Xu, Mengting, Gu, Shi, Lin, Peng, Ma, De, Tang, Huajin, Zheng, Qian, Pan, Gang
Spiking Neural Networks (SNNs) have attracted increasing attention for their biologically inspired temporal dynamics. As their applications expand, understanding their robustness has become an important research focus. However, little is known about how the intrinsic temporal properties of SNNs affect robustness. In this work, we revisit SNN robustness from an information-theoretic perspective and reveal the pivotal role of temporal dynamics. We establish a theoretical link between robustness error and the mutual information (MI) between inputs and latent representations along the temporal dimension, grounded in the information bottleneck principle. Through an analysis of spike-based information transmission, we show that temporal dynamics inherently compress MI, thereby tightening the robustness error bound. Building on this insight, we propose a Temporal Mutual Information (TMI) regularizer that explicitly exploits temporal characteristics to enhance robustness. Extensive experiments on CIFAR-10, CIFAR-100, DVS-CIFAR10, Tiny-ImageNet, and ImageNet demonstrate that our method consistently improves SNN robustness across various architectures and attack settings.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Perception under low illumination remains a major challenge for computer vision systems, as RGB sensors often fail to capture sufficient structural and color information in extremely dark environments. Event cameras, with their high dynamic range and temporal resolution, provide complementary cues that are well suited for such conditions. In this work, we present eRetinexGS, a novel framework that jointly leverages event streams and low-light frames through 3D Gaussian Splatting for scene-level enhancement and reconstruction. Unlike previous approaches that operate on individual frames, eRetinexGS enforces geometric and photometric consistency across multiple views, bridging the gap between degraded images and noisy event signals. By introducing an event-assisted Retinex decomposition and a reflectance-illumination representation within the 3DGS pipeline, our method reconstructs normal-light radiance fields with fine-grained details and accurate color. Extensive experiments on both synthetic and real datasets demonstrate that eRetinexGS achieves state-of-the-art performance in low-light scene enhancement while maintaining real-time rendering capability.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
The task of visual grounding (i.e., VG) aims to locate or segment objects in images based on referring expressions. Existing research on VG primarily focuses on large objects. However, these images often contain objects at various scales. Although large objects are usually the visual focus, small objects sometimes carry crucial information. To bridge the gap, we propose a novel benchmark for small object visual grounding, i.e., SoVG. Specifically, we introduce an automatic pipeline using MLLMs to build a benchmark dataset. Our pipeline is built on the popular dataset COCO. Thus, we obtain our RefCOCOs dataset. The visual objects in our RefCOCOs have an average area of 1/50 area of an entire image, whereas that of classic VG datasets is 1/5. Furthermore, we propose SoVG-Net with a hierarchical textual infusion module for the novel SoVG task. Finally, we conduct extensive experiments using classic datasets with our RefCOCOs. The results showcase that our built dataset is useful for advancing VG research, and our proposed SoVG-Net is a strong baseline. Our dataset and code will be made publicly available after review.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Xu, Mengting, Gu, Shi, Lin, Peng, Ma, De, Tang, Huajin, Zheng, Qian, Pan, Gang
As the third generation of neural networks, Spiking Neural Networks (SNNs) have demonstrated remarkable potential across diverse applications owing to their unique temporal dynamics. In recent years, analyzing the robustness of SNNs from a temporal perspective has become an emerging research focus. However, most existing works examine only the overall temporal behavior of SNNs, typically applying adversarial attacks that rely on time-averaged gradients. In this study, we revisit SNN robustness through the lens of temporal granularity, emphasizing the distinct behaviors that occur at individual time steps. We first introduce a Temporal Granularity Attack (TG-Attack), which selectively perturbs gradients at specific time steps. This approach enables a finer-grained evaluation of SNN robustness across time and demonstrates higher attack success rates than traditional gradient-averaging methods. Furthermore, we theoretically show that the robustness of SNNs at a given time step is determined by the Hessian of the input-output gradient at that step, which we define as Temporal Sensitivity (TS). By calculating the Temporal Sensitivity Value (TSV) for each time step, robustness can be effectively estimated without generating adversarial examples. Finally, we propose a Temporal Granularity Regularization (TG-Reg) term that constrains the TSV across all time steps, thereby improving the model's overall robustness. Experimental evaluations confirm that our framework consistently outperforms existing state-of-the-art methods.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Novel view synthesis for dynamic scenes remains challenging due to complex motion variations. Recent methods represent dynamic and static regions with separate Gaussians to improve efficiency and accuracy, but inaccurate assignment of static and dynamic Gaussian primitives still limits performance. We identify two key issues, namely inaccurate mask priors and improper tag representations, which lead to boundary artifacts, loss of fine-grained motion details, and overfitting on input views, resulting in degraded side-view synthesis. To address these problems, we propose a spatio-temporally fine-grained mask field and a discontinuous dynamic-static tagging field to achieve accurate assignment of dynamic and static Gaussian primitives, enabling high-quality novel view synthesis, especially in fine-grained motions, motion boundary regions, and side viewpoints. Experiments show that our method achieves state-of-the-art rendering quality and real-time performance.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表