problem first presented in Tassa et al. (2012), has been studied as one of the hardest control problems due to its large state and action spaces. The earliest solutions to this problem use ideas in model-based optimal control to generate policies given an accurate model of the humanoid . Subsequent works in RL have shown that model-free policies can solve the humanoid control problem given access to proprioceptive state observations. However, solving such a problem from visual observations has been a challenging problem, with leading RL algorithms making little progress to solve the task (Tassa et al., 2018). Recently, Hafner et al. (2020) was able to solve this problem through a model-based technique in around 30M environment steps and 340 hours of training on a single GPU machine. DrQ-v2, presented in this paper, marks the first model-free RL method that can solve humanoid control from visual observations, taking also around 30M steps and 86 hours of training on the same hardware.
+
+# 6 CONCLUSION
+
+We have introduced a conceptually simple model-free actor-critic RL agent for image-based continuous control – DrQ-v2. Our method provides significantly better computational footprint and masters tasks from DMC directly from pixels, most notably the humanoid locomotion tasks that
+
+were previously unsolved by model-free approaches. To support our empirical results and inspire further research in visual RL we provide an efficient PyTorch implementation of $\mathrm { D r Q - v } 2$ at https://anonymous.4open.science/r/drqv2.
+
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