Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach

## Abstract

Abstract In the rapidly expanding domain of web video content, the task of text-video retrieval has become increasingly critical, bridging the semantic gap between textual queries and video data.
This paper introduces a novel data-centric approach, Generalized Query Expansion (GQE), to address the inherent information imbalance between text and video, enhancing the effectiveness of text-video retrieval systems.
Unlike traditional model-centric methods that focus on designing intricate cross-modal interaction mechanisms, GQE aims to expand the text queries associated with videos both during training and testing phases.
By adaptively segmenting videos into short clips and employing zero-shot captioning, GQE enriches the training dataset with comprehensive scene descriptions, effectively bridging the data imbalance gap.
Furthermore, during retrieval, GQE utilizes Large Language Models (LLM) to generate a diverse set of queries and a query selection module to filter these queries based on relevance and diversity, thus optimizing retrieval performance while reducing computational overhead.
Our contributions include a detailed examination of the information imbalance challenge, a novel approach to query expansion in video-text datasets, and the introduction of a query selection strategy that enhances retrieval accuracy without increasing computational costs.
GQE achieves state-of-the-art performance on several benchmarks, including MSR-VTT, MSVD, LSMDC, and VATEX, demonstrating the effectiveness of addressing text-video retrieval from a data-centric perspective.

## 1 Introduction

The exponential growth of web videos has intensified the demand for text-video retrieval.
As a fundamental task in vision-language learning, text-video retrieval aims to retrieve videos that are semantically similar to a text query, allowing users to quickly find target information.
Typically, text-video retrieval models are built by training a similarity function between video and text.
This is often achieved by first encoding a text and a video separately into a joint latent space and then apply a distance metric such as the cosine similarity between the text and video embeddings.

It is said that “a picture is worth a thousand words".
This information imbalance is even more obvious between video and text.
A text generally cannot fully capture the entire contents of a video.
Instead, it either describes certain frames/regions of the video, or roughly describes the general content of the video at a coarse level.
Therefore, a video corresponds to multiple possible texts.

Figure: Figure 1: Motivation. An example video and its text annotations from MSR-VTT [] dataset. Videos contain much richer information than text. A video can be described by numerous possible text queries, while some of them are missing in the dataset.
Refer to caption: x1.png

When it comes to the text-video retrieval task, the information imbalance manifests in two ways.
Firstly, it affects model training.
Training a text-video retrieval model on video-text datasets under these imbalanced conditions can be considered as training with incomplete labels.
This biases the model toward specific granularities or scenes emphasized by text queries in the training data.
In Fig. 1, a sample video from the MSR-VTT dataset [] shows that descriptions for some scenes of a video might be missing in the dataset.
The third scene can be described as “A noble girl is surrounded by two girls floating in the air".
The cross-modal matching that should exist there is missing, resulting in incomplete alignment.
This issue could potentially lead to retrieval failures with queries like “A noble girl is…".
We notice that popular video-text datasets are annotated without particular consideration about the completeness of the text queries, especially scene coverage.
For text-video retrieval, it would be favorable to have a video-text dataset with complete text queries.

Secondly, the imbalance issue undermines the retrieval process (testing).
With the one-to-many nature, given the same video, successful retrieval can be achieved by varying texts over different granularities or emphasizing different scenes or details of a scene.
Consider the text-video retrieval task as a dart game: a text query is like a dart, and a video is like a dartboard.
The traditional retrieval process is like throwing a dart (query) to see whether it can correctly hit the target field (video).
There are multiple positions in the same field of the dartboard (video) that can be targeted by darts (queries), all scoring equally.
Accordingly, can we use multiple queries during retrieval to leverage the imbalanced one-to-many property?

Previous methods address the information imbalance by designing cross-modal interaction mechanisms at different granularities, such as sentence-frame level [] and word-frame level [], or designing more dedicated model architectures, such as teacher-student [].
The core idea is to encourage the incomplete text queries to capture the relevant part of the video.
We regard these methods as model-centric approaches, as they primarily concentrate on model architecture design.
Although they have produced promising results, information imbalance in the data continues to limit model performance.

In this paper, we propose an approach, called Generalized Query Expansion (GQE), from a data-centric perspective, which is orthogonal to the model-centric approaches.
Specifically, GQE addresses the imbalance issue via expanding the text queries in both training and testing.

Firstly, GQE expands the text queries in the training dataset to bridge the data imbalance gap.
Intuitively, captioning models can generate additional queries.
Previous works [] used a zero-shot video captioning model to generate captions.
Note that our mission is to complete the text queries such that they can cover most scenes of the video.
Therefore, we first adaptively segment the video into short clips according to its content.
Then we apply zero-shot captioning for each scene, bringing novel information as the captioning model is pre-trained on large-scale image-text data.
Compared to vanilla video captioning, this approach balances the distribution of text queries across time spans.

Secondly, GQE expands the text queries during retrieval, including a query generation step and a query selection step.
In query generation, we propose leveraging advanced Large Language Models (LLMs) to obtain an extensive set of expanded queries.
Although query expansion  [, ] (e.g, using synonyms) seems to be a traditional technique, we emphasize that how to utilize LLMs for query expansion is still under-explored.
In popular CLIP-based [] text-video retrieval models, LLMs can be regarded as an enhancement of the original text encoder.
In addition, we introduce a novel query selection step based on the expanded queries.
Although simply involving these queries brings considerable improvement, it also induces higher computation cost and the risk of modifying the sought content.
In the metaphor of a dart game, a premise of successful retrieval is that the multiple darts (queries) should be in *different* positions within the *same* field of the dartboard (video).
Therefore, we devise a query selection module that selects effective queries among the candidates by considering relevance and diversity.
It not only lowers computational costs but also enhances performance, outperforming the use of the entire set of generated queries.

Our main contributions are as follows:
(i) We investigated the information imbalance problem in the text-video retrieval task and addressed this problem from a data-centric perspective.
(ii) We designed a comprehensive video captioning approach to expand the text queries of videos in the training dataset.
(iii) We introduced an effective query expansion strategy for text-video retrieval, enhancing retrieval performance while limiting computational costs.
(iv) The proposed approach, called GQE, achieves state-of-the-art results across standard benchmarks, including MSR-VTT [], MSVD [], LSMDC [] and VATEX [].

## 2 Related Work

### 2.1 Vision-Language Models

Vision-language learning is a fundamental task in multimodal field.
In the early stage, efforts primarily focused on specific downstream cross-modal tasks, such as image captioning [, , ], visual question answering (VQA) [], and text-image retrieval [], etc.
These tasks typically rely on pre-trained models from single modalities.
With the rise of multimodal data, approaches like CLIP [] and ALIGN [] have emerged, demonstrating promising results on numerous downstream tasks [, , ] by pre-training on large-scale image-text pair datasets [, ].
More recently, large language models (LLMs) [, ] have further boosted the vision-language domain, thanks to their strong language understanding performance.
Based on LLMs, multimodal models [, ] like BLIP-2 [], LLaVA [], can do image captioning and visual question answering within one model.
In this paper, we bootstrap our retrieval model from the pre-trained CLIP [] model, following prior research.
BLIP-2 [] model is adopted as an image captioner for expanding text queries.

### 2.2 Text-Video Retrieval

In the field of text-video retrieval, early works are mainly built on pre-trained unimodal models [, , , , ].
With the extracted features, intra-modal and cross-modal interactions are designed to enable effective feature fusion.
With the emergence of pre-trained vision-language models (VLMs), CLIP4Clip [] and Straight-CLIP [] use the pre-trained cross-modal model CLIP [] as a backbone, which is shown to be effective on the text-video retrieval task.
However, a gap still exists between image and video data.
Subsequent studies have turned to fine-tuning on video-text datasets [, , ] to adapt CLIP to the video domain and designing more specialized cross-modal attention mechanisms [, , , ].
The advancement of VLMs also inspired researchers to utilize pre-trained models to generate auxiliary captions [, ] for text-video retrieval data.
Cap4video [] explores an architecture to make use of video captions in text-video retrieval.
CLIP-VIP [] utilizes frame captions to enable diverse loss designs.
Our work differs from them in two ways.
First, we use a comprehensive caption generation strategy to capture the video scenes as complete as possible.
Second, the query expansion is generalized to both training and testing phases.

### 2.3 Query Expansion

In this section, we focus on text query expansion, as text is the most commonly used query format.
Query expansion is a classical technique aimed at improving retrieval quality in information (document) retrieval, particularly for sparse retrieval systems [], where queries and documents have limited overlapping terms or features, leading to vocabulary mismatches.
The main purpose of query expansion is to minimize the lexical gap between the query and target documents by reducing the vocabulary mismatch.
As its core, this technique expands a query into new terms that express the same concept.
Early works either relied on lexical knowledge bases [, ] or utilized pseudo-relevance feedback [, ] from external resources.
Recent works use generative models [, , ] to generate expansion terms through model trained on specifically curated datasets.
As large language models (LLMs) [, ] advanced, some studies began exploring query expansion by prompting LLMs [, ].

In the cross-modal retrieval domain, a few early research efforts attempted to employ lexical knowledge bases [, ] or pseudo-relevance feedback [, ] for query expansion.
In this work, we study how to utilize powerful LLMs for query expansion in the text-video retrieval process.
Furthermore, we devise an approach to select effective queries that enhance performance and reduce the computational cost introduced by expansion.

## 3 Method

Figure: Figure 2: Framework of our approach. The left part shows the process of query expansion of training dataset via a comprehensive video captioning approach, including a video temporal segmentation module and a zero-shot captioner. In the middle, we adopt a dual-encoder model with a text-conditioned video pooling design. The right part illustrates query expansion during testing phase, where a query generation module and a query selection module are devised to enhance the retrieval performance.
Refer to caption: x2.png

### 3.1 Model

In text-video retrieval, the goal is to learn a similarity function $s(Q,V)$ for a given text query $Q$ and a video $V$.
It aims to maximize the similarity score of positive text-video samples and assign lower similarity for irrelevant pairs.
Consistent with previous studies [, , , ], we adopt pre-trained CLIP [] as the backbone.
In the process of adapting pre-trained CLIP from image to the video domain, a commonly employed technique is mean-pooling, which aggregates frame embeddings into a global video embedding.
However, since videos are much more expressive than texts, typical text-agnostic aggregation methods that encompass the entire video may encode irrelevant information not accounted for in the input text.
Hence, we utilize text-conditioned pooling from the previous work, X-Pool [].

To elaborate, when provided with the text query $Q$ and the video $V$, we first use CLIP backbone to obtain the text embedding $\mathbf{e}_{t}\in\mathbb{R}^{D}$ and frame embeddings $\mathbf{e}_{v}\in\mathbb{R}^{F\times D}$, where $D$ is the dimension of the latent space, $F$ is the number of frames.
Then we employ a learnable cross-attention module that learns to highlight the most semantically similar video frames as described in the given text:

$$ $\mathbf{e}_{v|t}=\text{CrossAttn}(\mathbf{e}_{t},\mathbf{e}_{v},\mathbf{e}_{v}),$ (1) $$

where the query of the cross-attention module is the text embedding $\mathbf{e}_{t}$, the key and value are both frame embeddings $\mathbf{e}_{v}$.
The resulting output $\mathbf{e}_{v|t}$ is an aggregated video embedding conditioned on the text.
For the sake of simplicity, we omit certain implementation details, such as layer normalization.
For more details, please refer to X-Pool [].

For the training objective, we follow the common practice of optimizing the bidirectional learning objective.
A symmetric cross-entropy loss is employed to maximize the similarity between matched text-video pairs and and minimize the similarity for other pairs:

$$ $\mathcal{L}_{Q2V}=-\frac{1}{B}\sum_{i=1}^{B}\text{log}\frac{\text{exp}(s( \mathbf{e}_{t}^{i},\mathbf{e}_{v|t}^{i})/\tau)}{\sum_{j=1}^{B}\text{exp}(s( \mathbf{e}_{t}^{i},\mathbf{e}_{v|t}^{j})/\tau))},$ (2) $$

$$ $\mathcal{L}_{V2Q}=-\frac{1}{B}\sum_{i=1}^{B}\text{log}\frac{\text{exp}(s( \mathbf{e}_{t}^{i},\mathbf{e}_{v|t}^{i})/\tau)}{\sum_{j=1}^{B}\text{exp}(s( \mathbf{e}_{t}^{j},\mathbf{e}_{v|t}^{i})/\tau))},$ (3) $$

$$ $\mathcal{L}=\frac{1}{2}(\mathcal{L}_{Q2V}+\mathcal{L}_{V2Q}),$ (4) $$

where $s(\cdot,\cdot)$ is a cosine similarity function, $B$ is the batch size and $\tau$ is a learnable scaling parameter.

### 3.2 GQE: Query Expansion of Training Set

The text-conditioned pooling architecture effectively addresses the information imbalance between the text query and the video by capturing video frames relevant to the text.
As a result, irrelevant video information not mentioned in the text will be eliminated.
However, imbalance in the dataset still limits the model performance.
For instance, information considered irrelevant for text query A might be highly relevant to text query B.
If query B is not present in the training dataset, this information is unlikely to be captured during training.
In such a scenario, the video embedding will be biased toward the content described by query A in the latent space.
During retrieval, query B will struggle to effectively retrieve the video.
If the text queries could comprehensively capture all scenes of a video, then the information of the video can be sufficiently and evenly mined.
To tackle this problem, we propose expanding the text queries in the training set using a comprehensive video captioning approach with two key modules: a video temporal segmentation module and an image captioning module.

As the core of the video temporal segmentation module, Kernel Temporal Segmentation (KTS) [] algorithm is adopted.
KTS divides the video into non-overlapping temporal segments by analyzing a sequence of frame descriptors and identifying the change points.
In our implementation, we use a pre-trained CLIP to extract the frame features.
Given a video $V\in\mathbb{R}^{F\times H\times W\times 3}$,

$$ $\mathbf{e}_{v}=\text{CLIP}_{img}(V),\quad\mathbf{e}_{v}\in\mathbb{R}^{F\times D},$ (5) $$

$$ $\{t_{1},...,t_{m^{*}}\}=\text{KTS}(\mathbf{e}_{v}),$ (6) $$

where $t_{i}$ is the change-point position, $m^{*}$ is the optimal number of change points selected by KTS.
For the sake of simplicity, we omit the details of the KTS algorithm.
With the detected change-points, the video can be split into multiple segments.
Since CLIP features contain semantically rich information, the obtained segments are semantically-consistent, being regarded as ‘atomic’ scenes.

Next, we employ an image captioning module to generate descriptions for each scene.
Specifically, we select the middle frame of each scene as the representative frame as Eq. 7.
Compared with video captioning, an image captioning model is more efficient as it takes only one frame as input.
We assume a single frame is sufficient for representing the ‘atomic’ scene.

$$ $Q_{i}^{train}=\text{ImageCaptioner}(V[\frac{t_{i}+t_{i+1}}{2}]).$ (7) $$

With the above modules, we can generate a set of text queries that captures each semantically-consistent ‘atomic’ scene for a given video.
By running on all the videos of the training set, a video-text dataset with complete text queries is obtained.
Inevitably, even using state-of-the-art image captioning models, there will be some noise being introduced.
In order to make our approach compatible with a wider range of model-centric methods and reveal the importance of data, we have not explored model architecture designs specifically tailored to address the noise issue.
Instead, we show that simply introducing a pre-training stage on the noisy expanded dataset is effective.
More study on the data will be elaborated in Sec. 4.2.1.

### 3.3 GQE: Query Expansion of Testing

In traditional text-video retrieval, one text query is used to retrieve the target video.
However, under the information imbalance condition, a video can be successfully retrieved by multiple possible text queries.
In this section, we introduce query expansion during testing.

Query Generation.
During query generation, there is no access to the video content or other potential texts related to the video.
We must initiate the process from the single existing text query.
Large Language Models (LLMs) are powerful text generation models, especially suited for zero-shot, few-shot, or prompt-based learning.
In our work, we design prompts to instruct an LLM to expand the text query.
We show in the ablations (see Tab. 6) that LLMs are preferable over manual or rule based methods (e.g. NLTK []).
Given a text query $Q$, we task the LLM to imagine the visual scene and rewrite the text query by diversifying the sentence structure and word usage (^1^11The detailed prompts will be provided in the Appendix.)
Formally,

$$ $\{Q_{1}^{test},...,Q_{n}^{test}\}=\text{LLM}(\text{prompt},Q),$ (8) $$

$n$ is the number of generated queries, specified in the prompts.
In this paper, we set $n$ to 10.

Utilizing Generated Queries.
Next, we explain how the generated queries aid in the retrieval process.
A common approach for query expansion used in document retrieval is to create a new, longer text query by appending additional information to the original query.
The advantage is that it does not bring additional computation since it only requires one-time similarity computation.
However, in our experiments (see Tab. 6), we discovered that concatenating text queries or mean-pooling the text embeddings had a detrimental effect on performance.
Another option is to run the retrieval with each query independently and then aggregate the results (similarity or ranking).
Our empirical study shows that aggregating the ranking results using a majority voting strategy yielded the best performance.

Despite the significant performance improvement, there remains an evident drawback: the increased computational cost.
For instance, when using 1 original query plus 10 expanded queries, we require 11 times the amount of pairwise similarity computations compared to the previous approach.
Therefore, we propose to reduce the number of generated queries by a query selection module.

Query Selection.
Before delving into query selection, we first explore the potential benefits of incorporating the generated queries by calculating an upper bound performance.
In particular, from the set of $\{Q,Q_{1}^{test},...,Q_{n}^{test}\}$, we select the oracle query that yields the best retrieval result.
We found that a huge improvement can be achieved via the oracle query selection.
In MSR-VTT dataset, the Rank-1 metric is boosted from $46.7\%$ to $61.4\%$ (see Tab. 6).
This surprising result reveals that: i) query expansion has significant potential for improving text-video retrieval; ii) selecting an effective query can simultaneously reduce computational costs and enhance retrieval performance.

The result motivates us to investigate effective query selection.
When defining effective query selection, our primary considerations are two aspects: relevance and diversity.
The former aspect demands that expanded queries should accurately convey semantic meaning with minimal illusions.
The latter emphasizes that the text queries should be expressed in a variety of ways, such that more novel views can be included.
Often, these two aspects exhibit some degree of contradiction.
Relevance may limit diversity, whereas increased diversity also increases the risk of introducing illusions.
In this work, we firstly control the relevance within a certain range by designing an appropriate prompt, which forces that LLM query generation strictly adheres to the factual information in the original query.
As shown in Fig. 3, the output queries are constrained within a range of relevance by doing this.
After that, we sample a certain number of queries from this pool.
The goal of this step is to maximize the diversity within this range.
In the process, we drew inspiration from point cloud research work [] and transformed it into the Farthest Query Sampling (FQS) algorithm on the query feature space.

Figure: Figure 3: Illustration of Farthest Query Sampling (FQS) algorithm. The queries are distributed within a range of relevance. The blue point (original query) is set as the first query. In this example, FQS sample 2 expanded queries to enrich diversity by considering the distance in the embedding space.
Refer to caption: x3.png

The main idea is to iteratively select queries in a way that maximizes the minimum distance between the selected queries.
The algorithm starts with an initial query and repeatedly selects the query that is farthest from all the previously selected queries.
This process continues until the desired number of queries is selected.
Note that the result of this algorithm is affected by the initial query.
We set the original user query as the initial query to stabilize the algorithm and provide double insurance for *relevance*.
The formal expression is Eq. 9, where $Q$ is the original query, $\{Q_{1}^{*},...,Q_{k}^{*}\}$ are the expanded queries, $k$ is the number of query selection ($k\leq n$).
The distance is computed at an embedding space.

$$ $\{Q,Q_{1}^{*},...,Q_{k}^{*}\}=\text{FQS}(\{Q,Q_{1}^{test},...,Q_{n}^{test}\},k),$ (9) $$

## 4 Experiments

Setups. We employ MSR-VTT [], MSVD [], LSMDC [], and VATEX [] to evaluate our method.
We use standard retrieval metrics: recall at rank K (R@K, higher is better), median rank (MdR, lower is better), and mean rank (MnR, lower is better) to evaluate the performance.
The model backbone is initialized from pre-trained CLIP ViT-B/32 and undergo end-to-end training on each dataset.
More details of experiment setup and implementation are elaborated in the appendix.

### 4.1 Comparison with the State-of-the-Art

**Table 1: Text-to-video retrieval results on the 1K-A test set of MSR-VTT 1K []. *Denotes that the method uses DSL [] post-processing.**
| Method | Venue | Rank@1 | Rank@5 | Rank@10 | Median Rank | Mean Rank |
| --- | --- | --- | --- | --- | --- | --- |
| *CLIP-ViT-B/32* |  |  |  |  |  |  |
| CLIP4Clip [] | Neurocomp.’22 | 44.5 | 71.4 | 81.6 | 2.0 | 15.3 |
| CAMoE* [] | arXiv’21 | 47.3 | 74.2 | 84.5 | 2.0 | 11.9 |
| CLIP2Video [] | arXiv’21 | 45.6 | 72.6 | 81.7 | 2.0 | 14.6 |
| X-Pool [] | CVPR’22 | 46.9 | 72.8 | 82.2 | 2.0 | 14.3 |
| QB-Norm [] | CVPR’22 | 47.2 | 73.0 | 83.0 | 2.0 | - |
| TS2-Net [] | ECCV’22 | 47.0 | 74.5 | 83.8 | 2.0 | 13.0 |
| DRL [] | arXiv’22 | 47.4 | 74.6 | 83.8 | 2.0 | - |
| UATVR [] | ICCV’23 | 47.5 | 73.9 | 83.5 | 2.0 | 12.9 |
| Cap4Video [] | CVPR’23 | 49.3 | 74.3 | 83.8 | 2.0 | 12.0 |
| TEFAL [] | ICCV’23 | 49.4 | 75.9 | 83.9 | 2.0 | 12.0 |
| CLIP-VIP [] | ICLR’23 | 50.1 | 74.8 | 84.6 | 1.0 | - |
| GQE |  | 52.1 | 76.8 | 86.3 | 1.0 | 9.7 |
| GQE* |  | 54.1 | 81.6 | 89.7 | 1.0 | 8.0 |
| CLIP-VIP []+GQE |  | 53.2 | 79.3 | 88.2 | 1.0 | 10.2 |
| CLIP-VIP []+GQE* |  | 58.9 | 83.4 | 89.5 | 1.0 | 8.3 |
| *CLIP-ViT-B/16* |  |  |  |  |  |  |
| CLIP2TV [] | arXiv’21 | 48.3 | 74.6 | 82.8 | 2.0 | 14.9 |
| CenterCLIP [] | SIGIR’22 | 48.4 | 73.8 | 82.0 | 2.0 | 13.8 |
| TS2-Net [] | ECCV’22 | 49.4 | 75.6 | 85.3 | 2.0 | 13.5 |
| DRL [] | arXiv’22 | 50.2 | 76.5 | 84.7 | 1.0 | - |
| UATVR [] | ICCV’23 | 50.8 | 76.3 | 85.5 | 1.0 | 12.4 |
| Cap4Video [] | CVPR’23 | 51.4 | 75.7 | 83.9 | 1.0 | 12.4 |
| CLIP-VIP [] | ICLR’23 | 54.2 | 77.2 | 84.8 | - | - |
| GQE |  | 54.6 | 79.5 | 86.7 | 1.0 | 9.1 |
| GQE* |  | 57.1 | 81.9 | 89.2 | 1.0 | 8.0 |

**Table 2: Comparisons with SOTA of t2v retrieval on MSVD [].**
| Method | R@1$\uparrow$ | R@5$\uparrow$ | R@10$\uparrow$ | MdR$\downarrow$ | MnR$\downarrow$ |
| --- | --- | --- | --- | --- | --- |
| CE [] | 19.8 | 49.0 | 63.8 | 6.0 | - |
| SUPPORT [] | 28.4 | 60.0 | 72.9 | 4.0 | - |
| CLIP [] | 37.0 | 64.1 | 73.8 | 3.0 | - |
| Frozen [] | 33.7 | 64.7 | 76.3 | 3.0 | - |
| TMVM [] | 36.7 | 67.4 | 81.3 | 2.5 | - |
| CLIP4Clip [] | 45.2 | 75.5 | 84.3 | 2.0 | 10.3 |
| UATVR [] | 46.0 | 76.3 | 85.1 | 2.0 | 10.4 |
| X-Pool [] | 47.2 | 77.4 | 86.0 | 2.0 | 9.3 |
| DRL [] | 48.3 | 79.1 | 87.3 | 2.0 | - |
| GQE | 51.1 | 81.1 | 88.6 | 1.0 | 7.2 |

**Table 4: Comparisons with SOTA of t2v retrieval on LSMDC [].**
| Method | R@1$\uparrow$ | R@5$\uparrow$ | R@10$\uparrow$ | MdR$\downarrow$ | MnR$\downarrow$ |
| --- | --- | --- | --- | --- | --- |
| CE [] | 11.2 | 26.9 | 34.8 | 25.3 | - |
| MMT [] | 12.9 | 29.9 | 40.1 | 19.3 | 75.0 |
| NoiseE [] | 6.4 | 19.8 | 28.4 | 39.0 | - |
| Straight-CLIP [] | 11.3 | 22.7 | 29.2 | 56.5 | - |
| MDMMT [] | 18.8 | 38.5 | 47.9 | 12.3 | 58.0 |
| Frozen [] | 15.0 | 30.8 | 39.8 | 20.0 | - |
| TeachText-CE+ [] | 17.2 | 36.5 | 46.3 | 13.7 | - |
| CLIP4Clip [] | 22.6 | 41.0 | 49.1 | 11.0 | 61.0 |
| XPool [] | 25.2 | 43.7 | 53.5 | 8.0 | 53.2 |
| CLIP-VIP [] | 25.6 | 45.3 | 54.4 | 8.0 | - |
| TEFAL [] | 26.8 | 46.1 | 56.5 | 7.0 | 44.4 |
| GQE | 28.3 | 49.2 | 59.1 | 6.0 | 37.1 |

In this section, we compare our Generalized Query Expansion (GQE) method with recent state-of-the-art methods on four benchamarks.
Tab. 1 shows the comparisons on MSR-VTT.
Our method sets a new state-of-the-art performance in text-to-video retrieval for both ViT-B/32 and ViT-B/16 backbones, surpassing previous methods significantly.
For instance, we achieve a remarkable +7.6% higher R@1 compared to CLIP4Clip when using the same ViT-B/32 backbone for text-to-video retrieval, without any post-processing techniques.
In comparison to methods that also utilize auxiliary captions, such as Cap4Video [] and CLIP-VIP [], our approach consistently achieves higher performance with both ViT-B/32 and ViT-B/16 backbones.
It’s worth noting that CLIP-VIP uses the large-scale video-text dataset HowTo100M [] for pre-training.
Despite this, our method still outperforms it, providing further evidence of effectiveness of our approach.
We further show that, as a data-centric method, GQE is compatible with stronger models, such as CLIP-VIP [], and post-processing techniques, such as DSL [], yielding significantly higher performance.

Furthermore, we conduct evaluations on other benchmarks, including MSVD (Tab. 3), VATEX (Tab. 3), and LSMDC (Tab. 4).
To ensure a fair comparison, all experiments in these three datasets are conducted using the ViT-B/32 backbone.
GQE consistently improves performance across different datasets, ultimately achieving new state-of-the-art results.
Overall, our method’s consistent performance improvements across these four benchmarks demonstrate the effectiveness of GQE.

### 4.2 Ablation Study

**Table 5: Ablation study on query expansion of training set on MSR-VTT []. We compare our GQE (Rows 4-6) with various training query expansion settings.**
| Query Expansion | R@1$\uparrow$ | R@5$\uparrow$ | R@10$\uparrow$ | MdR$\downarrow$ | MnR$\downarrow$ |
| --- | --- | --- | --- | --- | --- |
| 1 ✗ (Baseline) | 46.7 | 73.0 | 83.3 | 2.0 | 14.2 |
| 2 Global Video Captions | 46.9 | 73.0 | 81.9 | 2.0 | 13.9 |
| 3 Fixed Time Interval | 47.0 | 72.2 | 82.3 | 2.0 | 14.2 |
| 4 GQE (Training) | 47.7 | 71.9 | 82.1 | 2.0 | 13.9 |
| 5 GQE (Testing) | 50.2 | 78.0 | 86.9 | 1.0 | 10.1 |
| 6 GQE (Full) | 52.1 | 76.8 | 86.3 | 1.0 | 9.7 |

#### 4.2.1 Query Expansion of Training Set

In Tab. 6, we study the impact of the query expansion of training set.
Row 1 is the baseline model trained on the original MSR-VTT [], without training query expansion.
Row 2 shows results with the model pre-trained on video captions provided by a previous work [], which uses a zero-shot video captioning model ZeroCap [].
In Row 3, we design a strawman setting, in which we uniformly break a video in fixed intervals and do captioning for each chunk.
The text-video retrieval model is then pre-trained on this expanded caption set.
In Rows 4 and Row 6, the model is pre-trained on comprehensive video captions generated by our approach.
To ensure that the quantity of data does not lead to unfair comparison, we generate a comparable number of captions for Rows 2 to 6, resulting in approximately 45K additional queries for the 9K training videos in the MSR-VTT dataset.
Comparing Rows 1 to 4 suggests that incorporating extra text queries in the training data is generally beneficial.
Particularly, GQE (Training) is shown to be more effective, demonstrating a 1.0% performance increase.
We attribute this improvement to the proposed approach, which effectively reduces the information imbalance gap by covering more video scenes.
When comparing GQE (Testing) and GQE (Full), we observe that GQE (Training) is not only effective on the baseline model but also enhances performance based on GQE (Testing), resulting in a 1.9% increase.
These observations confirm the effectiveness of training set query expansion.

**Table 7: Ablation study on query selection, including the selection algorithm and the choice of parameter $k$. We report only R@1 metric for simplicity.**
| Model | Query Selection | $k=0$ | $k=2$ | $k=6$ | $k=10$ |
| --- | --- | --- | --- | --- | --- |
| Baseline | Random | 46.7 | 49.6 | 48.8 | 49.6 |
| 46.7 | 50.2 | 48.9 | 49.6 |  |  |
| 46.7 | 49.0 | 49.6 | 49.6 |  |  |
| 46.7 | 49.9 | 49.0 | 49.6 |  |  |
| $k$-DPP [] | 46.7 | 50.2 | 49.9 | 49.6 |  |
| FQS | 46.7 | 50.2 | 50.1 | 49.6 |  |
| GQE | $k$-DPP [] | 46.7 | 50.9 | 51.0 | 49.6 |
| FQS | 47.7 | 52.1 | 50.5 | 49.6 |  |

#### 4.2.2 Query Expansion of Testing

We use 1 original query and 10 expanded queries on the MSR-VTT dataset to evaluate and compare different types of query generation and result aggregation methods.

Query Generation.
We first compare the two types of query generation methods.
For the NLTK-based method, we utilize the NLTK [] tool to identify representative terms (nouns and verbs) in the query and generate new queries by replacing these terms with their synonyms obtained from the NLTK tool.
For the LLM-based method, we instruct the LLM (GPT-4) to generate queries through specific prompts.
As shown in Tab. 6, under all the result aggregation methods, LLM always outperforms the NLTK-based method.
Notably, the upper bound results, which remove the influence of aggregation methods, provide a clearer distinction between the two query generation methods.
This performance gap demonstrates the effectiveness of LLM as a tool for query expansion in text-video retrieval.
Despite being slightly less effective than LLM, it’s worth noting that NLTK still achieves significant improvement compared with baseline.
It should be acknowledged that NLTK-based methods can also be a cheaper option with some compromise on the performance.

Result Aggregation.
Next, we investigate different methods for aggregating the retrieval results from multiple queries.
The results in Tab. 6 show that certain methods, such as concatenating queries and mean-pooling of text embeddings, can lead to even worse performance than the baseline.
Among these methods, majority voting is a simple yet effective one, outperforming other methods.
The most interesting part is the Oracle result.
Firstly, it shows the huge potential of testing query expansion with the significant performance improvement.
Secondly, the gap between the Oracle result and other methods reveals that there are still a large room for improvement.
Finally, the Oracle result is achieved without compromising on computational cost while majority voting with all the 11 queries appears to be inefficient.
These findings inspire us to develop a dedicated query expansion module to further improve retrieval performance as well as reduce computational cost.

Query Selection aims to enhance retrieval performance to approach the Oracle and improve inference efficiency.
In Tab. 7, we report the R@1 retrieval performance under various query selection settings.
Firstly, we find that randomly selecting a specific number of queries can result in promising performance.
The primary drawback of random query selection is its lack of stability, a characteristic that should be avoided in retrieval systems.
Next, we adapt the determinantal point processes (DPPs) [] into the query selection task.
In particular, we utilize MAP inference of DPPs introduced in [] for fast and deterministic inference.
It returns a subset of points of a pre-specified size (i.e. $k$) that are maximally diverse.
The results show that $k$-DPP can be a viable implementation of query selection.
Finally, we observe that the proposed Farthest Query Sampling algorithm achieves competitive or better performance in a deterministic way.

Compared to a single query or random query sampling, FQS is able to cover a larger area in the embedding space, naturally increasing the diversity.
When comparing $k$-DPP and FQS, the main difference is that $k$-DDP selects queries from the list of candidates comprised of GT query and expanded queries.
In other words, $k$-DDP treat GT query and expanded queries equally.
It is possible that the GT query will not be selected.
In contrast, our FQS always select the GT query as the anchor then select other diverse queries from the expanded queries.
The involvement of GT query essentially ensures the relevance of text query and establishes a solid retrieval lower bound, allowing FQS to achieve better retrieval performance.
We use the text encoder in our retrieval model for embedding extraction in the experiments unless specified.

In the context of query selection, the hyper-parameter $k$ plays a crucial role.
Our results indicate that the optimal value for $k$ is 2.
It achieves the highest R@1 score among most of the tested settings as shown in Tab. 7.
Notably, when compared with $k=10$ (using all the queries), setting $k=2$ results in higher performance, with an R@1 score of 50.2 compared to 49.6, while also substantially reducing computational costs.
We choose an even number for $k$ to ensure there is an odd number of queries when including the original query, which avoids potential ties in the majority voting process.
In both theoretical and experimental scenarios, $k=2$ proves to be the optimal choice.

### 4.3 Discussion

We provide more results in the appendix, including implementation details, experiments on model architectures rather than X-Pool [] showing that our data-centric approach is compatible with model-centric approaches; discussion about the interesting oracle query selection; video-to-text retrieval result; and qualitative examples that demonstrate how the expanded queries aid in text-video retrieval.
We also thoroughly discuss the limitation and future work.

## 5 Conclusion

In this work, we address the one-to-many problem in text-video retrieval task resulting from information imbalance between video and text queries.
We introduce GQE, a data-centric approach to address this issue in both training and testing phases.
For training dataset, we expand text queries through comprehensive video captioning, ensuring full video scene coverage.
During testing, we use large language models to generate multiple relevant yet diverse text queries to enhance retrieval performance.
Moreover, we apply farthest query sampling for effective query selection, improving retrieval performance and reducing computational costs.
Extensive experiments prove GQE has consistent improvements over four benchmarks, surpassing current state-of-the-art methods.
Our work serves as an inspiration for future research efforts, encompassing both model-centric methods and data-centric enhancements.

## Appendix A Appendix

### A.1 Experiment Setups and Implementation Details

Datasets.
MSR-VTT [] contains a total of 10K video clips, each having 20 captions.
We utilize the Training-9k subset for training and the test-1K-A subset for evaluation.
MSVD [] contains 1,970 videos with 80K captions, with 40 captions on average per video. There are 1200, 100, and 670 videos in the train, validation, and test sets, respectively.
LSMDC [] consists of 118,081 video clips sourced from 202 movies with one caption corresponding to each clip. Evaluation is conducted on a test set of 1,000 videos from movies disjoint from the train and validation sets.
VATEX [] collects around 35K videos with multiple text annotations in both English and Chinese for each video.
There are around 26K videos for training, 1,500 for validation, and 1,500 for testing.

Evaluation metrics.
We use standard retrieval metrics: recall at rank K (R@K, higher is better), median rank (MdR, lower is better), and mean rank (MnR, lower is better) to evaluate the performance. R@K (Recall at K) calculates the percentage of test samples for which the correct result is found in the top-K retrieved points to the query.
We report results for R@1, R@5, and R@10. Median Rank calculates the
median of the ground-truth results in the ranking. Mean Rank calculates the mean rank of all correct results.

Implementation Details.
We initialize our backbone from the pre-trained CLIP ViT-B/32.
Following previous work [], the text-conditioned pooling module is randomly initialized.
Our models undergo end-to-end training on each dataset.
For the training hyper-parameters, we also follow X-Pool [].
The model is trained for 3 epochs on the expanded training set and 5 epochs on the standard training set.
A cosine scheduler [] is employed to decay the learning rate.
For all experiments, we follow previous works by uniformly sampling 12 frames from each video and resizing them to 224x224.
For query expansion of training set, we use blip2-opt-2.7b-coco as the captioner.
For query expansion during testing, we utilize GPT-4 model through the API (^2^22[https://openai.com/blog/openai-api](https://openai.com/blog/openai-api)) to generate queries.
We train our model using NVIDIA A10 24 GPU.
The training takes around 12 hours.

### A.2 Experiment on Vanilla Model

In our main paper, we utilize the pre-trained CLIP and the text-conditioned pooling module as the retrieval model.
The text-conditioned pooling is designed to capture video frames relevant to the text query, effectively alleviating the information imbalance.
In this section, we demonstrate that our data-centric approach is not coupled with a specific architecture design, such as the text-conditioned pooling.
We conduct experiments on the vanilla CLIP model with a mean-pooling strategy to obtain the video embedding.
The results are reported in Tab. 8.

**Table 8: Text-to-video retrieval results with vanilla CLIP mean-pooling model on MSRVTT [].**
| # | GQE<br>Training | GQE<br>Testing | R@1 | R@5 | R@10 | MdR | MnR |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | ✗ | ✗ | 42.2 | 70.4 | 79.2 | 2.0 | 15.7 |
| 2 | ✓ | ✗ | 42.9 | 70.2 | 79.8 | 2.0 | 16.3 |
| 3 | ✗ | ✓ | 46.5 | 75.9 | 85.1 | 2.0 | 11.6 |
| 4 | ✓ | ✓ | 48.8 | 75.3 | 84.6 | 2.0 | 11.4 |

The comparison between Row 1 and Row 2, as well as between Row 3 and Row 4, illustrate the effectiveness of GQE Training.
Without GQE Testing (Row 1 vs. Row 2), GQE Training achieves an improvement of 0.7% on R@1.
The performance gain becomes more obvious when combined with GQE Testing (+2.3% on R@1, Row 3 vs. Row 4).
Finally, the 6.6% performance gap between the results in Row 1 and Row 4 further validates the benefits of our full approach.

### A.3 GQE Testing as a Training-free Tool

In this section, we demonstrate that the proposed query expansion of testing can be applied to a wide range of text-video retrieval models as a training-free tool for performance improvement.
We conduct evaluations on the MSR-VTT dataset [] using four widely recognized methods.
1) For CLIP [] zero-shot, we utilize the pre-trained CLIP model and employ mean-pooling to obtain the video embedding in a zero-shot manner;
2) CLIP4Clip [], a classical method in the text-video retrieval domain;
3) X-Pool [], adopted as the base model in our work;
4) CLIP-VIP [], recognized as one of the current state-of-the-art methods.
To maintain consistency, all experiments utilize the ViT-B/32 as the retrieval backbone and CLIP text encoder for query selection.
The baseline results are reproduced from their released codebases.

**Table 9: Text-to-video retrieval results with various methods on the MSR-VTT []. The result are reproduced based the released codebase of each method respectively.**
| Model | R@1 | R@5 | R@10 | MdR | MnR |
| --- | --- | --- | --- | --- | --- |
| CLIP${}_{\text{zero-shot}}$ [] | 31.2 | 52.4 | 63.6 | 5.0 | 42.8 |
| CLIP${}_{\text{zero-shot}}$ [] + GQE Testing | 36.4 (+5.2) | 62.6 | 71.6 | 3.0 | 27.7 |
| CLIP4Clip${}_{\text{mean-pooling}}$ [] (Reproduced) | 42.3 | 70.6 | 81.9 | 2.0 | 16.5 |
| CLIP4Clip${}_{\text{mean-pooling}}$ [] + GQE Testing | 46.3 (+4.0) | 76.3 | 85.3 | 2.0 | 11.9 |
| X-Pool [] (Reproduced) | 46.7 | 73.0 | 83.3 | 2.0 | 14.2 |
| X-Pool [] + GQE Testing | 50.6 (+3.9) | 76.3 | 86.5 | 1.0 | 10.4 |
| CLIP-VIP [] (Reproduced) | 49.3 | 74.8 | 84.9 | 2.0 | 13.4 |
| CLIP-VIP [] + GQE Testing | 53.2 (+3.9) | 79.3 | 88.2 | 1.0 | 10.2 |
| CLIP-VIP [] + DSL [] (Reproduced) | 55.1 | 78.9 | 86.6 | 1.0 | 11.1 |
| CLIP-VIP [] + DSL [] + GQE Testing | 58.9 (+3.8) | 83.4 | 89.5 | 1.0 | 8.357 |

The text-to-video retrieval results are reported in Tab. 9.
We observe that GQE Testing consistently improves the retrieval performance across the four methods.
For the zero-shot CLIP, GQE Testing boosts the R@1 by 5.2% and R@5 by over 10%, which is a significant improvement.
Notably, for the state-of-the-art CLIP-VIP [], despite the remarkable results achieved, GQE Testing still improves the performance by a large margin, further validating the effectiveness of our method.

We note that several popular methods, including DSL [] and QB-Norm [], enhance pre-trained text-video retrieval performance using a similar training-free approach.
In the final experiment of Tab. 9, we demonstrate that our GQE Testing is compatible with these training-free methods.
The combination of our approach and DSL significantly elevates the performance to a much higher level.
Moreover, it is important to note that DSL [] requires access to all queries during testing, which is an impractical assumption for real-world retrieval systems.
In contrast, our approach does not require access to other queries during testing, making it more practical in real-world applications.

### A.4 Video-to-Text Retrieval

In our work, we mainly focus on the task of text-to-video retrieval.
In this section, we investigate the feasibility of applying generalized query expansion to the task of video-to-text retrieval.
In this task, the query is a video while the retrieval targets are texts.
Therefore, we apply the video captioning strategy described in our paper as query expansion.
For each video query, we generate a set of captions to serve as auxiliary queries.
We conduct retrieval for each query independently and aggregate the results via majority voting.
The results are reported in Tab. 10.
We observe that simply incorporating video captions as query expansion does not improve the performance of video-text retrieval.

In previous work [], a common approach of processing captions is to sort and filter the captions according to the similarity scores between the video and its captions, since captions are often considered noisy.
Inspired by this, we conduct experiments with various approaches for query (caption) selection.
Nearest/farthest neighbor sampling sorts the captions based on the similarity between video and it captions, then selects the top-$k$ most similar/dissimilar captions as the expanded queries.
FQS (Farthest Query Sampling) is described in our paper.
We set $k$ to 2 in the experiments.
The results demonstrate that the combination of video captioning as query expansion and the FQS algorithm effectively boosts the performance of video-text retrieval.
In contrast, nearest/farthest neighbor sampling is shown to be not helpful.
Upon comparing different query selection methods, we attribute the superiority of FQS to its ability to cover a larger and diverse area in the embedding space.
The potential noise contained in the captions are effectively eliminated by majority voting.

### A.5 Understanding Oracle Query Selection

The oracle query selection is implemented as follows: for one specific instance, among 1 GT query and $k$ expanded queries, we always select the query that yields the best ranking, where the best ranking refers to that the target video is ranked highest.
If there are multiple such queries, we prioritize the GT query.
We also provide the code in the supplementary material.
Following the rule above, we stats the percentage of GT query vs. expanded query in the selected oracle query.
The statistic is conducted on $1,000$ test samples of MSR-VTT dataset, $k=10$.
The result shows that $59.5\%$ of selected queries come from the GT query set, while $40.5\%$ of selected queries are from the expanded queries.
This actually provides an explanation of the huge performance gap between baseline and oracle, suggesting that the text queries can be specially investigated to enhance the retrieval.

**Table 10: Video-to-text retrieval results on MSR-VTT [].**
| Method | R@1 | R@5 | R@10 | MdR | MnR |
| --- | --- | --- | --- | --- | --- |
| MMT [] | 27.0 | 57.5 | 69.7 | 3.7 | 21.3 |
| T2VLAD [] | 31.8 | 60.0 | 71.1 | 3.0 |  |
| SupportSet [] | 28.5 | 58.6 | 71.6 | 3.0 | - |
| TMVM [] | 34.8 | 63.8 | 73.7 | 3.0 | - |
| CLIP4Clip [] | 42.7 | 70.9 | 80.6 | 2.0 | 11.6 |
| CenterCLIP [] | 42.8 | 71.7 | 82.2 | 2.0 | 10.9 |
| CLIP2Video [] | 43.5 | 72.3 | 82.1 | 2.0 | 10.2 |
| X-Pool (Baseline) [] | 44.4 | 73.3 | 84.0 | 2.0 | 9.0 |
| TS2-Net [] | 45.3 | 74.1 | 83.7 | 2.0 | 9.2 |
| DRL [] | 45.3 | 73.9 | 83.3 | 2.0 | - |
| UATVR [] | 46.0 | 73.7 | 82.8 | 2.0 | 8.7 |
| Cap4Video [] | 47.1 | 73.7 | 84.7 | 2.0 | 8.7 |
| TEFAL [] | 47.1 | 75.1 | 84.9 | 2.0 | 7.4 |
| Baseline | 44.6 | 73.3 | 84.0 | 2.0 | 8.8 |
| Baseline + GQE (All Captions) | 42.0 | 64.6 | 73.6 | 2.0 | 55.0 |
| Baseline + GQE (Nearest-Neighbor) | 44.9 | 71.0 | 79.5 | 2.0 | 29.6 |
| Baseline + GQE (Farthest-Neighbor) | 45.9 | 71.9 | 80.7 | 2.0 | 24.4 |
| Baseline + GQE (FQS) | 52.2 | 78.3 | 86.5 | 1.0 | 7.9 |

### A.6 Prompt for Testing Query Expansion

We provide the prompt used for generating testing queries in Fig. 4.

Figure: Figure 4: Prompts used for query generation.
Refer to caption: x4.png

### A.7 Discussion, Limitation and Further Work

Despite the significant performance achieved by our method, it exhibits certain limitations that serve as inspiration for future research.
Firstly, in the query expansion of the training set, our approach focuses on a data-centric approach that expands the dataset. However, there is potential for further improvement by designing dedicated model architectures tailored to the expanded dataset, such as multi-instance learning.
Secondly, in the query expansion of testing, we employ LLM as the tool to generate diverse queries. Given the vast prompting space of LLM, we have not invested significant efforts in prompt engineering. As a future research avenue, more dedicated prompts, such as in-context learning, can be harnessed to further enhance the quality of text queries.
Finally, we acknowledge that there is still a noticeable gap in terms of retrieval performance and computational cost between our method and the upper bound of query expansion.
This discrepancy encourages further research to bridge the gap.

### A.8 Qualitative Examples

In this section, we present a series of text-video retrieval examples from the MSR-VTT dataset [].
These examples not only qualitatively validate the effectiveness of our approach but also demonstrate how the expanded queries aid in text-video retrieval.
The examples are shown in Fig. 5, Fig. 7, Fig. 6, and Fig. 8.
In each example, we show that the original query fails to retrieve the target video, while the expanded query does.
Detailed explanations are presented in the caption of each figure, respectively.

Figure: Figure 5: Rephrasing concepts. The original query focuses on the concept ‘team’. Although the concept of ‘team’ may imply the involvement of ‘multiple players’, it is not explicitly stated. The expanded query provides a more explicit view to elaborate the query, correcting the retrieval.
Refer to caption: x5.png

Figure: Figure 6: Altering word order. The initially recalled video successfully covers the concept of ‘woman’, ‘man’, and ‘cooking food’. While it does not contain content corresponding to ‘setting a table’. Our hypothesis is that this is because ‘setting a table’ is positioned far back in the original query, limiting its impact. The expanded query recalls the video that captures the most terms in the query. This is achieved by altering the word order and emphasis of the original query.
Refer to caption: x6.png

Figure: Figure 7: Typo correction. We notice that there is a typo in the original query (‘rate’ vs. ‘rat’). Even a very small typo like this can lead to problematic retrieval result. The incorrect video has captured the concept of ‘hand’ but fails on other aspects. With the corrected query, we can successfully find the target video. This capability stems from the strong language understanding ability of the large language model, which automatically recognizes the incoherent word in the query and corrects it. This case is challenging to correct for rule-based methods, such a NLTK, since ‘rate’ is not a wrong word.
Refer to caption: x7.png

Figure: Figure 8: Robustness. In this example, the original query is clear while the initially recalled video appears to be not strongly relevant to the original query. This might be due to the inherent weakness of the retrieval model. However, the expanded query can still successfully retrieve the target video. This can be seen as an indication that query expansion improves the robustness of the retrieval model, making it resilient to corner failure cases.
Refer to caption: x8.png

## Peer Reviews

confidence: 5
contribution: 2
initial_rating: 3
presentation: 3
questions: 1. Line 290 proposed to ensure the diversity in the supplemented queries. Why are the supplemented queries should be expressed in as many ways?

2. To verify the effectiveness of the proposed method, the authors should provide some examples of the generated captions during training and the expanded queries during inference. It can help to understand the contribution of the work.

3. The approach proposed in Figure 3 requires more illustration on design idea and details; the current version is a bit confusing.

4. The authors need to explain the training efficiency and inference efficiency of the proposed method.

Other questions seen in the Weaknesses.
soundness: 2
strengths: 1. The proposed problem of information asymmetry in the text-video retrieval is important and needs to be solved in the development of this task.
2. The paper is well written and easy to follow.
summary: This work aims to solve the problem of information asymmetry between text descriptions and videos in text-video retrieval,i.e., text often contains only part of the video content. The author uses VLM to generate more captions from videos during training; and uses LLM to expand queries and add more content during inference.
weaknesses: 1. The proposed method uses VLM and LLM to generate details to the original caption. The captions and queries generated in this way are unreliable and contain a lot of information that is not in the video. In the inference phase, without the addition of oracle information, the newly generated captions and querys are hallucinated by the large language model. The authors need to provide more evidence to support their idea.

2. The work involves utilizing large visual language models and large language models. The required training cost and inference computation are much higher than compared works. It`s not fair to compare directly in experiments. The authors need to prove the performance gain is not from the accumulation of more computation.

3. Line 238 mentions that this work used an image captioner. The designed method does not consider the temporal cues in the video neither. The proposed method is essentially designed for an image-text retrieval task rather than a video-text retrieval task.

4. In line 278, the fact that oracle query can improve model performance does not mean that supplementing the query is useful. Introducing the ground truth query can greatly enhance model performance, regardless of whether the query is supplemented or not.

---

confidence: 5
contribution: 2
initial_rating: 5
presentation: 2
questions: Q1.  Could you include experiments comparing the model’s performance with more recent methods beyond those published in 2022? This would enhance the context of the model's strengths and limitations relative to current advancements.
Q2. How might the proposed framework perform in real-world, large-scale text-video retrieval systems？
Q3.  Could you clarify how this work distinguishes itself from existing methods beyond leveraging VLMs and LLMs? Are there any unique components or methodological innovations that specifically contribute to the improvements in performance?

Below are a few suggestions which could help the authors to refine a better version of this work.

1. This paper addressed the issue of information asymmetry in text-video retrieval from a data-centric perspective, therefore leveraging VLMs for event-level captioning and LLMs for query diversification. While this approach primarily combined existing methods to enhance textual representations, emphasizing the uniqueness of the model structure would better highlight its innovative contributions.
 
2. As the paper mentioned, relevant and diverse queries are expected to be retrieved. After initially constraining the relevance of the generated queries, FQS is applied to iteratively select queries by maximizing the minimum distance between them.
It may be worth considering adding relevance constraints to FQS, such as setting a minimum similarity threshold between query embeddings. Similarity metrics, such as dot product or cosine similarity, could be used for this purpose. Incorporating both distance and similarity into FQS might enhance its performance.
Additionally, FQS could be also compared with methods that minimize the minimum distance between selected queries to further demonstrate its ability to select diverse queries effectively.
Overall, designing alternative algorithms as comparisons would provide a stronger demonstration of FQS’s effectiveness.
 
3. In this paper, Table 1 presents the performance of the latest models. However, Tables 2 and 3 do not include these methods, such as Cap4Video. Including these comparisons would provide a more comprehensive evaluation.
soundness: 2
strengths: S1.  This paper shows a method of utilizing VLMs and LLMs to enrich textual representations in training and inference stage.
S2. The data-centric method proposed by this paper maybe useful in practice, as this paper provides evidence of its effectiveness.
S3.  The writing is well-structured and clear, making the paper easy to follow.
summary: Due to the information asymmetry between video and text in text-video retrieval (TVR), this paper introduces a data-centric framework aimed at enriching textual representations to better align with the rich information contained in video content. However, despite efforts to leverage vision-language models (VLMs) and large language models (LLMs), the method lacks significant innovation.
weaknesses: W1. The method does not present substantial innovation. The improvement in model performance appears to be primarily attributed to the inherent capabilities of VLMs and LLMs in visual and textual understanding.
W2. While the paper introduces a query selection mechanism and designs a Farthest Query Sampling (FQS) algorithm, it would benefit from exploring additional query selection algorithms to further validate FQS’s effectiveness.
W3. The comparative experiments with the latest methods are somewhat lacking. Most of the related work cited in the experiments on state-of-the-art (SOTA) methods is limited to publications from 2022.

---

confidence: 5
contribution: 4
initial_rating: 6
presentation: 4
questions: - Will the author open-source the enriched dataset and code?
soundness: 3
strengths: - The paper investigates an under-explored area in the field of text-video retrieval, a data-centric approach to improving the performance by identifying pitfalls in the current training dataset captions and test retrieval process.
- The full method demonstrate remarkable performance in the text-video retrieval task across several benchmarks.
- The experiments with the oracle queries show a huge gap in the current methods, which is novel and interesting. It opens up future works to consider such data-centric approaches for improving performance.
- The paper is well written and easy to follow.
summary: This paper investigates the problem of information asymmetry in text video retrieval from a data-centric perspective. Existing works usually use model-centric approaches of carefully designing advanced text-video interaction modules, e.g., text-conditioned video representations, stochastic embedding, etc. This paper proposes a unified text enrichment framework to enhance the textual representation during both training and testing phase. The results consistently show promising improvements over existing methods. Interestingly, the concept of “oracle query” clearly demonstrate the potential of the query generation and selection, possibly opening up a new venue in this domain.
weaknesses: - There are some existing works on augmenting the data, e.g., [1], what is the main difference between this work and existing works?
- The oracle query experiment shows huge performance gap. Although the result is inspiring, it is better to provide more explanations and/or investigations about this phenomenon. What contribute to the final performance?
- In the ablation study, the performance gain seems mainly come from the Retrieval Phase Enrichment, while the gain Training Phase Enrichment seems to be marginal.
- The method will inevitably increase the computational cost, which should better be investigated.
- It is mentioned that majority voting over the enriched queries yields the best performance. However, it is not clear how this is implemented. I would suggest include such details, as least in the appendix.

[1] HAVTR: Improving Video-Text Retrieval Through Augmentation Using Large Foundation Models. ECCV 2024.

Meta-review: This paper tackles the challenge of text-video retrieval. To enhance text-visual alignment, it introduces a method that leverages Vision-Language Models (VLMs) for event-level captioning and Large Language Models (LLMs) for query diversification, alongside a query selection mechanism. The strengths of the paper include the importance and usefulness of the task, clear writing, and good performance demonstrated on multiple benchmarks.

Several weaknesses were addressed during the rebuttal phase. Ultimately, two out of three reviewers expressed support for accepting the paper. The remaining concern, raised by the opposing reviewer, centers on the potential for hallucinations introduced by language models. In the AC's opinion, it does not appear to be a substantial weakness. The paper’s empirical validation demonstrates effectiveness, which has been recognized by other reviewers. In addition, the authors have acknowledged this issue in the rebuttal and committed to including a discussion in the revised paper.

Decision: Accept (Poster)