pith. sign in

arxiv: 2412.07584 · v2 · submitted 2024-12-10 · 💻 cs.CV · cs.AI

Multimodal Contextualized Support for Enhancing Video Retrieval System

Quoc-Bao Nguyen-Le , Thanh-Huy Le-Nguyen This is my paper

Pith reviewed 2026-05-23 07:12 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords video retrievalmultimodal datamultiple frameslatent meaningskeyframe analysisaction inferencecomputer vision
0
0 comments X

The pith

Video retrieval systems gain accuracy by extracting multimodal data from multiple frames to infer latent meanings instead of analyzing single keyframes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that video retrieval systems perform poorly on queries about actions or events because they analyze only individual keyframes, which supply too little context and restrict models to listing visible objects. The authors introduce a pipeline that pulls multimodal information across several frames of a clip so models can derive higher-level inferences about what is happening. If the approach works, retrieval results should improve for the kinds of dynamic descriptions users actually submit. A sympathetic reader would care because the change moves systems from static image matching toward understanding video content as sequences with implied meaning.

Core claim

Current video retrieval systems primarily focus on querying individual keyframes or images rather than encoding an entire clip or video segment. Queries often describe an action or event over a series of frames, not a specific image, resulting in insufficient information and less accurate results. The proposed system integrates the latest methodologies with a novel pipeline that extracts multimodal data and incorporates information from multiple frames within a video, enabling the model to abstract higher-level information that captures latent meanings inferred from the video clip rather than focusing only on object detection in one single image.

What carries the argument

Novel pipeline that extracts multimodal data from multiple frames to support higher-level inference of latent meanings.

If this is right

  • Retrieval models encode higher-level abstract insights from video clips instead of only describing objects in one frame.
  • Results improve for queries that describe actions or events spanning multiple frames.
  • Systems shift emphasis from object detection in isolated images to inferences drawn from the full clip.
  • Integration of multimodal data across frames supplies enough information for deeper understanding than image-only embeddings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same multi-frame extraction step could be applied to tasks such as video captioning or action recognition to test whether latent-meaning gains appear outside retrieval.
  • If the pipeline requires no extra labeled data, it might reduce reliance on large annotated video datasets for training.
  • Extending the approach to longer video segments would show whether context benefits scale with clip length or plateau after a few frames.

Load-bearing premise

Extracting and integrating multimodal data across multiple frames supplies the extra context models need to infer higher-level latent meanings and deliver more accurate retrieval than single-keyframe analysis.

What would settle it

A head-to-head test on a standard video retrieval benchmark using action-based queries, comparing retrieval precision of the multi-frame multimodal pipeline against a single-keyframe baseline.

Figures

Figures reproduced from arXiv: 2412.07584 by Quoc-Bao Nguyen-Le, Thanh-Huy Le-Nguyen.

Figure 1
Figure 1. Figure 1: The architecture of the CLIP model is shown on the left. However, Nomic Vision outperforms CLIP across all benchmarks. Our experience also confirms that Nomic is significantly superior to CLIP in visual-language retrieval tasks. Throughout the paper, we analyze the improved results achieved by our approach. In Section 3, we present and scientifically analyze the methodologies used in our system. Section 4 … view at source ↗
Figure 2
Figure 2. Figure 2: Although all the input texts describe a context involving a dog, ViClipB16 accurately interprets the sequence of frames and assigns the highest score to the first text, which also matches the query. Here, f(·) ∈ Rd is the feature vector from Dinov2, δ is the deduplication threshold (usually δ = 0.9), and (Re) is the set of frames to remove. We retain only Xi and delete the subsequent duplicate frames Xj wi… view at source ↗
Figure 3
Figure 3. Figure 3: Phi-35, enhanced with contextualized audio summaries, can grasp the high￾level concepts behind a clip, making it well-suited for complex and abstract queries. context to the model by incorporating processed audio. This audio is extracted using the Whisper-Large-V3 [11] model, corrected for spelling errors by GPT-4, Gemini, or Llama, and then summarized as segmented clips within a video. For instance, the f… view at source ↗
Figure 4
Figure 4. Figure 4: Query:"The video is presented through a series of consecutive colored drawings. The content of the drawings depicts a trial in court. There is an American flag in one of the drawings". In this example, we select the Nomic method from eight other options to query, and the correct result appears ranked first. When the user clicks on any frame, a modal displays a list of preceding and following frames. On the… view at source ↗
read the original abstract

Current video retrieval systems, especially those used in competitions, primarily focus on querying individual keyframes or images rather than encoding an entire clip or video segment. However, queries often describe an action or event over a series of frames, not a specific image. This results in insufficient information when analyzing a single frame, leading to less accurate query results. Moreover, extracting embeddings solely from images (keyframes) does not provide enough information for models to encode higher-level, more abstract insights inferred from the video. These models tend to only describe the objects present in the frame, lacking a deeper understanding. In this work, we propose a system that integrates the latest methodologies, introducing a novel pipeline that extracts multimodal data, and incorporate information from multiple frames within a video, enabling the model to abstract higher-level information that captures latent meanings, focusing on what can be inferred from the video clip, rather than just focusing on object detection in one single image.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that single-keyframe video retrieval systems lack sufficient context for action/event queries and proposes a novel pipeline that extracts multimodal data across multiple frames to enable higher-level abstraction of latent meanings, moving beyond object detection in isolated images.

Significance. If the pipeline demonstrably improves retrieval accuracy through temporal and multimodal context, it would address a practical limitation in video search systems. The manuscript, however, contains no experiments, datasets, metrics, or comparisons, so the significance remains entirely prospective rather than demonstrated.

major comments (2)
  1. [Abstract] Abstract: The central claim that multi-frame multimodal extraction 'enables the model to abstract higher-level information that captures latent meanings' and produces more accurate results is stated without any supporting evidence, implementation details, datasets, or quantitative evaluation.
  2. [Abstract] Abstract: No baseline comparisons, ablation studies, or metrics (e.g., recall@k, mAP) are provided to test the assumption that additional multi-frame context yields measurable gains over single-keyframe methods; the contribution is therefore a description rather than a validated improvement.
minor comments (1)
  1. [Abstract] Abstract: Grammatical inconsistency in 'extracts multimodal data, and incorporate information' (should be 'incorporates').

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed comments. We acknowledge that the manuscript presents a conceptual pipeline proposal without empirical validation, experiments, or quantitative results, and the claims in the abstract require tempering. We will revise to address this.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that multi-frame multimodal extraction 'enables the model to abstract higher-level information that captures latent meanings' and produces more accurate results is stated without any supporting evidence, implementation details, datasets, or quantitative evaluation.

    Authors: We agree that the abstract overstates the benefits without evidence. In revision, we will rewrite the abstract to describe the proposed pipeline as a conceptual framework for incorporating multi-frame multimodal context, removing any assertions of improved accuracy or higher-level abstraction without validation. We will add a limitations section noting the absence of implementation and evaluation. revision: yes

  2. Referee: [Abstract] Abstract: No baseline comparisons, ablation studies, or metrics (e.g., recall@k, mAP) are provided to test the assumption that additional multi-frame context yields measurable gains over single-keyframe methods; the contribution is therefore a description rather than a validated improvement.

    Authors: The observation is accurate: the manuscript offers no such comparisons or metrics because it focuses on pipeline design rather than empirical demonstration. We will revise the abstract and introduction to explicitly frame the work as a proposed methodology description, and include a future work subsection outlining potential evaluation protocols using standard video retrieval benchmarks. revision: yes

Circularity Check

0 steps flagged

No circularity: purely conceptual system description with no derivations, equations, or fitted predictions.

full rationale

The manuscript presents a high-level proposal for a multimodal multi-frame video retrieval pipeline but contains no equations, parameter fittings, self-citations used as load-bearing premises, or any derivation chain. The central claim—that integrating multimodal data across frames enables higher-level latent inferences—is asserted descriptively rather than derived from prior results or data fits within the paper. No load-bearing steps reduce to inputs by construction, satisfying the default expectation of no significant circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract contains no mathematical model, fitted parameters, axioms, or new postulated entities; the contribution is a high-level system proposal.

pith-pipeline@v0.9.0 · 5688 in / 1102 out tokens · 54998 ms · 2026-05-23T07:12:18.561106+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

14 extracted references · 14 canonical work pages · 7 internal anchors

  1. [1]

    Abdin, M., Aneja, J., Awadalla, H., Awadallah, A., Awan, A.A., Bach, N., Bahree, A., Bakhtiari, A., Bao, J., Behl, H., Benhaim, A., Bilenko, M., Bjorck, J., Bubeck, S., Cai, M., Cai, Q., Chaudhary, V., Chen, D., Chen, D., Chen, W., Chen, Y.C., Chen, Y.L., Cheng, H., Chopra, P., Dai, X., Dixon, M., Eldan, R., Fragoso, V., Gao, J., Gao, M., Gao, M., Garg, A...

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  2. [2]

    Chen, J., Xiao, S., Zhang, P., Luo, K., Lian, D., Liu, Z.: Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self- knowledge distillation (2024),https://arxiv.org/abs/2402.03216

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  3. [3]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Cherti, M., Beaumont, R., Wightman, R., Wortsman, M., Ilharco, G., Gordon, C., Schuhmann, C., Schmidt, L., Jitsev, J.: Reproducible scaling laws for contrastive language-image learning. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (Jun 2023).https://doi.org/10.1109/ cvpr52729.2023.00276, http://dx.doi.org/10.1109/CV...

    work page doi:10.1109/cvpr52729.2023.00276 2023

  4. [4]

    Doan, K.T., Huynh, B.G., Hoang, D.T., Pham, T.D., Pham, N.H., Nguyen, Q.T.M., Vo, B.Q., Hoang, S.N.: Vintern-1b: An efficient multimodal large language model for vietnamese (2024),https://arxiv.org/abs/2408.12480

    work page arXiv 2024

  5. [5]

    Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.E., Lomeli, M., Hosseini, L., Jégou, H.: The faiss library (2024)

    work page 2024

  6. [6]

    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015), https://arxiv.org/abs/1512.03385

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  7. [7]

    IEEE Transactions on Big Data7(3), 535–547 (2019)

    Johnson, J., Douze, M., Jégou, H.: Billion-scale similarity search with GPUs. IEEE Transactions on Big Data7(3), 535–547 (2019)

    work page 2019

  8. [8]

    Nussbaum, Z., Duderstadt, B., Mulyar, A.: Nomic embed vision: Expanding the latent space (2024),https://arxiv.org/abs/2406.18587 Multimodal Contextualized Support for Enhancing Video Retrieval System 9

    work page arXiv 2024

  9. [9]

    Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W.,Howes,R.,Huang,P.Y.,Li,S.W.,Misra,I.,Rabbat,M.,Sharma,V.,Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., Bojanowski, P.: Dinov2: Learning robust visual features without su...

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  10. [10]

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision (2021),https://arxiv.org/abs/ 2103.00020

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  11. [11]

    Radford,A.,Kim,J.W.,Xu,T.,Brockman,G.,McLeavey,C.,Sutskever,I.:Robust speech recognition via large-scale weak supervision (2022),https://arxiv.org/ abs/2212.04356

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  12. [12]

    Reis, D., Kupec, J., Hong, J., Daoudi, A.: Real-time flying object detection with yolov8 (2024), https://arxiv.org/abs/2305.09972

    work page arXiv 2024

  13. [13]

    Wang, J., Fu, X., Xiao, F., Tian, C.: Dhash: Enabling dynamic and efficient hash tables (2020), https://arxiv.org/abs/2006.00819

    work page arXiv 2020

  14. [14]

    Wang, Y., Li, K., Li, Y., He, Y., Huang, B., Zhao, Z., Zhang, H., Xu, J., Liu, Y., Wang, Z., Xing, S., Chen, G., Pan, J., Yu, J., Wang, Y., Wang, L., Qiao, Y.: Internvideo: General video foundation models via generative and discriminative learning (2022), https://arxiv.org/abs/2212.03191

    work page internal anchor Pith review Pith/arXiv arXiv 2022