arxiv: 2603.21901 · v2 · submitted 2026-03-23 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

CLEAR: Context-Aware Learning with End-to-End Mask-Free Inference for Adaptive Video Subtitle Removal

Qingdong He , Chaoyi Wang , Peng Tang , Yifan Yang , Xiaobin Hu

Authors on Pith no claims yet

Pith reviewed 2026-05-15 00:36 UTC · model grok-4.3

classification 💻 cs.CV

keywords video subtitle removalmask-free inferencediffusion modelscontext-aware adaptationLoRA fine-tuningself-supervised disentanglementzero-shot generalizationvideo restoration

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The pith

CLEAR removes video subtitles end-to-end without any masks by learning disentangled representations in one stage and refining them with generation feedback in the next.

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

The paper presents CLEAR as a mask-free alternative to existing diffusion methods that require explicit mask sequences at every step. It splits the task into two stages that first extract subtitle features through self-supervised orthogonality on dual encoders and then adapt the generator with low-rank updates driven by the output itself. This separation supports truly end-to-end inference while training only 0.77 percent of the base model's parameters. Results on Chinese benchmarks show clear gains over mask-dependent baselines, and the same model generalizes zero-shot to six other languages.

Core claim

CLEAR achieves mask-free subtitle removal by decoupling prior extraction from generative refinement: Stage I trains dual encoders under self-supervised orthogonality constraints to produce disentangled subtitle representations, while Stage II applies LoRA adaptation guided by generation feedback to adjust context dynamically without ground-truth masks.

What carries the argument

Two-stage architecture that uses self-supervised orthogonality constraints on dual encoders to disentangle subtitle representations and LoRA-based adaptation with generation feedback for dynamic context adjustment.

If this is right

On Chinese subtitle benchmarks the method records +6.77 dB PSNR and -74.7 percent VFID relative to mask-dependent baselines.
Zero-shot removal works across English, Korean, French, Japanese, Russian and German without retraining.
Training requires only 0.77 percent of the base diffusion model's parameters.
Inference proceeds without supplying ground-truth masks at any stage.

Where Pith is reading between the lines

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

The same feedback-driven adaptation could apply to other overlay-removal tasks such as logo or watermark erasure.
Reducing trainable parameters to under one percent opens the possibility of fine-tuning on modest hardware for domain-specific subtitles.
The disentanglement step may transfer to separating other transient elements like captions or annotations in video streams.

Load-bearing premise

Self-supervised orthogonality constraints on dual encoders can produce subtitle representations disentangled enough to support reliable removal without any masks at inference time.

What would settle it

A set of test videos containing subtitles where the method leaves visible text artifacts or distorts background content that a mask-guided baseline removes cleanly.

Figures

Figures reproduced from arXiv: 2603.21901 by Chaoyi Wang, Peng Tang, Qingdong He, Xiaobin Hu, Yifan Yang.

**Figure 1.** Figure 1: Qualitative visualization of the zero-shot cross-lingual generalization (Zoom in for best view). CLEAR achieves robust video subtitle removal across English, Korean, French, Japanese, Russian, and German without language-specific training. Abstract Video subtitle removal aims to distinguish text overlays from background content while preserving temporal coherence. Existing diffusion-based methods necessit… view at source ↗

**Figure 2.** Figure 2: CLEAR framework overview. Two-stage training enables mask-free inference: Stage I learns self-supervised priors Mprior through disentangled feature learning; Stage II trains LoRA-adapted diffusion with adaptive weighting optimized by distillation, generation feedback, and sparsity losses. Inference requires only subtitled video input. commonly produce flicker, temporal inconsistency, or residual artifacts… view at source ↗

**Figure 3.** Figure 3: CLEAR Pipeline Details. Stage I: Dual encoders extract disentangled features with orthogonality and adversarial losses. Stage II: Occlusion head predicts adaptive weights from DiT encoder features, modulating generation through focal weighting (ϵ gen) γ with gradient backflow. noisy due to lighting changes, semi-transparent subtitles, and motion blur at boundaries. Stage II is designed to robustly handle … view at source ↗

**Figure 4.** Figure 4: Illustration of our progressive mask refinement. Despite noisy pseudo-labels from Stage I (Mprior), Stage II learns adaptive weights (Mpred) that dynamically adjust based on generation difficulty, enabling robust context-aware modulation, which are internal to training and not predicted during inference. weighting strategies that adapt based on both prior structure and generation feedback. 3.3.2. DIFFUSIO… view at source ↗

**Figure 5.** Figure 5: Qualitative comparison with baseline methods. Unlike ProPainter, Minimax-Remover, and DiffuEraser that require explicit masks during inference, CLEAR achieves mask-free end-to-end removal with clean subtitle elimination and fine-grained detail preservation. achieves substantial improvements across all metrics: +6.77 dB PSNR over the best baseline (Minimax-Remover), - 74.7% VFID reduction indicating superio… view at source ↗

**Figure 6.** Figure 6: Visual comparison of output quality between the LoRAonly baseline and CLEAR (Ours). The baseline struggles with residual subtitles and background blurring, while our two-stage framework achieves clean subtitle removal and faithful preservation of structural details. generalization stems from learning abstract occlusion patterns rather than character-specific features. 4.3. Ablation Study Ablation of Com… view at source ↗

read the original abstract

Video subtitle removal aims to distinguish text overlays from background content while preserving temporal coherence. Existing diffusion-based methods necessitate explicit mask sequences during both training and inference phases, which restricts their practical deployment. In this paper, we present CLEAR (Context-aware Learning for End-to-end Adaptive Video Subtitle Removal), a mask-free framework that achieves truly end-to-end inference through context-aware adaptive learning. Our two-stage design decouples prior extraction from generative refinement: Stage I learns disentangled subtitle representations via self-supervised orthogonality constraints on dual encoders, while Stage II employs LoRA-based adaptation with generation feedback for dynamic context adjustment. Notably, our method only requires 0.77% of the parameters of the base diffusion model for training. On Chinese subtitle benchmarks, CLEAR outperforms mask-dependent baselines by + 6.77dB PSNR and -74.7% VFID, while demonstrating superior zero-shot generalization across six languages (English, Korean, French, Japanese, Russian, German), a performance enabled by our generation-driven feedback mechanism that ensures robust subtitle removal without ground-truth masks during inference.

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.

Desk Editor's Note private letter to a colleague

CLEAR gives a workable mask-free route for diffusion-based subtitle removal by pairing dual-encoder orthogonality with cheap LoRA feedback, but the disentanglement step still needs tighter checks.

read the letter

The main takeaway is that this paper removes the mask requirement at inference for video subtitle removal by splitting the task into two stages: first a pair of encoders trained with self-supervised orthogonality to pull apart subtitle features, then a LoRA-adapted diffusion step that uses generation feedback to clean up the output. It trains on only 0.77 percent of the base model parameters and reports +6.77 dB PSNR plus a 74.7 percent VFID drop on Chinese data, with zero-shot results holding across English, Korean, French, Japanese, Russian, and German. That efficiency and the cross-language angle are the practical wins here; prior diffusion subtitle work stayed tied to explicit masks, so dropping them at test time matters for real editing pipelines. The low parameter count also makes the approach easier to adapt than full fine-tuning. The soft spot is the core assumption that orthogonality on the dual encoders produces clean semantic separation. Linear independence in feature space does not automatically block leakage when text edges or colors overlap with scene content, and the zero-shot claims rest on that separation holding outside the training distribution. The abstract gives the headline numbers but leaves the exact baselines, ablation controls, and temporal consistency checks thin, so it is hard to judge how much the gains come from the new pieces versus careful tuning. If the full experiments include strong ablations on the orthogonality term and show the encoders really isolate subtitle signals without ground-truth masks, the method looks usable. This is aimed at people building video tools or adapting diffusion models for editing tasks. A reader who needs mask-free inference or low-cost adaptation would find the design details and the reported generalization useful. I would send it for peer review; the efficiency claim and the mask-free result are concrete enough to merit referee time even if the validation needs more detail.

Referee Report

2 major / 1 minor

Summary. The paper introduces CLEAR, a mask-free framework for adaptive video subtitle removal using a two-stage design. Stage I learns disentangled subtitle representations through self-supervised orthogonality constraints on dual encoders. Stage II uses LoRA-based adaptation with generation feedback for dynamic context adjustment. It reports outperforming mask-dependent baselines by +6.77dB PSNR and -74.7% VFID on Chinese benchmarks and superior zero-shot generalization to six languages, while training only 0.77% of the base model's parameters.

Significance. Should the central claims hold, this would represent a meaningful advance in video editing by enabling truly end-to-end inference without masks, addressing a practical limitation in diffusion-based approaches. The low parameter overhead and cross-lingual performance are strengths that could broaden applicability in real-world scenarios.

major comments (2)

[Stage I] Stage I, orthogonality constraints: The self-supervised orthogonality constraints on dual encoders are claimed to produce disentangled subtitle representations sufficient for mask-free inference. However, orthogonality enforces linear independence in feature space but does not guarantee semantic separation when subtitle pixels share low-level statistics with background content (e.g., edges or hues). This assumption is load-bearing for the +6.77 dB PSNR claim and zero-shot cross-language results; targeted ablations or feature visualizations are needed to validate it.
[Results] Experimental results: The reported gains (+6.77 dB PSNR, -74.7% VFID) and zero-shot generalization across six languages depend on the disentanglement holding outside the Chinese training distribution, but the manuscript provides limited detail on how the generation feedback mechanism prevents leakage from residual subtitle features into the refined output.

minor comments (1)

[Abstract] Abstract: A brief note on the specific Chinese subtitle datasets used would help contextualize the quantitative benchmarks immediately.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important aspects of our method's assumptions and mechanisms. We address each major point below and will incorporate targeted revisions to strengthen the manuscript.

read point-by-point responses

Referee: [Stage I] Stage I, orthogonality constraints: The self-supervised orthogonality constraints on dual encoders are claimed to produce disentangled subtitle representations sufficient for mask-free inference. However, orthogonality enforces linear independence in feature space but does not guarantee semantic separation when subtitle pixels share low-level statistics with background content (e.g., edges or hues). This assumption is load-bearing for the +6.77 dB PSNR claim and zero-shot cross-language results; targeted ablations or feature visualizations are needed to validate it.

Authors: We agree that linear independence via orthogonality does not automatically ensure semantic disentanglement when low-level features overlap. While our empirical results (including cross-lingual zero-shot performance) indicate effective separation in practice, we will add targeted ablations and visualizations in the revised manuscript. These will include t-SNE projections of dual-encoder features before/after the orthogonality loss, quantitative metrics such as mutual information between subtitle and background subspaces, and controlled experiments on synthetic edge/hue-overlap cases. This will directly validate the assumption's contribution to the reported gains. revision: yes
Referee: [Results] Experimental results: The reported gains (+6.77 dB PSNR, -74.7% VFID) and zero-shot generalization across six languages depend on the disentanglement holding outside the Chinese training distribution, but the manuscript provides limited detail on how the generation feedback mechanism prevents leakage from residual subtitle features into the refined output.

Authors: We acknowledge the need for greater detail on leakage prevention. In the revision we will expand Section 3.2 with an explicit description of the generation feedback loop, including the iterative loss formulation that penalizes residual subtitle signals detected via the Stage-I encoder on the refined output. We will also add analysis (e.g., feature-norm comparisons and qualitative residual maps) demonstrating reduced leakage across the six languages, showing how the LoRA adaptation dynamically suppresses subtitle remnants without requiring masks at inference. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper presents a two-stage framework relying on newly introduced self-supervised orthogonality constraints for disentangled subtitle representations in Stage I and LoRA-based adaptation with generation feedback in Stage II. These mechanisms are defined as architectural and training innovations without any equations or claims reducing the outputs to fitted parameters by construction, self-citations that bear the central load, or uniqueness theorems imported from prior author work. Performance claims rest on empirical benchmark comparisons rather than tautological re-derivations of inputs, rendering the chain self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; the approach builds on standard diffusion models and LoRA from prior literature while adding self-supervised orthogonality constraints and generation feedback.

pith-pipeline@v0.9.0 · 5498 in / 1151 out tokens · 47078 ms · 2026-05-15T00:36:26.687450+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Stage I learns disentangled subtitle representations via self-supervised orthogonality constraints on dual encoders... L_ortho = 1/(T·H'·W') Σ <F_sub, F_content>²
IndisputableMonolith/Foundation/BranchSelection.lean branch_selection unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

LoRA-based adaptation with generation feedback for dynamic context adjustment... only 0.77% of the parameters

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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Remove the text overlays and subtitles from this video while preserving the original background content and maintaining temporal consistency across frames

11 CLEAR: Context-Aware Learning with End-to-End Mask-Free Inference for Adaptive Video Subtitle Removal We introduce a context-dependent occlusion head H with only 2.1M parameters attached to the diffusion transformer’s middle encoder layer. The head consists of two convolutional layers: H(henc) =Conv 1 1×1(SiLU(Conv64 3×3(henc))), where henc represents ...

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