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arxiv: 2312.06950 · v3 · submitted 2023-12-12 · 💻 cs.CV · cs.CL

READ: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language Modeling

Thong Nguyen , Xiaobao Wu , Xinshuai Dong , Khoi Le , Zhiyuan Hu , Cong-Duy Nguyen , See-kiong Ng , Luu Anh Tuan This is my paper

Pith reviewed 2026-05-24 05:12 UTC · model grok-4.3

classification 💻 cs.CV cs.CL
keywords recurrent adapterparameter-efficient fine-tuningvideo-language modelingpartial optimal transporttemporal language groundinglow-resource learningadapter modulesvideo summarization
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The pith

A recurrent adapter with partial alignment lets pretrained video-language models adapt efficiently without losing temporal information.

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

The paper addresses the high cost and instability of fully fine-tuning large transformer models for video-language tasks when training data is limited. It proposes READ, a lightweight recurrent adapter inserted into the pretrained model that uses recurrent computation to model relations across video frames and text words. To keep critical task information from being lost when inputs are projected into the adapter's low-dimensional space, the method adds a PVLA objective based on partial optimal transport. Only the adapter parameters are updated during fine-tuning, which cuts storage needs and training variance. Experiments demonstrate that this setup outperforms existing fine-tuning and adapter approaches on low-resource temporal language grounding and video-language summarization benchmarks.

Core claim

We introduce lightweight adapters to the pre-trained model and only update them at fine-tuning time. However, existing adapters fail to capture intrinsic temporal relations among video frames or textual words. Moreover, they neglect the preservation of critical task-related information that flows from the raw video-language input into the adapter's low-dimensional space. To address these issues, we first propose a novel REcurrent ADapter (READ) that employs recurrent computation to enable temporal modeling capability. Second, we propose Partial Video-Language Alignment (PVLA) objective via the use of partial optimal transport to maintain task-related information flowing into our READ modules

What carries the argument

The READ module, which inserts recurrent computation into adapters for temporal modeling, paired with the PVLA objective that uses partial optimal transport to align and preserve task-critical video-language information.

If this is right

  • Only small adapter modules are stored and updated instead of entire models, lowering memory and compute demands for multiple tasks.
  • Temporal relations across video frames and text tokens are modeled directly inside the adapter.
  • Task-related information is retained during the projection to low-dimensional space via the partial alignment loss.
  • Training becomes more stable than full fine-tuning on limited data.
  • The same modules deliver gains on both temporal language grounding and video-language summarization.

Where Pith is reading between the lines

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

  • The same recurrent-plus-alignment pattern could be tested on other sequence-heavy multimodal tasks such as audio-visual event detection.
  • Partial optimal transport alignment may help in any adapter setting where input modalities are only partially matched.
  • Recurrent adapters might be stacked or combined with other efficiency methods like low-rank updates for further parameter reduction.
  • The results imply that recurrence is a general fix for the sequential information loss common to many adapter designs.

Load-bearing premise

Recurrent computation inside the adapter plus partial optimal transport alignment will capture temporal relations and preserve task-critical information that standard adapters lose without creating new failure modes.

What would settle it

A low-resource temporal language grounding benchmark where READ records lower performance than a standard non-recurrent adapter or than full fine-tuning of the base model.

Figures

Figures reproduced from arXiv: 2312.06950 by Cong-Duy Nguyen, Khoi Le, Luu Anh Tuan, See-kiong Ng, Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Zhiyuan Hu.

Figure 1
Figure 1. Figure 1: Examples of the TLG and VLS problems. TLG model needs to understand the meaning of language entities such as [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of our proposed READ method with [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overall illustration of the proposed recurrent [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
read the original abstract

Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal language grounding and video-language summarization. With a growing number of tasks and limited training data, such full fine-tuning approach leads to costly model storage and unstable training. To overcome these shortcomings, we introduce lightweight adapters to the pre-trained model and only update them at fine-tuning time. However, existing adapters fail to capture intrinsic temporal relations among video frames or textual words. Moreover, they neglect the preservation of critical task-related information that flows from the raw video-language input into the adapter's low-dimensional space. To address these issues, we first propose a novel REcurrent ADapter (READ) that employs recurrent computation to enable temporal modeling capability. Second, we propose Partial Video-Language Alignment (PVLA) objective via the use of partial optimal transport to maintain task-related information flowing into our READ modules. We validate our READ framework through extensive experiments where READ significantly outperforms all existing fine-tuning strategies on multiple low-resource temporal language grounding and video-language summarization benchmarks. The code, model, and data have been made available at https://nguyentthong.github.io/READ.

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

0 major / 2 minor

Summary. The manuscript proposes the READ framework for parameter-efficient fine-tuning of pretrained video-language transformers. It introduces a REcurrent ADapter (READ) module that uses recurrent computation to capture temporal relations among frames and words, together with a Partial Video-Language Alignment (PVLA) objective based on partial optimal transport to preserve task-critical information when projecting into the adapter's low-dimensional space. The central claim is that READ with PVLA significantly outperforms all existing fine-tuning strategies on multiple low-resource temporal language grounding and video-language summarization benchmarks; code, models, and data are released.

Significance. If the empirical results hold, the work provides a practical, lightweight adaptation method that directly targets documented shortcomings of standard adapters (temporal modeling and information loss) in data-scarce video-language settings. The release of code, models, and data is a clear strength that supports reproducibility and follow-up work.

minor comments (2)
  1. [Abstract] Abstract: the outperformance claim is stated at a high level without any quantitative deltas, baseline names, or benchmark identifiers; adding one or two headline numbers would make the abstract self-contained.
  2. The manuscript would benefit from an explicit statement of the number of trainable parameters in READ versus the compared adapters and full fine-tuning baselines.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, significance assessment, and recommendation of minor revision. No specific major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper introduces an empirical architecture (READ recurrent adapter) and training objective (PVLA via partial optimal transport) for parameter-efficient video-language modeling. All central claims rest on experimental validation across benchmarks rather than any derivation, equation, or self-referential reduction. No load-bearing step equates a claimed result to a fitted parameter or prior self-citation by construction; the method is presented as a practical proposal with released code and data.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The central claim rests on the standard assumption that pretrained transformers can be usefully adapted via small modules and that temporal modeling plus information preservation are the key missing pieces in prior adapters; no explicit free parameters or invented physical entities are described in the abstract.

axioms (2)
  • domain assumption Pretrained large-scale transformer models for video-language tasks can be effectively adapted by updating only lightweight modules rather than all parameters.
    Invoked by the decision to introduce adapters instead of full fine-tuning.
  • ad hoc to paper Recurrent computation inside an adapter is sufficient to capture intrinsic temporal relations among frames and words.
    Stated as the motivation for the READ design.
invented entities (2)
  • READ recurrent adapter module no independent evidence
    purpose: To add temporal modeling capability to parameter-efficient adapters
    New architectural component introduced in the paper
  • PVLA partial optimal transport objective no independent evidence
    purpose: To maintain task-related information when mapping raw inputs into the adapter's low-dimensional space
    New training objective introduced in the paper

pith-pipeline@v0.9.0 · 5774 in / 1434 out tokens · 22612 ms · 2026-05-24T05:12:38.226186+00:00 · methodology

discussion (0)

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Reference graph

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    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

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    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

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