PaperlayerView exact revision on arXiv

Source-linked AI summary

HVD: Human Vision-Driven Video Representation Learning for Text-Video Retrieval

Zequn Xie, Xin Liu, Boyun Zhang, Yuxiao Lin, Sihang Cai, Tao Jin

arXiv:2601.16155v1cs.CVcs.IR

TL;DR

With sparse text queries, existing methods may incorporate all visual features into text interaction without considering their effectiveness. HVD selects relevant frames and compresses patch features into visual entities, and it achieves state-of-the-art retrieval performance across five benchmarks.

  • Problem

    With sparse text queries, existing methods may incorporate all visual features into text interaction without considering their effectiveness.

    Source: §1 · p. 1

  • Method

    HVD selects key frames and compresses their patch features into visual entities for fine-grained alignment.

    Source: §1 · pp. 1–2

  • Results

    HVD achieves state-of-the-art retrieval performance on five benchmarks; on MSRVTT, R@1 is 48.8, improving by 0.2 over HBI.

    Source: §3.2 · p. 3, §4 · p. 4

  • Takeaways & Limitations

    Ablations and visualizations validate the effectiveness and intuitiveness of FFSM and PFCM.

    Source: §4 · p. 4

Abstract

from arXiv · show

The success of CLIP has driven substantial progress in text-video retrieval. However, current methods often suffer from "blind" feature interaction, where the model struggles to discern key visual information from background noise due to the sparsity of textual queries. To bridge this gap, we draw inspiration from human cognitive behavior and propose the Human Vision-Driven (HVD) model. Our framework establishes a coarse-to-fine alignment mechanism comprising two key components: the Frame Features Selection Module (FFSM) and the Patch Features Compression Module (PFCM). FFSM mimics the human macro-perception ability by selecting key frames to eliminate temporal redundancy. Subsequently, PFCM simulates micro-perception by aggregating patch features into salient visual entities through an advanced attention mechanism, enabling precise entity-level matching. Extensive experiments on five benchmarks demonstrate that HVD not only captures human-like visual focus but also achieves state-of-the-art performance.

1. INTRODUCTION

HVD addresses imprecise text-video alignment with a human-inspired coarse-to-fine framework that selects relevant frames and compresses their patches into visual entities. The introduction reports state-of-the-art retrieval performance on five benchmarks.

  • CLIP-based methods map text and video into a shared latent space, while later methods add granularity alignment and feature enhancement.Source: §1 · p. 1
  • With sparse queries, existing methods interact text with all visual features, including unrelated frames such as an interview scene.Source: §1 · p. 1, Figure 1 · p. 1
  • Prior methods and feature enhancement approaches still suffer noisy features and imprecise cross-modal matching.The introduction characterizes these limitations as weak robustness to distracting visual cues and sparse text, alongside interaction blind spots or insufficient information exchange.Source: §1 · p. 1
  • FFSM selects query-relevant key frames by similarity, eliminating redundant and irrelevant frames while retaining patches for fine-grained alignment.Source: §1 · pp. 1–2
  • HVD achieves state-of-the-art text-video retrieval performance across five benchmarks.Source: §1 · p. 2
  • PFCM compresses selected-frame patches with DPC-KNN, importance scores, and attention weights to form word-level visual entity features.Source: §1 · p. 2

2. METHODS

HVD uses CLIP features in a coarse-to-fine retrieval pipeline: FFSM selects text-relevant frames, PFCM compresses patches, and training and inference combine global and local similarities.

2.1. Preliminaries

  • CLIP encoders extract video frame and patch features and text word and sentence features, which support cross-modal contrastive alignment.The sentence representation is the [EOS] token, and each frame representation aggregates its [CLS] token.Source: §2.1 · p. 2

2.2. Frame Features Selection Module

  • FFSM performs human-inspired macro alignment by ranking frames against the sentence feature, selecting the top-N* frames, and updating their patch features.Frame ranking uses sentence-frame cosine similarity.Source: §2.2 · p. 2

2.3. Patch Features Compression Module

  • PFCM applies DPC-KNN to score patches by density and distance, merges neighbors around high-scoring cluster centers, and uses transformer attention to iteratively compress features.The merged patch serves as the query, while original patches provide keys and values; the module also focuses on key features and spatial relationships.Source: §2.3 · p. 2

2.4. Training and Sampling

  • Training jointly minimizes losses for sentence-frame and word-entity similarities, while inference aggregates global frame and local patch similarities to rank videos.The global representation comes from FFSM and the local representation from PFCM.Source: §2.4 · p. 3

3. EXPERIMENTS

HVD is evaluated across five retrieval benchmarks, with reported gains on MSRVTT, DiDeMo, and LSMDC and experiments examining component interaction and retention granularity.

3.1. Experimental Settings

  • The evaluation uses MSRVTT, DiDeMo, LSMDC, ActivityNet, and Charades, with CLIP, 12 sampled frames, and 0.5 frame and patch retention ratios.Training uses 5 epochs, batch size 32, feature dimension 512, and maximum text length 24; metrics are R@K (K=1, 5, 10), MdR, and MnR.Source: §3.1 · p. 3

3.2. Experimental Performance

  • 48.8 R@1 on MSRVTT is 0.2 higher than HBI, while HVD shows consistent performance improvements on DiDeMo and LSMDC.Source: Table 1 · p. 3, §3.2 · p. 3, Table 2 · p. 3
  • Combining macro frame selection (R2) with micro patch compression (R3) gives the best retrieval performance (R4), with FFSM refining PFCM input and PFCM adding complementary detail.Source: §3.2 · pp. 3–4, Table 3 · p. 4
  • Retaining 0.50 of frames and patches yields HVD’s best retrieval performance, indicating that an appropriate feature granularity can enhance feature interactions.The tested proportions are ℏ for video frames and ¯λ for image patches.Source: §3.2 · p. 4, Table 4 · p. 4
  • The visualization shows selected frames removed and successive PFCM operations reducing the number of features involved in text interaction.Non-red regions indicate frames used for text interaction.Source: §3.2 · p. 4, Figure 3 · p. 4

4. CONCLUSION

HVD combines frame selection and patch compression to improve coarse- and fine-grained text–video alignment. It achieves state-of-the-art retrieval performance on five benchmarks, with module effectiveness examined through ablations and visualizations.

4. CONCLUSION

  • HVD achieves state-of-the-art retrieval performance on MSRVTT, DiDeMo, LSMDC, ActivityNet, and Charades.The paper reports evaluation on five benchmark datasets.Source: §4 · p. 4
  • HVD integrates FFSM frame selection and PFCM patch compression to improve video representations at frame and patch levels.Together, the modules address coarse- and fine-grained alignment from human macroscopic and microscopic perspectives.Source: §4 · p. 4
  • Ablation studies on FFSM, PFCM, and hyper-parameter settings, alongside visualizations, further validate the modules’ effectiveness and intuitiveness.Source: §4 · p. 4

Related papers

Loading 2601.16155v1…