arxiv: 2605.13178 · v1 · submitted 2026-05-13 · 💻 cs.CV · cs.AI

Recognition: no theorem link

CLIP Tricks You: Training-free Token Pruning for Efficient Pixel Grounding in Large VIsion-Language Models

Sangin Lee , Yukyung Choi

Authors on Pith no claims yet

Pith reviewed 2026-05-14 20:34 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords token pruningpixel groundingvision-language modelsCLIP similaritytraining-freevisual tokensefficient inferencereferent regions

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The pith

Reversing CLIP visual-text similarity retains the tokens needed for accurate pixel grounding without training.

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

Large vision-language models carry many visual tokens that create heavy computation costs during pixel grounding, a task where token value depends strongly on the input text. CLIP analysis shows that tokens inside the text-referred regions usually score lower in similarity to the text embedding than other tokens. LiteLVLM reverses that similarity ranking to keep the referent tokens, then restores a few context tokens so the model can separate foreground from background. The result is a training-free method that exceeds prior pruning techniques by more than five percent across token budgets and delivers 90 percent of full-model performance at 22 percent faster speed and 2.3 times lower memory use.

Core claim

By reversing the similarity scores between visual tokens and text embeddings from CLIP, LiteLVLM identifies and preserves the visual tokens that cover the referent regions mentioned in the input text. It then recovers additional context tokens to distinguish foreground from background, allowing efficient inference in large vision-language models for pixel grounding tasks without any training or fine-tuning.

What carries the argument

Reversed CLIP visual-to-text similarity ranking, which prioritizes low-similarity tokens in referent areas for retention during pruning.

Load-bearing premise

The assumption that referent-region visual tokens consistently show low similarity to text in CLIP and that reversing this ranking will reliably select the necessary tokens in the target large vision-language models.

What would settle it

A pixel-grounding benchmark run where the reversed-similarity pruning produces lower accuracy than random token selection or the full unpruned model at the same token count.

Figures

Figures reproduced from arXiv: 2605.13178 by Sangin Lee, Yukyung Choi.

**Figure 1.** Figure 1: Comparison of different token pruning methods. Top: Colored patches indicate the retained visual tokens for each referring expression. Our LiteLVLM effectively preserves the tokens corresponding to the referent. Bottom: LiteLVLM achieves the best performance across all referring expression segmentation benchmarks, retaining around 90% performance with 66.7% token pruning. LiteLVLM also improves efficiency… view at source ↗

**Figure 2.** Figure 2: Analysis of visual-text similarity. (a) Attention correlation between [CLS] and [EOS]. Visual tokens show a clear positive attention correlation. (b) [REF]-[CLS] similarity rank distribution. (c) [REF]-[EOS] similarity rank distribution. [REF] tokens show even lower similarity to the [EOS] token than to the [CLS] token. attention-weighted sum of visual tokens: E I i = X M j=0 α0,jVj , α0,j = softmax Qv⊤K… view at source ↗

**Figure 3.** Figure 3: Analysis of text attention sink. (a) Average attention scores from the [EOS] to each text token. (b) Layer-wise selfattention heatmap from the [EOS] token to each word, where brighter colors indicate higher attention scores. a result, [REF] tokens receive weaker gradient signals from them, leading to lower similarity with global representations while preserving more localized, object-specific details. 3.3… view at source ↗

**Figure 4.** Figure 4: Overview of LiteLVLM. Given an image I and N texts {Ti} N i=1, we extract visual tokens Zv and [EOS] text token Z[EOS] from their respective encoders. We retain visual tokens with low similarity to [EOS] embedding E T i , then recover contextually informative tokens using [CLS] token (Z[CLS]) attention. The selected tokens are fed into the LLM and pixel decoder for segmentation mask generation. the last-la… view at source ↗

**Figure 5.** Figure 5: Visualization of LiteLVLM for different referring expression. Similarity-aware and context-aware tokens are highlighted in red and green boxes, respectively. From left to right, the number of retained tokens is progressively increased (64, 128, and 192 tokens). visual-text similarity visual tokens with recovered contextually informative ones, our LiteLVLM (model #4) yields a 2.7% gain in performance (Avg.… view at source ↗

**Figure 6.** Figure 6: Deeper analysis of CLIP-family variant encoders. (a) MetaCLIP [REF]-[EOS] similarity rank distribution. (b) SigLIP average attention scores from the [EOS] to each text token. (c) SigLIP [REF]-[EOS] similarity rank distribution. all evaluations under the settings reported in the original LLaVA paper [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

**Figure 7.** Figure 7: Visualization of CLIP Visual-Text Similarity Reversal. The top row shows RefCOCO+ results, while the bottom row presents RefCOCOg benchmark results, with green- and red-outlined tokens indicating the highest and lowest visual–text similarity, respectively. F.2. More Visualization Examples [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗

**Figure 8.** Figure 8: More qualitative visualizations of LiteLVLM on referring video object segmentation. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗

read the original abstract

In large vision-language models, visual tokens typically constitute the majority of input tokens, leading to substantial computational overhead. To address this, recent studies have explored pruning redundant or less informative visual tokens for image understanding tasks. However, these methods struggle with pixel grounding tasks, where token importance is highly contingent on the input text. Through an in-depth analysis of CLIP, we observe that visual tokens located within referent regions often exhibit low similarity to the textual representation. Motivated by this insight, we introduce LiteLVLM, a training-free, text-guided token pruning strategy for efficient pixel grounding inference. By reversing the ranking of CLIP's visual-text similarity, LiteLVLM effectively retains visual tokens covering the referent regions, while recovering context tokens to enable clear foreground-background separation. Extensive experiments demonstrate that LiteLVLM significantly outperforms existing methods by over 5% across diverse token budgets. Without any training or fine-tuning, LiteLVLM maintains 90\% of the original performance with a 22% speedup and a 2.3x memory reduction. Our code is available at https://github.com/sejong-rcv/LiteLVLM.

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

Reversing CLIP similarity to keep referent tokens works as a training-free prune for grounding and gives real speed and memory wins, but the link from early encoder scores to later LVLM importance still needs direct checks.

read the letter

The core move is simple: CLIP tends to give low similarity to the visual tokens that actually cover the text referent, so they keep the bottom-ranked ones instead of the top ones and add back some context tokens for separation. That reversal is the part that feels new compared with the usual high-similarity or attention pruning papers. They report it beats prior methods by more than 5% across token budgets, keeps 90% of full performance, runs 22% faster, and cuts memory by 2.3x, all without training. The code release helps, and the foreground-background recovery step looks like a useful practical detail for text-contingent tasks.

Referee Report

1 major / 2 minor

Summary. The manuscript introduces LiteLVLM, a training-free, text-guided token pruning strategy for efficient pixel grounding in large vision-language models. It is motivated by an empirical observation in CLIP that visual tokens within referent regions exhibit low cosine similarity to the text embedding; the method therefore reverses the similarity ranking to retain those tokens while recovering context tokens to support foreground-background separation. Experiments claim that LiteLVLM outperforms prior pruning methods by more than 5% across token budgets, retains 90% of full-model performance, and delivers a 22% speedup with 2.3x memory reduction, all without training or fine-tuning. Code is released at the cited GitHub repository.

Significance. If the empirical results hold under rigorous verification, the work supplies a simple, parameter-free inference-time technique that materially reduces the token overhead of LVLMs on grounding tasks. The explicit release of code is a clear strength that enables direct reproduction and extension. The approach targets a practically relevant bottleneck in scaling vision-language models.

major comments (1)

[§3] §3 (Method): The central claim that reversing CLIP visual-text similarity reliably retains tokens needed for downstream LVLM pixel grounding rests on an untested generalization. No correlation analysis, ablation, or comparison is presented between the reversed CLIP ranks and any model-internal importance signal (cross-attention weights, gradients, or output sensitivity) measured on the actual grounding task after the visual tokens have passed through the LLM layers.

minor comments (2)

[Abstract and §4] Abstract and §4 (Experiments): Exact baseline implementations, dataset splits, number of runs, statistical significance tests, and error bars are not reported, which prevents full verification of the stated >5% gains and 90% retention figures.
[§3.1] §3.1: The token-recovery step for context tokens is described only in prose; an explicit equation or short algorithm box would clarify the precise selection rule and its interaction with the reversed similarity ranking.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comment on the method section raises a valid point about strengthening the link between our CLIP observation and the full LVLM pipeline. We address this below and will incorporate revisions to provide additional analysis.

read point-by-point responses

Referee: [§3] §3 (Method): The central claim that reversing CLIP visual-text similarity reliably retains tokens needed for downstream LVLM pixel grounding rests on an untested generalization. No correlation analysis, ablation, or comparison is presented between the reversed CLIP ranks and any model-internal importance signal (cross-attention weights, gradients, or output sensitivity) measured on the actual grounding task after the visual tokens have passed through the LLM layers.

Authors: We appreciate this observation. The current manuscript motivates LiteLVLM from the CLIP-level analysis (low visual-text similarity for referent tokens) and demonstrates its effectiveness via direct empirical results on multiple pixel-grounding benchmarks, where it outperforms prior pruning methods by >5% while retaining 90% of full-model performance. However, we agree that an explicit correlation study linking the reversed CLIP ranks to downstream LLM-internal signals (e.g., cross-attention weights or output sensitivity after the LLM layers) is absent. In the revised version we will add a dedicated paragraph and figure in §3 that computes Pearson correlation between our pruning scores and the average cross-attention weights extracted from the LVLM on the same grounding examples. We will also include a small ablation that replaces our CLIP-based scores with LLM attention-based scores and reports the resulting grounding accuracy, thereby quantifying how well the CLIP reversal approximates the model-internal importance signal. revision: partial

Circularity Check

0 steps flagged

No significant circularity; method follows directly from independent CLIP observation

full rationale

The paper's core derivation begins with an empirical analysis of CLIP visual-text similarities on referent regions, then applies a simple reversal of those existing cosine scores to select tokens for the downstream LVLM. No parameters are fitted to grounding-task outputs, no equations reduce to prior fitted quantities by construction, and no load-bearing premise rests on self-citation. The observation is treated as an external input rather than derived from the target model or task results, satisfying the criteria for a self-contained, non-circular chain.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of the CLIP low-similarity observation for referent tokens and its transfer to VLLM grounding inference. No free parameters are fitted and no new entities are postulated.

axioms (1)

domain assumption Visual tokens located within referent regions exhibit low similarity to the textual representation in CLIP.
This is the core motivating observation from the in-depth CLIP analysis that justifies reversing the similarity ranking.

pith-pipeline@v0.9.0 · 5504 in / 1226 out tokens · 37226 ms · 2026-05-14T20:34:10.754156+00:00 · methodology

discussion (0)

Reference graph

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Dataset We conduct experiments with LiteLVLM on 6 widely used benchmarks, including 3 referring expression segmentation datasets and3referring video object segmentation datasets

11 CLIP Tricks You: Training-free Token Pruning for Efficient Pixel Grounding in Large Vision-Language Models Appendix A. Dataset We conduct experiments with LiteLVLM on 6 widely used benchmarks, including 3 referring expression segmentation datasets and3referring video object segmentation datasets. Each dataset is described in detail below. A.1. Referrin...

2024
[11]

left/right

images and segmentation masks. RefCOCO(Kazemzadeh et al., 2014). RefCOCO is designed for the referring expression comprehension task and is used to evaluate pixel grounding performance. It provides multiple natural language expressions for each target object, together with bounding box and segmentation mask annotations. The dataset is split into validatio...

2014
[12]

As shown in Table 7, LiteLVLM maintains its performance with only a 0.2% drop while pruning 65.9% of the total visual tokens (192 tokens)

to ground instances specified via referring expressions across video frames. As shown in Table 7, LiteLVLM maintains its performance with only a 0.2% drop while pruning 65.9% of the total visual tokens (192 tokens). Even when pruning 85.9% of the visual tokens (81 tokens), LiteLVLM still preserves 99.0% of the original performance. Moreover, LiteLVLM cons...

2017
[13]

model and compare it with previous state-of-the-art methods: ToMe (Bolya et al., 2023), FastV (Chen et al., 2024a), SparseVLM (Zhang et al., 2025b), LLaV A- PruMerge+ (Shang et al., 2025), VisionZip (Yang et al., 2025), and VisPruner (Zhang et al., 2025a). For a fair comparison, 13 CLIP Tricks You: Training-free Token Pruning for Efficient Pixel Grounding...

2023
[14]

as the vision encoder. D.1. Generalization Beyond LLaV A Since LiteLVLM is primarily studied on LLaV A-1.5, we additionally test our method on UniPixel (Liu et al., 2026), which is built upon Qwen2.5-VL. Concretely, Qwen2.5-VL employs a redesigned Vision Transformer (ViT) as its vision encoder to support native input resolutions, while adopting Qwen2.5 LL...

2026
[15]

Here, we employ LiteLVLM‡, an enhanced version that primarily uses context-aware tokens

following VisionZip (Yang et al., 2025). Here, we employ LiteLVLM‡, an enhanced version that primarily uses context-aware tokens. When retaining 192 and 32 visual tokens, LiteLVLM alone yields only a marginal memory reduction (within 1%, ∼118-120 MB). However, quantizing LiteLVLM further reduces memory usage substantially—by about 40% with 8-bit quantizat...

2025