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REVIEW 4 major objections 4 minor 54 references

Object removal works best when the model reasons about background, not when it blocks attention

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-03 14:03 UTC pith:TIIPA3IX

load-bearing objection Solid, genuinely novel method under a headline claim that its own extended tables contradict — worth refereeing, but the quantitative story needs a major revision. the 4 major comments →

arxiv 2512.21545 v2 pith:TIIPA3IX submitted 2025-12-25 cs.CV

EraseLoRA: MLLM-Driven Foreground Exclusion and Background Subtype Aggregation for Dataset-Free Object Removal

Sanghyun Jo , Donghwan Lee , Eunji Jung , Seong Je Oh , Kyungsu Kim This is my paper
classification cs.CV
keywords dataset-free object removalbackground-aware reasoningmultimodal large language modeltest-time adaptationLoRAcross-attention guidanceforeground exclusionimage inpainting
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

EraseLoRA aims to establish that dataset-free object removal fails not because diffusion models lack capacity, but because previous methods mistake non-target objects for background and surgically disable attention, blurring the very cues needed to reconstruct what was hidden. It proposes replacing attention surgery with two ideas: an MLLM that separates the scene into target object, other foreground objects, and clean background from a single image-mask pair, and test-time LoRA adaptation that pulls multiple background subtypes into the mask through reconstruction and alignment losses. The paper reports that this yields at least 23% higher background similarity than prior dataset-free methods, roughly halves unwanted foreground regeneration, and surpasses dataset-driven approaches despite requiring no paired training data. A sympathetic reader would care because it suggests background-aware reasoning plus lightweight per-image adaptation can substitute for expensive paired data in a practical editing task.

Core claim

The paper's central claim is that masked targets reappear not because of attention leakage but because of a conceptual error: methods treat the masked area as the only foreground, so objects outside the mask that should be excluded are treated as reference background and regenerated. EraseLoRA asserts that once the scene is correctly partitioned into target foreground, non-target foregrounds, and clean background, a diffusion model can be steered by test-time LoRA optimization with two losses: an anchor loss that keeps latent background pixels faithful to the original, and a 'puzzle' loss that forces each background subtype's cross-attention to concentrate in the mask and contribute without

What carries the argument

The load-bearing objects are the region label function that divides the latent space into target foreground, non-target foreground, and clean background; the background reconstruction loss that anchors the output latent to the input latent on clean-background pixels; and the background puzzle loss, composed of an alignment term that keeps cross-attention inside valid regions and a diversity term that prevents one background tag from monopolizing the mask. Low-rank adaptation (LoRA) adapters inserted into the frozen diffusion backbone carry the test-time optimization, so no paired data or explicit attention blocking is used.

Load-bearing premise

The whole pipeline trusts that the MLLM, from a single image and mask, can correctly distinguish non-target foregrounds from occluded background; the paper's appendix admits that when true foreground objects are mislabelled as background subtypes, the reconstruction loss follows the wrong cues and regenerates object traces.

What would settle it

Take a set of paired before/after images with ground truth and run EraseLoRA twice: once with correct MLLM tags and once with selected real foreground objects deliberately relabelled as background subtypes. If the relabelled run shows no drop in background similarity and no residual traces, the method does not actually depend on the MLLM's partition being correct; if it collapses, the central claim and its dependency are confirmed.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • Plugging the foreground-exclusion stage into prior dataset-free methods improves their background similarity by up to 6.6% and reduces foreground regeneration by up to 8.6%, so the diagnosis transfers beyond EraseLoRA itself.
  • The framework runs on different diffusion backbones and MLLMs, including 7B models, with consistent gains; the improvement is not tied to one model family.
  • EraseLoRA can remove multiple masked objects jointly, and for video frames sharing a background it optimizes once on one frame and reuses the adapted model across frames, amortizing adaptation cost.
  • The LoRA weights merge into the frozen backbone after adaptation, so final inference incurs no extra parameters or latency beyond the base diffusion model.

Where Pith is reading between the lines

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

  • If the reported 23% background-similarity gain and halved foreground regeneration hold under third-party unpaired metrics, the practical takeaway is that a strong MLLM plus per-image adaptation can substitute for curated removal datasets, shifting the cost center from data collection to inference-time compute.
  • The method's success makes a testable prediction: removal quality should track the quality of the MLLM's scene partition, not the diffusion model's raw inpainting strength; an experiment with deliberately mislabelled tags would isolate this dependency.
  • The puzzle-loss mechanism is general: any task that needs to transplant multiple contextual cues into a masked region, such as reflection removal, logo replacement, or image harmonization, could reuse the same alignment-plus-diversity objective.
  • Because one-shot adaptation transfers across frames sharing a background, a natural extension is a video object-removal benchmark where per-frame cost is amortized; the paper shows a preliminary version but does not fully evaluate it.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes EraseLoRA, a dataset-free object-removal framework. Stage 1 (BFE) uses an MLLM to separate target foreground, non-target foregrounds, and background tags from a single image-mask pair, then uses Tag2Mask models to localize the non-target foreground regions. Stage 2 (BRSA) performs test-time LoRA adaptation with a background reconstruction loss (Eq. 2) and a background puzzle loss (Eqs. 3-5) to reconstruct the masked region from background subtypes. Experiments on OpenImages V7, RORD, and RemovalBench compare against dataset-free and dataset-driven baselines, with claims of a 23%+ background-similarity improvement over dataset-free methods, nearly halved foreground regeneration, and superiority over all dataset-driven methods on both background fidelity and foreground suppression.

Significance. The methodological direction is promising: replacing hard attention blocking with MLLM-guided background reasoning and test-time LoRA adaptation is a clean, well-motivated idea. The ablations in Tables 3 and 4 provide credible evidence that BFE and the two loss terms each contribute to the reported gains. The paper also demonstrates unusual breadth by testing multiple diffusion backbones, multiple MLLMs, and multiple Tag2Mask models, and it releases refined three-label masks. However, the headline quantitative claims are not supported by the manuscript's own data: the FG Sim metric is coupled to BG Sim through Eq. (7), and the extended Table 8 shows a dataset-driven method beating EraseLoRA on both BG Sim and FG Sim on RORD. The core method can still be a solid contribution after these claims are corrected, but the current framing overstates the results.

major comments (4)
  1. [Appendix B.1.2, Eq. (7); Section 4.2] FG Sim is defined as (1 - BG Sim) * cos_sim(...). Because EraseLoRA raises BG Sim, its FG Sim is multiplied by a smaller factor than baselines, so the reported reduction from 0.286 to 0.151 (OpenImages) and 0.319 to 0.141 (RORD) is partly mechanical. Reconstructing the unweighted cosine from Table 2 gives SD3.5-M raw FG ≈ 0.286/0.395 = 0.724 vs. EraseLoRA raw FG ≈ 0.151/0.254 = 0.594 on OpenImages, and 0.763 vs. 0.624 on RORD. That is an ~18% relative reduction, not 'nearly halving'. The paper should report unweighted foreground cosine similarity alongside the weighted score, and the abstract's 'nearly halving' claim should be revised.
  2. [Table 8 vs. Abstract and Section 4.2] The abstract states that EraseLoRA 'surpasses all dataset-driven approaches in both aspects', and Section 4.2 says it attains the highest BG Sim and lowest FG Sim on both benchmarks. The paper's own extended comparison contradicts this: on RORD, OmniEraser has BG Sim 0.786 vs. EraseLoRA 0.774 and FG Sim 0.131 vs. EraseLoRA 0.141, i.e., OmniEraser is better on both of these aspects. This is a load-bearing claim that must be reconciled. Either the claim should be restricted to the Table 2 baseline set, or the paper should acknowledge that dataset-driven methods such as OmniEraser remain competitive/superior on some benchmarks, and reframe the contribution as dataset-free state-of-the-art with competitive performance against trained methods.
  3. [Appendix B.1.1 and Fig. 18] The main hyperparameters—λ = 0.2, τ = 100, LoRA rank = 32, and TTA iterations = 500—are selected using the same benchmarks on which final numbers are reported, with no held-out validation split. Since the method is presented as dataset-free (no training data), the fact that test-set statistics are used for hyperparameter selection should be disclosed prominently. A sensitivity analysis on a separate validation set, or at least a statement that these values were fixed before benchmarking, is needed to avoid inflating the comparisons.
  4. [Appendix E.1] The paper candidly states that when true foreground tags are misclassified as background subtypes, BRSA 'follows incorrect cues and regenerates residual object traces.' Since BFE is the input premise of the whole pipeline, this failure mode deserves a quantitative treatment: e.g., how often does the default MLLM/Tag2Mask pipeline make such misclassifications on the evaluation sets, and how much do final BG Sim/FG Sim improve when those misclassifications are manually corrected? Without this, the robustness of the central contribution is not fully characterized.
minor comments (4)
  1. [Abstract vs. Section 1/6] The abstract gives a public code URL, while the main text says 'The code will be made available upon publication.' Please make these consistent.
  2. [Section 4.2, Table 8] The statement that EraseLoRA's BG Pres. (~0.93) is 'about 0.20 higher than all five dataset-driven methods' ignores FLUX.1-Fill-dev in the extended Table 8, which reports BG Pres. 0.945 on OpenImages. Clarify that the comparison is to the five methods in Table 2 only.
  3. [Figure 18] The plotted iteration and rank values are hard to read and the axis labels are visually disconnected from the data points. Please reformat for clarity.
  4. [Appendix C.1] The GPT-Metric is introduced to assess 'removal success' and 'semantic perceptual score', but the exact prompt template and evaluation protocol are not given. For reproducibility, include the prompt and the GPT model version used.

Circularity Check

1 steps flagged

FG Sim. is defined as (1−BG Sim.)×cosine, so the reported 'nearly halving' of foreground regeneration is partly forced by the BG Sim. gain.

specific steps
  1. self definitional [Appendix B.1.2, Eq. (7); Abstract; Sec. 4.2 / Table 2]
    "To discourage background-inconsistent restoration, we weight this score by (1−BG Sim): FG Sim.= (1−BG Sim.)· f(I[F])·f(Î[R])/(∥f(I[F])∥∥f(Î[R])∥) (7) ... [Abstract:] nearly halving unwanted foreground re-generation."

    Eq. (7) defines the reported FG Sim. as raw foreground cosine multiplied by (1−BG Sim.), so a high BG Sim. mechanically lowers FG Sim. From Table 2, EraseLoRA's BG Sim. is 0.746 (OpenImages) / 0.774 (RORD), scaling FG Sim. by 0.254/0.226; SD3.5-M's 0.605/0.582 is scaled by 0.395/0.418. Reconstructing the unweighted cosine: EraseLoRA ≈0.151/0.254=0.594 vs SD3.5-M ≈0.286/0.395=0.724 on OpenImages (~18% reduction, not 'nearly halving'). On RORD, OmniEraser's unweighted ≈0.131/0.214=0.612 is lower than EraseLoRA's ≈0.141/0.226=0.624, contradicting 'surpasses all dataset-driven approaches in both aspects.' The headline is thus partly built into the metric's definition.

full rationale

EraseLoRA is primarily an empirical method paper; the core pipeline (BFE with MLLM+Tag2Mask, BRSA with LoRA test-time adaptation) is not derived from a self-citation chain, and the cited prior work is almost entirely external (diffusion backbones, MLLMs, metrics). The self-citations (e.g., Kim et al. 2025c for the BG Pres. protocol) are minor and not load-bearing. The one concrete reduction I can exhibit is the FG Sim. metric in Eq. (7): because FG Sim. is defined as (1−BG Sim.) times raw foreground similarity, the 'nearly halving' of foreground regeneration is partially an artifact of the method's high BG Sim.; unweighting shows a smaller raw-FG gap and even a loss to OmniEraser on RORD. Other claims (BG Sim. gains, backbone/MLLM generalization, paired RemovalBench numbers) are measured with externally defined DINO/SSIM/VLM metrics and retain independent content. This is a partial, metric-level circularity rather than a fully forced derivation, hence the score of 6.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 0 invented entities

The method's contribution is a pipeline of model choices and test-time objectives; it does not derive constants from first principles. Several hyperparameters are tuned on the same benchmarks used for the reported gains, and the evaluation relies on author-defined masks and metrics. No new physical or conceptual entities are postulated.

free parameters (4)
  • λ (puzzle loss weight) = 0.2
    Hand-set weight balancing L_recon and L_puzzle in L_total; no sensitivity analysis across benchmarks is shown.
  • τ (cross-attention temperature) = 100
    Temperature for tag-wise softmax normalization in the puzzle loss; fixed after experimental selection.
  • LoRA rank = 32
    Selected from {16,32,64,128} based on BG Sim/FG Sim on the same evaluation benchmark (Fig. 18).
  • TTA iterations = 500
    Selected from {100,200,300,400,500} on the same evaluation benchmark (Fig. 18).
axioms (5)
  • domain assumption MLLM can infer occluded background tags behind a masked target from a single image-mask pair.
    BFE (§3.1) relies on this. Appendix E admits true foreground tags misclassified as background cause residual traces.
  • domain assumption Tag2Mask models localize all MLLM-predicted tags accurately; hallucinated tags are discarded.
    §3.1: 'The union of these localized regions defines the non-target foreground mask'. If localization fails, clean-background labels are wrong.
  • domain assumption Cross-attention maps for background subtype tags reflect spatial flow into the mask and can be steered by LoRA.
    Puzzle loss (Eqs. 3-5) uses A_b[p]. Appendix E says weak image-text alignment leads to inaccurate subtype aggregation (Fig. 21).
  • domain assumption DINOv3 feature similarity between the input background region and the reconstructed mask region is a valid unpaired measure of removal fidelity.
    Eqs. (6)-(7). No validation that DINO features respond to background identity rather than low-level texture; metric is author-defined.
  • domain assumption Manually curated three-label masks for evaluation are reliable ground truth.
    §4.1: 'we annotate them with three-label ... ground-truth masks'. No inter-annotator agreement or independent release is provided.

pith-pipeline@v1.3.0-alltime-deepseek · 23990 in / 10356 out tokens · 103646 ms · 2026-08-03T14:03:45.552498+00:00 · methodology

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Cite this review

Pith. "Pith review of EraseLoRA: MLLM-Driven Foreground Exclusion and Background Subtype Aggregation for Dataset-Free Object Removal." pith.science (2026). https://pith.science/paper/TIIPA3IX

@misc{pith2026251221545,
  author       = {Pith},
  title        = {Pith review of: EraseLoRA: MLLM-Driven Foreground Exclusion and Background Subtype Aggregation for Dataset-Free Object Removal},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TIIPA3IX}},
  note         = {Machine review of arXiv:2512.21545}
}
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read the original abstract

Object removal must prevent the masked target from reappearing and reconstruct the occluded background with structural and contextual fidelity, rather than merely filling a hole plausibly. Recent dataset-free approaches manipulate the diffusion model's internal self-attention to prevent it from referencing the masked region, yet they fail in two critical ways: (i) they treat the masked region as the sole foreground, misinterpreting non-target objects as background and regenerating them, and (ii) they apply uniform attention constraints without distinguishing diverse background subtypes, leading to textural blurring and structural misalignment. Both failures stem from the absence of explicit background-aware reasoning. We propose EraseLoRA, a dataset-free framework that replaces attention surgery with background-aware reasoning and test-time adaptation. The first stage, Background-aware Foreground Exclusion (BFE), leverages a multimodal large-language model to separate target foreground, non-target foregrounds, and clean background from a single image-mask pair. The second stage, Background-aware Reconstruction with Subtype Aggregation (BRSA), performs test-time optimization that treats inferred background subtypes as complementary pieces, enforcing their consistent integration through reconstruction and alignment objectives without explicit attention intervention. As a model-agnostic plug-in applicable to diverse diffusion backbones, EraseLoRA reconstructs backgrounds at least 23% more faithful to the original scene than previous dataset-free methods while nearly halving unwanted foreground re-generation, and surpasses all dataset-driven approaches in both aspects despite requiring no training data. Code is available at https://shjo-april.github.io/EraseLoRA.

Figures

Figures reproduced from arXiv: 2512.21545 by Donghwan Lee, Eunji Jung, Kyungsu Kim, Sanghyun Jo, Seong Je Oh.

Figure 1
Figure 1. Figure 1: Qualitative comparison with prior dataset-free methods. Previous state￾of-the-art approaches (Jia et al., 2025; Sun et al., 2025) treat only the masked region as foreground, misinterpreting non-target objects as background and regenerating them. EraseLoRA identifies and excludes non-target foregrounds and reconstructs the masked re￾gion using background cues, enabling faithful object removal. 1 Introductio… view at source ↗
Figure 2
Figure 2. Figure 2: Artifacts from attention manipulation. Recent dataset-free methods (Jia et al., 2025; Sun et al., 2025) directly modify attention inside the mask, leading to blurred or distorted background textures, whereas EraseLoRA aggregates background subtypes without attention blocking and preserves sharp, coherent structures. walls or wooden fences (see [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Background-aware reasoning power of MLLM. Unlike prior works (Kim et al., 2025a; Wang et al., 2024; Qu et al., 2025; Zhou et al., 2025) employ MLLMs for visual reasoning over the visible scene, we first leverage MLLMs to infer background cues behind the masked target. Recent state-of-the-art dataset-free approaches (Jia et al., 2025; Sun et al., 2025) redi￾rect or suppress self-attention within the masked … view at source ↗
Figure 4
Figure 4. Figure 4: Overview of EraseLoRA. BFE (section 3.1) separates target foreground, non￾target foregrounds, and background from a single image-mask pair using an MLLM (Zhu et al., 2025a) and Tag2Mask models (Liu et al., 2024; Ravi et al., 2025). After produc￾ing clean background cues, BRSA (section 3.2) performs test-time adaptation (Wang et al., 2020) with reconstruction and alignment objectives, coherently integrating… view at source ↗
Figure 5
Figure 5. Figure 5: Identification of non-target foregrounds. Prior methods (Chen et al., 2024; Sun et al., 2025) treat the entire unmasked region as background, which causes regener￾ation of non-target foregrounds. In contrast, EraseLoRA explicitly identifies non-target foregrounds within the mask and excludes them, producing clean background. semantic tags in the image and classifies the masked object as the target foregrou… view at source ↗
Figure 6
Figure 6. Figure 6: Effect of the background puzzle loss. We visualize how each background subtype (spectator, advertisement, grass) is represented inside the mask. The background puzzle loss ensures structurally coherent integration of background subtypes within the mask, unlike the weak integration without it. Background Reconstruction Loss. To preserve regions that are confidently iden￾tified as clean background by BFE (se… view at source ↗
Figure 7
Figure 7. Figure 7: Illustration of evaluation metrics (i.e., BG Sim. and FG Sim.) for unpaired object removal. non-target foregrounds and improving spatial consistency: Lalign = 1 − Dice A dom, 1{ℓ(p)∈{0,2}} , (4) where Dice(·, ·) measures the spatial agreement between attention activation and valid re￾gions (see details in Appendix B). Here, Adom[p] = maxb∈B Ab[p] denotes the dominant attention response at location p, and… view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative comparison on OpenImages V7 (Kuznetsova et al., 2020) and [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Effect of MLLM-driven non-target foreground exclusion (section 3.1) in dataset [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Visualization of loss components in BRSA (section 3.2). [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Examples of EraseLoRA in occlusion cases. Guided by the MLLM (Bai et al., 2025), EraseLoRA keeps nearby non-target foregrounds and instead treats occluded content (e.g., a painting, stairs, a tire) as background tags to be reconstructed from context. consistent gains (Tab. 6), confirming that our framework reliably leverages the background￾aware reasoning ability of MLLMs rather than depending on a specif… view at source ↗
Figure 12
Figure 12. Figure 12: Full MLLM prompt used in BFE (section 3.1) for background-aware reasoning [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Artifacts from disrupting short-range self-attention. Prior self-attention control methods (Jia et al., 2025; Sun et al., 2025) suppress short-range activations (b), which erodes fine details and produces blur and checkerboard artifacts in the reconstructed background (c). B.2 Design Rationale B.2.1 Limitations of Attention Surgery Previous state-of-the-art dataset-free methods (Chen et al., 2024; Jia et … view at source ↗
Figure 14
Figure 14. Figure 14: Color discrepancies on paired OmniPaint-Bench (Yu et al., 2025) dataset. Between before/after object removal images, there are substantial color mismatch, as shown in the difference heatmap. Appendix C. Additional Quantitative Results C.1 Experimental Setup This section provides extended evaluation details on baselines, datasets, and metrics. Baselines. Beyond the main comparison in Tab. 2, we additionall… view at source ↗
Figure 15
Figure 15. Figure 15: Qualitative results across MLLMs. Clean background reconstruction and strong foreground suppression are consistently achieved across diverse MLLMs (Liu et al., 2023; Zhao et al., 2024; Bai et al., 2025; Zhu et al., 2025a). et al., 2025; Sun et al., 2025). Although EraseLoRA requires additional computation costs during BRSA (section 3.2) due to LoRA adapters (see Tab. 7), EraseLoRA incurs no extra cost at … view at source ↗
Figure 16
Figure 16. Figure 16: Qualitative results across Tag2Mask models. Different Tag2Mask mod￾els (Kim et al., 2025b; Wang et al., 2025; Liu et al., 2024; Ravi et al., 2025; Carion et al., 2025) reliably localize non-target foreground regions, enabling complete background recon￾struction without foreground traces. YOLOE (Wang et al., 2025), SAM3 (Carion et al., 2025) and Grounded SAM2 (Grounding DINO (Liu et al., 2024) and SAM2 (Ra… view at source ↗
Figure 17
Figure 17. Figure 17: Applicability across diffusion architectures. Consistent clean background restoration on SD3.5-M and FLUX.1 (Esser et al., 2024; Labs, 2023) across various modern text-to-image diffusion backbones (Esser et al., 2024; Labs, 2023). This backbone-agnostic behavior is also clearly observed in qualitative results, where it sta￾bly removes target objects without foreground traces or noise, while preserving fin… view at source ↗
Figure 18
Figure 18. Figure 18: Effect of test-time optimization (Wang et al., 2020) capacity. Varying LoRA (Hu et al., 2022) rank and number of iterations shows that rank 32 and 500 iterations yield the best removal quality. Input & Mask LLaVA-7B MARINE Intern3-VL-78B [PITH_FULL_IMAGE:figures/full_fig_p028_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Impact of hallucination in MLLMs. Mitigating hallucination (Zhao et al., 2024) offers negligible gains, whereas stronger visual reasoning (Zhu et al., 2025a) enables accurate foreground exclusion and background cue extraction, leading to superior object removal. ground reconstruction (see [PITH_FULL_IMAGE:figures/full_fig_p028_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Interactive Control. (Right) Our interactive interface allows users to gen￾erate customized non-target foreground masks for BFE (section 3.1) based on manual points or bounding boxes and background tags. (Left) These human-guided non-target foreground exclusion and background tag selection effectively correct challenging failure cases of EraseLoRA. els (MLLMs), where models describe objects that do not ex… view at source ↗
Figure 21
Figure 21. Figure 21: Dependency of text-image alignment power. When EraseLoRA is applied to diffusion models with weak text–image alignment (Rombach et al., 2022; Podell et al., 2023), inaccurate subtype aggregation produces unintended object artifacts or structure￾inconsistent background. on the input image, and (vi) clicking the save button to store the manual results for BRSA (section 3.2). Based on this guidance, the corr… view at source ↗
Figure 22
Figure 22. Figure 22: Failure cases of EraseLoRA. Our method fails in two cases: (1) object traces by misclassified background tags and (2) presence of object effects. large MLLMs (Bai et al., 2025; Zhu et al., 2025a) alleviates this issue, but fully preventing semantic misclassification remains an open challenge. Presence of object effects. EraseLoRA removes the target object and synthesizes plausible background texture, but … view at source ↗
Figure 23
Figure 23. Figure 23: Efficiency of video extension. For video frames sharing similar background context, one-shot optimization on a single frame can be reused across the sequence, achiev￾ing performance comparable to per-frame optimization while reducing adaptation cost by the number of frames. Moreover, EraseLoRA remains robust for multiple object removal without additional optimization. baselines and benchmarks (Kuznetsova … view at source ↗
Figure 24
Figure 24. Figure 24: Additional qualitative comparison with dataset-driven and dataset-free methods [PITH_FULL_IMAGE:figures/full_fig_p033_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Additional qualitative comparison with dataset-driven and dataset-free methods [PITH_FULL_IMAGE:figures/full_fig_p034_25.png] view at source ↗

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Reference graph

Works this paper leans on

54 extracted references · 14 linked inside Pith

  1. [1]

    S. Bai, K. Chen, X. Liu, J. Wang, W. Ge, S. Song, K. Dang, P. Wang, S. Wang, J. Tang, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923, 2025

    Pith/arXiv arXiv 2025

  2. [2]

    Carion, L

    N. Carion, L. Gustafson, Y.-T. Hu, S. Debnath, R. Hu, D. Suris, C. Ryali, K. V. Alwala, H. Khedr, A. Huang, et al. Sam 3: Segment anything with concepts. arXiv preprint arXiv:2511.16719, 2025

    Pith/arXiv arXiv 2025

  3. [3]

    Caron, H

    M. Caron, H. Touvron, I. Misra, H. J \'e gou, J. Mairal, P. Bojanowski, and A. Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650--9660, 2021

    2021

  4. [4]

    Z. Chen, W. Wang, Z. Yang, Z. Yuan, H. Chen, and C. Shen. Freecompose: Generic zero-shot image composition with diffusion prior. In European Conference on Computer Vision, pages 70--87. Springer, 2024

    2024

  5. [5]

    Y. Ekin, A. B. Yildirim, E. E. C a g lar, A. Erdem, E. Erdem, and A. Dundar. Clipaway: Harmonizing focused embeddings for removing objects via diffusion models. Advances in Neural Information Processing Systems, 37: 0 17572--17601, 2024

    2024

  6. [6]

    Esser, S

    P. Esser, S. Kulal, A. Blattmann, R. Entezari, J. M \"u ller, H. Saini, Y. Levi, D. Lorenz, A. Sauer, F. Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In International Conference on Machine Learning, pages 12606--12633. PMLR, 2024

    2024

  7. [7]

    Fanelli, G

    N. Fanelli, G. Vessio, and G. Castellano. I dream my painting: Connecting mllms and diffusion models via prompt generation for text-guided multi-mask inpainting. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 6073--6082. IEEE, 2025

    2025

  8. [8]

    Goodfellow, J

    I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio. Generative adversarial networks. In International Conference on Computing Communication and Networking Technologies, 2021

    2021

  9. [9]

    J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. In Neural Information Processing Systems, 2020

    2020

  10. [10]

    E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1 0 (2): 0 3, 2022

    2022

  11. [11]

    Y. Jia, A. Cheng, Y. Yuan, C. Wang, J. Li, H. Jia, and S. Zhang. Designedit: Unify spatial-aware image editing via training-free inpainting with a multi-layered latent diffusion framework. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 3958--3966, 2025

    2025

  12. [12]

    Jiang, Z

    L. Jiang, Z. Wang, J. Bao, W. Zhou, D. Chen, L. Shi, D. Chen, and H. Li. Smarteraser: Remove anything from images using masked-region guidance. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 24452--24462, 2025

    2025

  13. [13]

    B. S. Kim, J. Kim, and J. C. Ye. Chain-of-zoom: Extreme super-resolution via scale autoregression and preference alignment. arXiv preprint arXiv:2505.18600, 2025 a

    Pith/arXiv arXiv 2025

  14. [14]

    C. Kim, H. Shin, E. Hong, H. Yoon, A. Arnab, P. H. Seo, S. Hong, and S. Kim. Seg4diff: Unveiling open-vocabulary segmentation in text-to-image diffusion transformers. arXiv preprint arXiv:2509.18096, 2025 b

    arXiv 2025

  15. [15]

    J. Kim, Z. Lee, D. Cho, S. Jo, Y. Jung, K. Kim, and E. Yang. Early timestep zero-shot candidate selection for instruction-guided image editing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18844--18854, 2025 c

    2025

  16. [16]

    Kuznetsova, H

    A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov, et al. The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale. International journal of computer vision, 128 0 (7): 0 1956--1981, 2020

    1956

  17. [17]

    B. F. Labs. Flux, 2023. URL https://github.com/black-forest-labs/flux

    2023

  18. [18]

    P. Li, Q. Nie, Y. Chen, X. Jiang, K. Wu, Y. Lin, Y. Liu, J. Peng, C. Wang, and F. Zheng. Tuning-free image customization with image and text guidance. In European Conference on Computer Vision, pages 233--250. Springer, 2024

    2024

  19. [19]

    W. Li, Z. Lin, K. Zhou, L. Qi, Y. Wang, and J. Jia. Mat: Mask-aware transformer for large hole image inpainting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10758--10768, 2022

    2022

  20. [20]

    H. Liu, C. Li, Q. Wu, and Y. J. Lee. Visual instruction tuning. Advances in neural information processing systems, 36: 0 34892--34916, 2023

    2023

  21. [21]

    S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, Q. Jiang, C. Li, J. Yang, H. Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. In European conference on computer vision, pages 38--55. Springer, 2024

    2024

  22. [22]

    Y. Liu, H. Zhou, B. Cui, W. Shang, and R. Lin. Erase diffusion: Empowering object removal through calibrating diffusion pathways. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 2418--2427, 2025

    2025

  23. [23]

    Manukyan, A

    H. Manukyan, A. Sargsyan, B. Atanyan, Z. Wang, S. Navasardyan, and H. Shi. Hd-painter: high-resolution and prompt-faithful text-guided image inpainting with diffusion models. In The Thirteenth International Conference on Learning Representations, 2023

    2023

  24. [24]

    Podell, Z

    D. Podell, Z. English, K. Lacey, A. Blattmann, T. Dockhorn, J. Müller, J. Penna, and R. Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023. URL https://arxiv.org/abs/2307.01952

    Pith/arXiv arXiv 2023

  25. [25]

    L. Qu, H. Li, W. Wang, X. Liu, J. Li, L. Nie, and T.-S. Chua. Silmm: Self-improving large multimodal models for compositional text-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 18497--18508, June 2025

    2025

  26. [26]

    N. Ravi, V. Gabeur, Y.-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. R \"a dle, C. Rolland, L. Gustafson, E. Mintun, J. Pan, K. V. Alwala, N. Carion, C.-Y. Wu, R. Girshick, P. Dollar, and C. Feichtenhofer. SAM 2: Segment anything in images and videos. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/fo...

    2025

  27. [27]

    T. Ren, S. Liu, A. Zeng, J. Lin, K. Li, H. Cao, J. Chen, X. Huang, Y. Chen, F. Yan, et al. Grounded sam: Assembling open-world models for diverse visual tasks. arXiv preprint arXiv:2401.14159, 2024

    Pith/arXiv arXiv 2024

  28. [28]

    Rombach, A

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684--10695, 2022

    2022

  29. [29]

    Sagong, Y.-J

    M.-C. Sagong, Y.-J. Yeo, S.-W. Jung, and S.-J. Ko. Rord: A real-world object removal dataset. In BMVC, page 542, 2022

    2022

  30. [30]

    Sargsyan, S

    A. Sargsyan, S. Navasardyan, X. Xu, and H. Shi. Mi-gan: A simple baseline for image inpainting on mobile devices. In IEEE International Conference on Computer Vision, 2023. doi:10.1109/ICCV51070.2023.00674

    arXiv 2023

  31. [31]

    Sim \'e oni, H

    O. Sim \'e oni, H. V. Vo, M. Seitzer, F. Baldassarre, M. Oquab, C. Jose, V. Khalidov, M. Szafraniec, S. Yi, M. Ramamonjisoa, et al. Dinov3. arXiv preprint arXiv:2508.10104, 2025

    Pith/arXiv arXiv 2025

  32. [32]

    Sun, X.-M

    W. Sun, X.-M. Dong, B. Cui, and J. Tang. Attentive eraser: Unleashing diffusion model’s object removal potential via self-attention redirection guidance. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 20734--20742, 2025

    2025

  33. [33]

    Suvorov, E

    R. Suvorov, E. Logacheva, A. Mashikhin, A. Remizova, A. Ashukha, A. Silvestrov, N. Kong, H. Goka, K. Park, and V. Lempitsky. Resolution-robust large mask inpainting with fourier convolutions. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 2149--2159, 2022

    2022

  34. [34]

    Q. Team. Qwen3-vl-235b-a22b-instruct: Multimodal vision-language model. https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct, 2025. Hugging Face model card, accessed October 2025

    2025

  35. [35]

    Tianyidan, R

    X. Tianyidan, R. Ma, Q. Wang, X. Ye, F. Liu, Y. Tai, Z. Zhang, L. Wang, and Z. Yi. Anywhere: A multi-agent framework for user-guided, reliable, and diverse foreground-conditioned image generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 7410--7418, 2025

    2025

  36. [36]

    A. Wang, L. Liu, H. Chen, Z. Lin, J. Han, and G. Ding. Yoloe: Real-time seeing anything. arXiv preprint arXiv:2503.07465, 2025

    arXiv 2025

  37. [37]

    D. Wang, E. Shelhamer, S. Liu, B. Olshausen, and T. Darrell. Tent: Fully test-time adaptation by entropy minimization. arXiv preprint arXiv:2006.10726, 2020

    Pith/arXiv arXiv 2006

  38. [38]

    Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13 0 (4): 0 600--612, 2004

    2004

  39. [39]

    Z. Wang, A. Li, Z. Li, and X. Liu. Genartist: Multimodal llm as an agent for unified image generation and editing. Advances in Neural Information Processing Systems, 37: 0 128374--128395, 2024

    2024

  40. [40]

    R. Wei, Z. Yin, S. Zhang, L. Zhou, X. Wang, C. Ban, T. Cao, H. Sun, Z. He, K. Liang, et al. Omnieraser: Remove objects and their effects in images with paired video-frame data. arXiv preprint arXiv:2501.07397, 2025

    Pith/arXiv arXiv 2025

  41. [41]

    T.-H. Wu, H. Lee, J. Ge, J. E. Gonzalez, T. Darrell, and D. M. Chan. Generate, but verify: Reducing hallucination in vision-language models with retrospective resampling. arXiv preprint arXiv:2504.13169, 2025

    arXiv 2025

  42. [42]

    S. Xie, Z. Zhang, Z. Lin, T. Hinz, and K. Zhang. Smartbrush: Text and shape guided object inpainting with diffusion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 22428--22437. Computer Vision Foundation / IEEE, 2022. URL https://arxiv.org/pdf/2212.05034

    Pith/arXiv arXiv 2022

  43. [43]

    B. Yang, S. Gu, B. Zhang, T. Zhang, X. Chen, X. Sun, D. Chen, and F. Wen. Paint by example: Exemplar-based image editing with diffusion models. In Computer Vision and Pattern Recognition, 2022. doi:10.1109/CVPR52729.2023.01763

    arXiv 2022

  44. [44]

    Y. Yu, Z. Zeng, H. Zheng, and J. Luo. Omnipaint: Mastering object-oriented editing via disentangled insertion-removal inpainting. arXiv preprint arXiv:2503.08677, 2025

    Pith/arXiv arXiv 2025

  45. [45]

    Zhang, L

    K. Zhang, L. Mo, W. Chen, H. Sun, and Y. Su. Magicbrush: A manually annotated dataset for instruction-guided image editing. Advances in Neural Information Processing Systems, 36: 0 31428--31449, 2023

    2023

  46. [46]

    Zhang, P

    R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586--595, 2018

    2018

  47. [47]

    L. Zhao, Y. Deng, W. Zhang, and Q. Gu. Mitigating object hallucination in large vision-language models via image-grounded guidance. arXiv preprint arXiv:2402.08680, 2024

    Pith/arXiv arXiv 2024

  48. [48]

    S. Zhao, J. Cui, Y. Sheng, Y. Dong, X. Liang, E. I. Chang, and Y. Xu. Large scale image completion via co-modulated generative adversarial networks. arXiv preprint arXiv:2103.10428, 2021

    Pith/arXiv arXiv 2021

  49. [49]

    J. Zhou, J. Li, Z. Xu, H. Li, Y. Cheng, F.-T. Hong, Q. Lin, Q. Lu, and X. Liang. Fireedit: Fine-grained instruction-based image editing via region-aware vision language model. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 13093--13103, 2025

    2025

  50. [50]

    J. Zhu, W. Wang, Z. Chen, Z. Liu, S. Ye, L. Gu, H. Tian, Y. Duan, W. Su, J. Shao, et al. Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479, 2025 a

    Pith/arXiv arXiv 2025

  51. [51]

    T. Zhu, S. Zhang, J. Shao, and Y. Tang. Kv-edit: Training-free image editing for precise background preservation. arXiv preprint arXiv:2502.17363, 2025 b

    Pith/arXiv arXiv 2025

  52. [52]

    Y. Zhu, Q. Zhang, Y. Wang, Y. Nie, and W.-S. Zheng. Entityerasure: Erasing entity cleanly via amodal entity segmentation and completion. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 28274--28283, 2025 c

    2025

  53. [53]

    Zhuang, Y

    J. Zhuang, Y. Zeng, W. Liu, C. Yuan, and K. Chen. A task is worth one word: Learning with task prompts for high-quality versatile image inpainting. In European Conference on Computer Vision, pages 195--211. Springer, 2024

    2024

  54. [54]

    Z. Zuo, L. Zhao, A. Li, Z. Wang, Z. Zhang, J. Chen, W. Xing, and D. Lu. Generative image inpainting with segmentation confusion adversarial training and contrastive learning. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 3888--3896, 2023

    2023