Title: DIFFSSR: Stereo Image Super-resolution Using Differential Transformer
Venue: NeurIPS 2025
Accepted: yes
Keywords: Stereo Image Super-resolution

Differential Transformer
Abstract: In the field of computer vision, the task of stereo image super-resolution (StereoSR) has garnered significant attention due to its potential applications in augmented reality, virtual reality, and autonomous driving. Traditional Transformer-based models, while powerful, often suffer from attention noise, leading to suboptimal reconstruction issues in super-resolved images. This paper introduces DIFFSSR, a novel neural network architecture designed to address these challenges. We introduce the Diff Cross Attention Block (DCAB) and the Sliding Stereo Cross-Attention Module (SSCAM) to enhance feature integration and mitigate the impact of attention noise. The DCAB differentiates between relevant and irrelevant context, amplifying attention to important features and canceling out noise. The SSCAM, with its sliding window mechanism and disparity-based attention, adapts to local variations in stereo images, preserving details, and addressing the performance degradation due to misalignment of horizontal epipolar lines in stereo images. Extensive experiments on benchmark datasets demonstrate that DIFFSSR outperforms state-of-the-art methods, including NAFSSR and SwinFIRSSR, in terms of both quantitative metrics and visual quality.


Peer reviews:
comments: SUMMARY:
The paper proposes a transformer-based algorithm for stereo super-resolution tasks. The algorithm redesigns the Differential transformer block to accommodate it in the vision task, and designs a cross-attention module with a sliding window that is more robust to inaccuracies in stereo rectification. The method achieves SOTA scores on benchmark datasets and demonstrates promising visual results.

STRENGTHS AND WEAKNESSES:
Strengths: 
* The method achieves SOTA scores on benchmark datasets and demonstrates promising visual results.
* The idea of using unfolding op has a simple implementation but sounds effective against small misalignments.

Weaknesses:
* One of the motivations and contributions is to deal with attention noise. Yet, the impact of such noise in the vision or stereo SR task is not sufficiently analyzed in the main body. A.0.2 shows a sparser attention map, but how it connects to more relevant pixels is in question. 
* Unfolding op cannot handle large parallax that falls outside of the unfolding size. Large parallax is not uncommon in the scenes with rich and distinct depths, e.g. autonomous driving.
* The numerical improvements are mostly less than 1% compared to SwinFIRSSR, which is considered marginal.

QUESTIONS:
Add analysis/visuals to better justify how the diff transformer helps narrow down the focus to relevant pixels.

The Quantitative Evaluations section is quite verbose. There is no need to repeat the data in the table. Keep it concise and save some space for supportive visuals.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors develop a stereo image SR network on top of differetial Transformer. As the attention noise issue in Transformers is well acknowledged and results in inferior performance, this paper leverages differential Transformer to alleviate this issue for higher accuracy. However, the authors have observed that directly applying differential Transformer to stereo image SR produces performance degradation. To remedy this, Diff Cross Attention Block and Sliding Stereo Cross-Attention Module are developed to enhance feature integration. Extensive experiments are conducted on benchmark datasets, and the results have demonstrated the superiority of the proposed method over previous approaches. In addition, an ablation study is also conducted to validate the effectiveness of the proposed network designs.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The motivation is clear, and the proposed method is well presented.\n\n- State-of-the-art performance is achieved by the proposed method.\n\n\nWeaknesses:\n- The major concern is that the technical contribution may seem to be limited at first glance. The major network designs (i.e., DCAB and SSCAM) are built upon differential Transformer, and the authors have modified the original architectures to fit the stereo SR task. Besides, extensive experiments are conducted to demonstrate the effectiveness of these modifications. Overall, I think the observation that directly applying Diff produces inferior performance is valuable, and the experiments are solid.\n\n- In addition to the comparison in Fig. 5, it is recommended to include the original differential attention map to further highlight the gains introduced by the proposed modifications.\n\n- As generalization in real-world scenarios is a critical factor for image SR methods, it is recommended to evaluate the proposed method on real-world low-resolution stereo images and compare its results with previous SOTA methods.\n\n- In Fig. 1, it is not very clear to me why DIFF also suffers a performance drop on KITTI and Middlebury. It seems the vertical misalignment only exists in the Flickr1024 dataset.\n\nQUESTIONS:\nPlease see weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes DIFFSSR, a stereo image super-resolution framework that adapts the Differential Transformer to visual tasks. To address attention noise and stereo misalignment, the authors introduce two modules: the Diff Cross Attention Block (DCAB) and the Sliding Stereo Cross-Attention Module (SSCAM). Extensive experiments on standard benchmarks demonstrate that DIFFSSR outperforms prior methods such as SwinFIRSSR and NAFSSR in both accuracy and parameter efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper is well organized and clearly written, making it easy to understand the motivation, proposed method, and the role of each component.\n\n2. This paper presents the first attempt to adapt the Differential Transformer to visual tasks and proposes two modules, DCAB and SSCAM, which effectively reduce attention noise and address stereo misalignment.\n\n3. The method achieves competitive performance on multiple standard benchmarks, with comprehensive comparisons against SOTA baselines, showing consistent improvements in both accuracy and efficiency.\n\n\nWeaknesses:\n\n1. Although the paper introduces two new modules, their design is relatively incremental. Both DCAB and SSCAM are based on known concepts such as attention refinement and sliding window mechanisms, and may not be considered highly novel from an architectural perspective.\n\n2. While the paper provides high-level motivation for DCAB and SSCAM, it lacks deeper analysis or justification of why these specific designs are effective in addressing attention noise and stereo misalignment. The theoretical or intuitive reasoning behind the effectiveness of the modules could be elaborated further.\n\n3. There are several minor errors and unclear points in the paper. For example, the red, blue, and green boxes in Figure 3(b) are not explicitly explained. Equations (13) and (14) use incorrect input notations, and some subscripts or superscripts are inconsistent with those shown in Figure 3(b). Additionally, the meanings of the superscripts L and R in Section 3.1 should be clearly defined at the beginning of the section.\n\nQUESTIONS:\n1. Since the method is built on the Differential Transformer, the paper would benefit from a brief preliminaries section summarizing its core ideas and formulations. This would help readers better understand the motivation behind the proposed architectural modifications.\n2. The paper should clearly state that the effectiveness of the Differential Transformer comes from constructing opposing attention heads and using their difference to highlight relevant features while suppressing background noise. This design motivation is important for understanding how it addresses attention noise. Additionally, Section 3.3 should better explain why SSCAM is effective for handling stereo misalignment.\n3. Section 4.3.1 discusses the impact of window sizes 1 to 3, but Table 2 does not include results for sizes 1 and 2. These should be reported for completeness. In addition, Table 4 should include a comparison between window-based multi-head differential attention and standard multi-head differential attention, as this is one of the three core modifications.\n\nIf the authors can adequately address the above questions and resolve the concerns outlined in the weaknesses section, I would be willing to reconsider my overall assessment.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: SUMMARY:
This paper proposes DIFFSSR, which transfers the Differential Transformer to stereo image super-resolution. It pairs a Diff Cross Attention Block that keeps useful detail and reduces noise with a Sliding Stereo Cross-Attention Module that scans left and right views to fix misalignment. DIFFSSR beats SwinFIRSSR and NAFSSR in both scores and visual quality.

STRENGTHS AND WEAKNESSES:
Strengths:
1.  Visual comparisons demonstrate superior texture preservation and reduced over-smoothing relative to the baselines.

Weaknesses:
1. Limited novelty: DCAB and SSCAM integrate established concepts such as Differential Transformer, windowed self-attention and cross-view attention, without new improvements for stereo tasks.
2. Heavy computation with incomplete efficiency analysis: Even with a small window, SSCAM incurs about 40 % more FLOPs than the variant without the window, which can hinder real-time deployment. Besides, this paper does not report FLOPs for competing methods.
3. Incremental experimental gains: Compared to competing methods with comparable parameter counts, DIFFSSR yields < 0.1 dB improvement on most datasets, indicating minimal practical advantage.
4. Excessive mathematical notation: The manuscript contains 25 equations, many of which are not essential to the core derivation and could be removed.

QUESTIONS:
Please refer to the weaknesses.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-04