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arxiv: 2606.27372 · v1 · pith:4TLZJRLW · submitted 2026-06-25 · cs.CV

DnA: Denoising Attention for Visual Tasks

Ron Campos , Subhajit Maity , Xin Li , Srijan Das , Aritra Dutta This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-26 05:02 UTCgrok-4.3pith:4TLZJRLWrecord.jsonopen to challenge →

classification cs.CV
keywords denoising attentionmulti-head attentionvision transformersImageNet classificationvideo understandingsubspace separationprincipal anglessoftmax alternative
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The pith

DnA improves visual attention by projecting positive and negative query interactions into two distinct subspaces with larger principal angles.

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

The paper introduces Denoising Attention (DnA) to address noisy patterns produced by standard softmax in multi-head attention for visual models. A positive query locates features belonging to the correct class while a negative query flags closely related but irrelevant features; these are then mapped into separate subspaces chosen to maximize principal angles between them. The authors report that this separation yields an absolute 0.8 percent accuracy lift on ImageNet-1K with a ViT-B backbone, plus 1.8 percent on video transformer tasks and 0.5 percent on video LLMs. A sympathetic reader would care because attention noise is a recurring bottleneck in transformer-based vision systems, and the method keeps the same backbone while altering only the attention computation. Extensive ablations are presented to justify the two-subspace design and the denoising effect.

Core claim

DnA modifies multi-head attention so that positive queries identify correct-class image features and negative queries identify closely associated but irrelevant features; the resulting interactions are projected into two distinct subspaces engineered to have larger principal angles, which the authors state promotes subspace separation and improved discriminability. On a ViT-B backbone this produces an absolute gain of 0.8 percent on ImageNet-1K, 1.8 percent on video-understanding tasks with video transformers, and 0.5 percent on video LLMs.

What carries the argument

Denoising Attention (DnA) mechanism that projects positive and negative query interactions into two distinct subspaces with larger principal angles

If this is right

  • Attention maps exhibit reduced noise and higher feature discriminability across image and video inputs.
  • Classification accuracy rises on ImageNet-1K without changing model size or training regime.
  • Video transformer and video LLM benchmarks record consistent positive gains, with the largest lift on video tasks.
  • Empirical analyses confirm that the two-subspace construction and the denoising step are both necessary for the observed improvements.

Where Pith is reading between the lines

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

  • The same positive-negative query split could be inserted into attention layers of non-vision transformers to test whether subspace separation helps language or multimodal models.
  • If the principal-angle construction is the active ingredient, replacing it with other geometric separation objectives might produce comparable or larger gains on the same benchmarks.
  • The reported improvements might allow smaller backbones to reach accuracy levels previously requiring larger models, which would be testable by scaling down ViT size while keeping DnA.
  • The approach might interact with existing regularization techniques such as attention dropout, an interaction the paper does not explore.

Load-bearing premise

That mapping the interactions into subspaces with larger principal angles will produce better separation and therefore higher discriminability.

What would settle it

Running the same ViT-B experiments on ImageNet-1K and finding that accuracy does not rise by the reported 0.8 percent or that measured principal angles show no correlation with the accuracy difference.

Figures

Figures reproduced from arXiv: 2606.27372 by Aritra Dutta, Ron Campos, Srijan Das, Subhajit Maity, Xin Li.

Figure 1
Figure 1. Figure 1: The original image from ImageNet-1K [18] shows the primary object, breast￾plate, along with secondary adversarial objects, person, helmet, etc. The traditional attention [20, 73] and differential attention [80] project the interactions onto a single V subspace, resulting in misallocation of attention to secondary objects. Our proposed DnA explicitly models positive and negative interactions using two sets … view at source ↗
Figure 2
Figure 2. Figure 2: Original images (center) from ImageNet-1K, their attention activation maps for traditional softmax only showing positive interactions (left), and our denoising attention, DnA (A Q±V ± h ) (right) showing both positive and negative interactions. (a) The softmax attention is dispersed and does not focus on the concerned object (hare), while our DnA focuses on the concerned object. (b) The softmax attention i… view at source ↗
Figure 3
Figure 3. Figure 3: A schematic comparison between traditional attention and the proposed de￾noising attention. (i) The multi-head attention uses a softmax activation on the rows of the scaled dot-product QhK⊤ √ h d and combines it with Vh. (ii) The proposed denoising attention A Q±V ± h , uses two sets of values, V + h and V − h , and two sets of queries Q + h and Q − h . We use softmax and softmin two scaled dot-products, Q… view at source ↗
Figure 4
Figure 4. Figure 4: (a) Top: Attention visualization for ViT-B [20] with softmax, differential at￾tention [80], and our DnA. The objects are dumbbells and a volleyball. (b) Bottom: Example of DnA outperforming baseline methods on egocentric video QA. Four frames are uniformly sampled; the object of interest is highlighted by a cyan box. The correct answer is in green, the incorrect in red. We initialize the positive projectio… view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of intruder dimension counts between DnA and differential atten￾tion ϵ = cos π 3 , k=10 [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Average pairwise cosine distances under varying noise levels; see noise variance (σ) and PSNR in titles. Solid lines represent models evaluated under clean data (DnA and softmax), while dashed lines reflect artifacts of noisy data (DnAσ and softmaxσ ) for different noise, σ. DnAσ exhibits smaller accuracy drops than softmaxσ across all noise levels, indicating more diverse feature representations and robus… view at source ↗
Figure 8
Figure 8. Figure 8: Training loss, validation loss, and validation accuracy of ViT-B using [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Visualization of the inference results of ViT-Det on a ViT-B backbone (a) using DnA (top row) and (b) using softmax attention with the corresponding ground truths (bottom row); zoom in for a better view. For each sample, the left image is the prediction and the right image is the ground truth. C.4 Qualitative Analysis on Detection & Segmentation Figure 9a displays the qualitative performance of using DnA i… view at source ↗
Figure 10
Figure 10. Figure 10: Statistics of αh across different denoising designs. Each row represents the mean, maximum, minimum, and standard deviation of the learnable parameter, αh, for each layer of the network. actions. (e) Finally, the proposed DnA, A Q±V ± h , outperforms all baselines as it can model token interactions better with separate value subspaces for positive and negative interactions. (B) Is the performance gain of … view at source ↗
Figure 11
Figure 11. Figure 11: Mean head redundancy measured by pairwise cosine distance between atten￾tion weights across heads. Higher distance indicates lower redundancy. line. This suggests that different heads in the negative branch learn to identify distinct noise patterns. After layer 8, the negative branch becomes more redun￾dant than the baseline, indicating that once noise suppression is complete, the heads become more simila… view at source ↗
Figure 12
Figure 12. Figure 12: Attention visualization for ViT-B [20] using softmax attention, differential attention [80], and our DnA. From top to bottom, the objects are: cassette, chain link, cocktail shaker, Egyptian cat, swimming cap, and ladybug [PITH_FULL_IMAGE:figures/full_fig_p035_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Examples of DnA outperforming baseline methods on egocentric video QA. For each example, frames are uniformly sampled, with the object of interest in a cyan box. The correct answer is in green, the incorrect in red. observe a larger range between the values of αh, and a mean that exhibits less linear progression. Lastly, for Aˆ± h , we see that for the first 8 layers, the minimum αh actually takes on a ne… view at source ↗
Figure 14
Figure 14. Figure 14: Layer-wise entropy of attention weights for ViT-B using softmax attention (QK⊤) and both DnA attention branches (Q +K⊤ and Q −K⊤) on the ImageNet-1K validation set. Solid lines show mean entropy across all attention heads. Dashed lines represent the entropy of the Top-k% most attended tokens. coder layers. Attention entropy, measured as the Shannon entropy, H (see Theo￾rem 1), of the attention weights, qu… view at source ↗
read the original abstract

The softmax activation in multihead attention (MHA) is the de facto standard for attention-based models in visual perception tasks. However, standard softmax can produce noisy attention patterns that dilute relevant features and degrade its performance. In this paper, we propose Denoising Attention or DnA, in which, first, a positive query identifies which image features belong to the correct class, and a negative query identifies closely associated but irrelevant image features. DnA then projects these interactions into two distinct subspaces with larger principal angles, promoting subspace separation and improved discriminability. Using a ViT-B backbone, our proposed DnA achieves an absolute gain of 0.8% on ImageNet-1K compared to the baseline. We further show improvements across multiple visual understanding tasks, including video understanding with video transformers (1.8%) and video LLMs (0.5%). Our extensive empirical analyses justify the design choices involving two interacting subspaces and the denoising effect of DnA.

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

3 major / 0 minor

Summary. The paper proposes Denoising Attention (DnA) to address noisy attention patterns from standard softmax in multi-head attention for visual tasks. It introduces positive and negative queries to identify relevant and irrelevant image features, then projects their interactions into two distinct subspaces chosen for larger principal angles to promote separation and discriminability. Empirical claims include an absolute 0.8% gain on ImageNet-1K using a ViT-B backbone, 1.8% improvement on video understanding with video transformers, and 0.5% on video LLMs, supported by extensive empirical analyses justifying the two-subspace design.

Significance. If the reported accuracy gains prove reproducible and the subspace-separation mechanism is shown to be causal rather than incidental, DnA could represent a lightweight modification to attention that improves discriminability across image and video tasks. The absence of any derivation, principal-angle measurements, or controlled ablations in the provided description limits the assessed significance; no machine-checked proofs, open code, or parameter-free derivations are mentioned.

major comments (3)
  1. [Abstract] Abstract: The central claim attributes performance gains to projection into subspaces with larger principal angles promoting separation and discriminability, yet supplies no derivation, bound, measurement of principal angles versus standard MHA, or controlled experiment isolating this mechanism from ancillary changes (extra queries, altered computation). This assumption is load-bearing for explaining the 0.8% ImageNet-1K gain.
  2. [Abstract] Abstract: No ablation of the two-subspace design, error bars, or training protocol is described, preventing verification that the reported gains (0.8% ImageNet-1K, 1.8% video transformers, 0.5% video LLMs) arise from the proposed denoising effect rather than capacity or implementation differences.
  3. [Abstract] Abstract: The construction and training of the positive query (for correct-class features) and negative query (for irrelevant but associated features) are not specified, leaving the method's implementation details and reproducibility unclear despite being foundational to the subspace projection step.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. Below we respond point-by-point to the major comments, clarifying details present in the full paper and indicating revisions we will make to improve clarity and reproducibility.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim attributes performance gains to projection into subspaces with larger principal angles promoting separation and discriminability, yet supplies no derivation, bound, measurement of principal angles versus standard MHA, or controlled experiment isolating this mechanism from ancillary changes (extra queries, altered computation). This assumption is load-bearing for explaining the 0.8% ImageNet-1K gain.

    Authors: The full manuscript motivates the subspace projection empirically through analyses of attention patterns and discriminability, rather than a theoretical derivation or bound. We agree that direct measurements of principal angles and a controlled ablation isolating the projection step would strengthen the causal link to the observed gains. We will add these measurements (comparing DnA subspaces to standard MHA) and the requested ablation in the revised manuscript. revision: yes

  2. Referee: [Abstract] Abstract: No ablation of the two-subspace design, error bars, or training protocol is described, preventing verification that the reported gains (0.8% ImageNet-1K, 1.8% video transformers, 0.5% video LLMs) arise from the proposed denoising effect rather than capacity or implementation differences.

    Authors: The manuscript contains extensive empirical analyses and ablations on the two-subspace design and denoising effect. However, we acknowledge that error bars and explicit training protocol details are not sufficiently highlighted. We will include error bars on all reported results and expand the training protocol description in the revision to facilitate verification. revision: yes

  3. Referee: [Abstract] Abstract: The construction and training of the positive query (for correct-class features) and negative query (for irrelevant but associated features) are not specified, leaving the method's implementation details and reproducibility unclear despite being foundational to the subspace projection step.

    Authors: The method section details the construction of the positive and negative queries, including their initialization, training objectives to identify correct-class versus associated irrelevant features, and integration with the subspace projection. To address the concern about the abstract, we will add a concise description of query construction in the abstract and introduction of the revised version. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical gains reported independently of asserted mechanism

full rationale

The paper defines DnA as a projection step into subspaces with larger principal angles and states that this promotes separation and discriminability, then reports measured accuracy improvements on ImageNet-1K and other tasks. No equations, fitted parameters, or self-citations are shown that reduce the reported gains to the assumption by construction. The gains are presented as external empirical outcomes rather than derived quantities, satisfying the default expectation of non-circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities can be extracted or audited.

pith-pipeline@v0.9.1-grok · 5702 in / 1071 out tokens · 26705 ms · 2026-06-26T05:02:21.061126+00:00 · methodology

discussion (0)

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