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Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval

Xun Yang, Jianfeng Dong, Yixin Cao, Xun Wang, Meng Wang, Tat-Seng Chua

arXiv:2007.02503v1cs.CV

TL;DR

Complex-query video retrieval needs representations that capture linguistic structure beyond keyword concepts or structure-agnostic embeddings. TCE recursively constructs a latent semantic tree, encodes queries and temporally modeled videos, and maps them into a joint embedding space; experiments on large-scale datasets report state-of-the-art retrieval performance and component effectiveness. The approach remains bounded by prior structured methods’ dependence on syntactic annotations and predefined construction rules as a stated challenge.

  • Problem

    Complex queries carry richer semantics than keyword queries, while common embedding methods do not exploit their syntactic structure and structured methods can require annotations and predefined rules.

  • Method

    TCE recursively builds a latent semantic tree, derives a structure-aware query representation, models temporal video information attentively, and jointly embeds queries and videos.

  • Results

    The approach achieves state-of-the-art retrieval performance on large-scale datasets, with experiments demonstrating the effectiveness of its components.

  • Takeaways & Limitations

    Automatically modeling query structure provides a flexible direction for complex-query video retrieval while retaining joint text-video matching.

  • Takeaways & Limitations

    Prior structured-query solutions require syntactic labels and complex predefined rules, making them difficult to apply across different linguistic expression patterns.

Abstract

from arXiv · show

The rapid growth of user-generated videos on the Internet has intensified the need for text-based video retrieval systems. Traditional methods mainly favor the concept-based paradigm on retrieval with simple queries, which are usually ineffective for complex queries that carry far more complex semantics. Recently, embedding-based paradigm has emerged as a popular approach. It aims to map the queries and videos into a shared embedding space where semantically-similar texts and videos are much closer to each other. Despite its simplicity, it forgoes the exploitation of the syntactic structure of text queries, making it suboptimal to model the complex queries. To facilitate video retrieval with complex queries, we propose a Tree-augmented Cross-modal Encoding method by jointly learning the linguistic structure of queries and the temporal representation of videos. Specifically, given a complex user query, we first recursively compose a latent semantic tree to structurally describe the text query. We then design a tree-augmented query encoder to derive structure-aware query representation and a temporal attentive video encoder to model the temporal characteristics of videos. Finally, both the query and videos are mapped into a joint embedding space for matching and ranking. In this approach, we have a better understanding and modeling of the complex queries, thereby achieving a better video retrieval performance. Extensive experiments on large scale video retrieval benchmark datasets demonstrate the effectiveness of our approach.

1 INTRODUCTION

Complex-query video retrieval is difficult because natural-language queries carry richer linguistic structure than keyword queries, while existing concept-based and structured approaches struggle to represent or flexibly construct that structure. TCE addresses this challenge by automatically building a latent semantic tree and jointly encoding queries and videos in a shared embedding space.

  • Motivation: Complex queries express richer semantics than keyword-based queries, making them a central challenge for text-based video retrieval.The paper illustrates this with natural-language descriptions containing multiple events and relations.
  • Existing Approaches: Concept-based retrieval aggregates matches over extracted visual concepts but is ineffective for long complex queries and difficult to train and configure.Its limitations include handling linguistic context, training concept classifiers, and selecting relevant concepts.
  • Existing Approaches: Structured-query methods describe semantic or spatial relations but typically require syntactic annotations and complex predefined construction rules.These requirements make adaptation to new linguistic expression patterns difficult.
  • Proposed Framework: TCE recursively composes a Latent Semantic Tree without syntactic annotations and uses memory-augmented node scoring to incorporate linguistic context.Each tree node represents a constituent of the complex query.
  • Proposed Framework: The framework uses attention to form a structure-aware query representation, temporally models video frames, and maps both modalities into a joint text-video embedding space.The stated contributions include informative constituent-node and frame selection and extensive evaluation on large-scale datasets.

2 RELATED WORK

Prior text-based video retrieval work mainly follows concept-based or embedding-based paradigms, while later methods incorporate linguistic structure through manually specified or annotated dependency representations. Video representations commonly aggregate frame features with pooling or sequential temporal models, and related work also applies natural-language retrieval to specialized domains.

  • Concept-Based Retrieval: Concept-based methods establish cross-modal associations through detected concepts and retrieve videos by matching query concepts to visual content.They use concept extraction, CNN-based detection, and, in some cases, larger vocabularies and SVM classifiers.
  • Embedding-Based Retrieval: Embedding-based methods map textual queries and videos into a common space and measure similarity with a distance metric such as cosine distance.Earlier query embeddings commonly relied on pretrained word2vec representations and could ignore word order.
  • Structure-Aware Retrieval: Structure-aware retrieval methods model syntactic or dependency relations to improve interpretability, but prior approaches use manually derived rules or dependency-tree construction.These methods explicitly explore query syntax beyond concept associations.
  • Video Representation: Video embeddings commonly aggregate frame-level CNN features with mean or max pooling, while other methods model temporal information using LSTMs.The reviewed approaches differ in how they convert frame sequences into video-level features.
  • Related Applications: Natural-language retrieval has also been applied to specialized settings including text-based person search, person re-identification, and dialog-based fashion retrieval.These applications combine natural-language descriptions with domain-specific visual retrieval tasks.

3 THE PROPOSED APPROACH

The approach builds a latent semantic tree for complex queries, selects constituents using semantic context, and combines structure-aware query encoding with temporal video modeling in a joint embedding space.

  • Overview: TCE embeds natural-language queries and videos into a joint space for matching and ranking.The framework targets content-based complex-query video retrieval.
  • 3.1 LST: Latent Semantic Tree: The latent semantic tree recursively merges adjacent nodes into a binary structure whose parent nodes represent increasingly complex query semantics.TreeLSTM transforms candidate parent nodes, while unselected nodes are carried forward through successive layers.
  • 3.1 LST: Latent Semantic Tree: Memory-augmented node scoring uses global leaf-level semantic context to select the parent candidate at each construction step.The scoring module injects semantic context into each selection decision, and the process continues until one node remains.
  • 3.2 Tree-augmented Query Encoder: An attention network aggregates constituent-node representations into a structure-aware query representation by weighting informative nodes.This avoids relying only on the final constituent, which may not cover all linguistic context in a complex query.
  • 3.3 Temporal-Attentive Video Encoder: The video encoder models consecutive-frame dependence with a GRU and whole-video frame interactions with multi-head self-attention.The resulting sequence representation captures both temporal dependence and frame-wise temporal interaction.
  • 3.4 Text-Video Joint Embedding: Training uses a margin-based loss averaged over the top hard negative videos to balance training stability and efficiency.The method avoids using only the hardest negative, which can destabilize training, and avoids averaging over all negatives, which can slow training.

4 EXPERIMENT

The experiment evaluates TCE against state-of-the-art methods, analyzes component contributions, and examines performance across different complex-query types.

  • 4 EXPERIMENT: The experiments address comparative performance, component impacts, and behavior on different types of complex queries.These questions are labeled R1, R2, and R3 in the experimental design.

4.1 Experimental Settings

Experiments evaluate TCE on MSR-VTT and LSMDC using rank-based retrieval metrics, reported under specified dataset splits and implementation settings.

  • Datasets: Experiments use MSR-VTT and LSMDC, with MSR-VTT evaluated across three dataset partitions.MSR-VTT contains 10K clips and 200K video-caption pairs; LSMDC contains 118,081 clips from 202 movies.
  • Implementation Details: MSR-VTT uses 512-dimensional word and hidden representations, with 256-dimensional query attention and 512-dimensional final query representations.The setup initializes word embeddings with a 500-D word2vec model and uses a unidirectional LSTM for leaf-node transformation.
  • Evaluation Results: The MSR-VTT comparison table reports percentage performance across dataset splits, while the LSMDC table reports percentage performance for 512-dimensional embeddings.The MSR-VTT experiment uses bidirectional GRU and LSTM, whereas the LSMDC table identifies motion and audio feature columns.
  • Implementation Details: LSMDC uses 300-dimensional GoogleNews word embeddings and provided multimodal video-level features because frame-level features were unavailable.The evaluation omits the gated embedding module and weighted-fusion similarity scoring used in prior work.
  • Evaluation Metrics: Performance is reported with R@K for K = 1, 5, 10 and Median rank, where higher R@K and lower MedR indicate better retrieval.R@K measures whether a relevant item appears among the top-K results; MedR is the median rank of the first relevant item.

4.2 Experimental Results and Analysis

Experiments compare TCE with state-of-the-art methods, isolate its query and video components, and test performance across query types and qualitative retrieval cases.

  • Comparison with State-of-the-Arts: TCE outperforms available methods across all three MSR-VTT dataset splits.The comparison includes RNN-based, multimodal fusion, and other state-of-the-art methods.
  • Ablation Studies: The full TCE model performs best in ablations except for MedR, while removing individual components causes relative performance degeneration.The reported ablations examine context, multihead attention, LSTM/GRU, and text/video attention modules.
  • Analysis on Different Types of Queries: TCE consistently outperforms DualGRU across query lengths, with larger gains for longer and generally more complex queries.The comparison uses MSR-VTT queries grouped by length and category.
  • Analysis on Different Types of Queries: TCE beats DualGRU across all query categories, although category difficulty varies substantially.Sports/actions exceed 0.25 performance, whereas news/politics reaches only about 0.13.

5 CONCLUSION

The paper presents a framework for complex-query video retrieval that automatically builds an interpretable latent semantic structure and jointly models query and video representations.

  • The proposed framework combines a tree-based complex query encoder with a temporal attentive video encoder.The query encoder models linguistic structure, while the video encoder models temporal dependence and frame-wise interaction.
  • The method automatically composes a latent semantic tree from words using memory-augmented node scoring and selection, without syntactic rules or annotations.An attention mechanism then encodes the tree into a structure-aware query representation.
  • The temporal attentive video encoder jointly models temporal dependence between frames and frame-wise temporal interaction before attentive pooling vectorizes the video.
  • The framework offers a novel direction by transforming complex queries into an easy-to-interpret structure without syntactic rules and annotations.
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