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arxiv: 2606.29267 · v1 · pith:ENTLOY6Knew · submitted 2026-06-28 · 💻 cs.CV

Enhancing Part-Level Point Grounding for Any Open-Source MLLMs

Jin-Cheng Jhang , Fu-En Wang , Xin Yang , Nan Qiao , Lu Xia , Min Sun , Cheng-Hao Kuo This is my paper

Pith reviewed 2026-06-30 08:03 UTC · model grok-4.3

classification 💻 cs.CV
keywords part-level groundingpoint groundingMLLMvisual groundingattention mechanismsfrozen modelQ-Synth Module
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The pith

A module adds accurate part-level point grounding to any frozen open-source MLLM by prompting its existing attention patterns.

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

The paper presents a plug-in approach that gives Multimodal Large Language Models the ability to output precise 2D points on object parts when given text queries. It does this by inserting a Q-Synth Module into intermediate layers to create text-conditioned queries that surface relevant attention maps, then feeding those maps to a small decoder that turns them into point heatmaps. All original model weights stay frozen, so the MLLM keeps its prior capabilities while gaining this new skill. A reader would care because part-level pointing supports tasks such as robotic grasping that need finer detail than whole-object boxes. The design works across different open-source MLLMs and improves accuracy on multiple grounding datasets.

Core claim

By synthesizing text-conditioned grounding-aware queries inside the intermediate layers of a frozen MLLM with the Q-Synth Module, target-relevant attention patterns are captured and then converted by a lightweight Attention-to-Point Decoder into point-centric heatmaps that deliver accurate part-level point predictions.

What carries the argument

The Q-Synth Module, which creates text-conditioned queries to elicit grounding-aware attention patterns from frozen intermediate layers, paired with the Attention-to-Point Decoder that refines those patterns into point heatmaps.

If this is right

  • Part-level grounding accuracy rises across tested datasets while the base MLLM stays unchanged.
  • The same module works with any open-source MLLM without retraining its parameters.
  • Point-based output becomes a direct alternative to box or mask grounding representations.
  • Pre-trained multimodal capabilities remain fully preserved after adding the grounding skill.

Where Pith is reading between the lines

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

  • The approach could be tested on tasks that need part-level interaction, such as tool-use or assembly instructions.
  • If attention patterns differ across model families, the decoder might need light per-model calibration while still freezing the backbone.
  • Success here suggests similar query-synthesis tricks could unlock other fine-grained visual outputs without full fine-tuning.

Load-bearing premise

The attention patterns already present in a frozen MLLM's intermediate layers contain enough target-relevant information, once prompted by the Q-Synth Module, to be turned into accurate part-level point predictions.

What would settle it

Running the method on several open-source MLLMs and part-level grounding datasets and finding no consistent accuracy gain, or finding that the extracted attention maps produce heatmaps no better than random point selection.

Figures

Figures reproduced from arXiv: 2606.29267 by Cheng-Hao Kuo, Fu-En Wang, Jin-Cheng Jhang, Lu Xia, Min Sun, Nan Qiao, Xin Yang.

Figure 1
Figure 1. Figure 1: Overview of the core idea. We aim to synthesize [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our framework. Instead of relying on the native text-to-image attention, the proposed Query Synthesis (Q-Synth) [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the Query Synthesis (Q-Synth) Module. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative results. We compare text pointing, attention pointing, and our proposed method across columns. Each row presents [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Architecture of the proposed Attention-to-Point (A2P) Decoder. The A2P Decoder fuses [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The proposed SDF mapping function f under different (τ, γ) settings. Here, x denotes the original scalar SDF values. In the left plot, γ controls the asymmetry of the mapped values inside versus outside the mask (i.e., for x < 0 vs. x > 0). In the right plot, varying τ adjusts the overall steepness of the penalty field. The intuition behind this design is that predictions outside the target region should i… view at source ↗
Figure 7
Figure 7. Figure 7: Examples of the original ground-truth masks and the [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Examples of filtered samples from the PACO [ [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: MLLM text-pointing prompts. The Long instruction used in the reasoning pointing task includes queries that require contextual understanding. For example: “If I want to pick up the knife, which part in the picture can be used?” 4 [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: More qualitative results. We compare text pointing, attention pointing, and our proposed method across columns. Each row [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
read the original abstract

Visual grounding aims to associate free-form textual queries with specific regions in an image. While recent Multimodal Large Language Models (MLLMs) have demonstrated promising capabilities in this domain, they primarily excel at object-level grounding and often struggle with part-level grounding-an essential requirement for fine-grained tasks such as robotic manipulation. In this work, we introduce a general approach that equips any open-source MLLMs with accurate 2D part-level point grounding, offering a more direct alternative to conventional grounding representations. Our method leverages the attention mechanisms inherently present in MLLMs. By synthesizing text-conditioned, grounding-aware queries within intermediate layers via the proposed Q-Synth Module, we capture target-relevant attention patterns and refine them with a lightweight Attention-to-Point Decoder, which converts these patterns into a point-centric heatmap for final prediction. Notably, all original MLLM parameters are frozen, ensuring full preservation of their pre-trained capabilities. Experiments show that our design consistently improves part-level grounding accuracy across datasets and can be seamlessly integrated into any open-source MLLMs.

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 / 0 minor

Summary. The paper claims to introduce a Q-Synth Module that synthesizes text-conditioned, grounding-aware queries in the intermediate layers of any open-source MLLM to capture target-relevant attention patterns, which are then refined by a lightweight Attention-to-Point Decoder into point-centric heatmaps for part-level point grounding. All original MLLM parameters remain frozen, and the authors assert that the design consistently improves part-level grounding accuracy across datasets while preserving pre-trained capabilities.

Significance. If the claimed improvements hold under rigorous evaluation, the approach would be significant as a parameter-efficient method for extending existing MLLMs to fine-grained part-level grounding tasks without retraining, which is relevant for applications such as robotic manipulation.

major comments (2)
  1. Abstract: the abstract asserts consistent accuracy gains but supplies no quantitative results, error bars, dataset details, or ablation studies; without these it is impossible to verify whether the reported improvements are robust or affected by post-hoc choices.
  2. Abstract: the central claim depends on the assumption that attention patterns already present in the intermediate layers of a completely frozen MLLM contain sufficient target-specific information for part-level point localization once the Q-Synth Module injects text-conditioned queries; this premise lacks any architectural guarantee and requires explicit validation that pre-trained heads encode part-level rather than only object-level cues.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful comments on our manuscript. Below we address each major comment point by point with honest responses and indicate planned revisions where appropriate.

read point-by-point responses

  1. Referee: [—] Abstract: the abstract asserts consistent accuracy gains but supplies no quantitative results, error bars, dataset details, or ablation studies; without these it is impossible to verify whether the reported improvements are robust or affected by post-hoc choices.

    Authors: We agree that the abstract is too concise and does not include the requested quantitative details. The full manuscript reports accuracy improvements with error bars, specific datasets, and ablation studies in the Experiments and Ablation sections. We will revise the abstract to incorporate key quantitative results (e.g., average accuracy gains and dataset names) so that the claims can be assessed directly from the abstract. revision: yes

  2. Referee: [—] Abstract: the central claim depends on the assumption that attention patterns already present in the intermediate layers of a completely frozen MLLM contain sufficient target-specific information for part-level point localization once the Q-Synth Module injects text-conditioned queries; this premise lacks any architectural guarantee and requires explicit validation that pre-trained heads encode part-level rather than only object-level cues.

    Authors: The Q-Synth Module is explicitly designed to synthesize text-conditioned queries that elicit part-relevant attention from the frozen intermediate layers. Our experiments provide supporting evidence through attention visualizations (showing part-level focus after query synthesis) and ablations that compare performance with and without the module, demonstrating that the pre-trained attention can be steered toward part-level cues. We will add an explicit validation subsection with additional attention-map analysis and object-vs-part comparisons in the revised manuscript. revision: partial

Circularity Check

0 steps flagged

No circularity: architectural addition validated by experiments

full rationale

The paper proposes an architectural method (Q-Synth Module + Attention-to-Point Decoder) that injects queries into frozen MLLM attention layers and decodes to point heatmaps. No equations, parameters, or derivations are presented that reduce the claimed accuracy gains to a fitted quantity defined by the evaluation data itself. No self-citation chains or uniqueness theorems are invoked as load-bearing premises. The central claim rests on experimental integration and measured improvements, which are independent of any definitional or fitting circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the domain assumption that transformer attention already encodes part-level information when suitably queried; no free parameters or invented entities are described in the abstract.

axioms (1)
  • domain assumption Attention mechanisms in MLLMs capture target-relevant patterns when text-conditioned queries are synthesized in intermediate layers.
    Invoked to justify the Q-Synth Module without additional training.

pith-pipeline@v0.9.1-grok · 5727 in / 1253 out tokens · 28598 ms · 2026-06-30T08:03:34.999330+00:00 · methodology

discussion (0)

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    × $!) Our Attention

    5, 6, 3 10 Enhancing Part-Level Point Grounding for Any Open-Source MLLMs Supplementary Material A. Overview This supplementary material provides additional details and results that complement the main manuscript. In Sec. B, we describe the architectural details of the proposed Attention- to-Point (A2P) Decoder. In Sec. C, we present plots and visualizati...