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首次观测 · 记录 #1CVF · CVPR 2026:cvf:9fd1e38d16a2d020b81f025a065abbe8CVF · CVPR 2026

AE2VID: Event-based Video Reconstruction via Aperture Modulation

Bai, Chenxu, Li, Boyu, Duan, Peiqi, Zhou, Xinyu, Lou, Hanyue, Shi, Boxin

原文摘要

Event-based video reconstruction seeks to recover high-speed, high-dynamic-range videos from event streams. While existing approaches rely exclusively on motion-triggered events, these events are inherently sparse and primarily capture dynamic regions. Therefore, they often suffer from error accumulation and degraded quality in regions with few events. In this work, we introduce aperture-modulation-triggered events as a complementary mechanism to enrich the captured scene information. Specifically, we periodically modulate the aperture to actively generate dense event signals, thereby encoding intensity cues even in static or low-motion regions. Building upon this idea, we design an AE2VID framework that jointly leverages aperture-modulation-triggered and motion-triggered events to enhance the fidelity of predictions. The proposed framework consists of two subnetworks for the dedicated processing of both event types. We further collect a real dataset and validate the effectiveness of our method. Extensive experiments show our superiority over state-of-the-art methods. Code and data will be available at https://github.com/a1henu/AE2VID/.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:a1b5ce775217b75c3c35cfebf4652a01CVF · CVPR 2026

Diagnose, Correct, and Learn from Manipulation Failures via Visual Symbols

Zeng, Xianchao, Zhou, Xinyu, Li, Youcheng, Shi, Jiayou, Li, Tianle, Chen, Liangming, Ren, Lei, Li, Yong-Lu

原文摘要

Vision-Language-Action (VLA) models have recently achieved remarkable progress in robotic manipulation, yet they remain limited in failure diagnosis and learning from failures. Additionally, existing failure datasets are mostly generated programmatically in simulation, which limits their generalization to the real world. In light of these, we introduce ViFailback, a framework designed to diagnose robotic manipulation failures and provide both textual and visual correction guidance. Our framework utilizes explicit visual symbols to enhance annotation efficiency. We further release the ViFailback dataset, a large-scale collection of 58,128 Visual Question Answering (VQA) pairs along with their corresponding 5,202 real-world manipulation trajectories. Based on the dataset, we establish ViFailback-Bench, a benchmark of 11 fine-grained VQA tasks designed to assess the failure diagnosis and correction abilities of Vision-Language Models (VLMs), featuring ViFailback-Bench Lite for closed-ended and ViFailback-Bench Hard for open-ended evaluation. To demonstrate the effectiveness of our framework, we built the ViFailback-8B VLM, which not only achieves significant overall performance improvement on ViFailback-Bench but also generates visual symbols for corrective action guidance. Finally, by integrating ViFailback-8B with a VLA model, we conduct real-world robotic experiments demonstrating its ability to assist the VLA model in recovering from failures. Project page: https://x1nyuzhou.github.io/vifailback.github.io/

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:d07908a12bef42b936b8dc8612504ec0CVF · CVPR 2026

Smoothing the Score Function to Enhance Generalization in Diffusion Models

Zhou, Xinyu, Zhang, Jiawei, Wright, Stephen J.

原文摘要

Diffusion models achieve remarkable generation quality, yet face a fundamental challenge known as memorization, where generated samples can replicate training samples exactly. We develop a theoretical framework to explain this phenomenon by showing that the empirical score function (the score function corresponding to the empirical distribution) is a weighted sum of the score functions of Gaussian distributions, in which the weights are sharp softmax functions. This structure causes individual training samples to dominate the score function, resulting in sampling collapse. In practice, approximating the empirical score function with a neural network can partially alleviate this issue and improve generalization. Our theoretical framework explains why: In training, the neural network learns a smoother approximation of the weighted sum, allowing the sampling process to be influenced by local manifolds rather than single points. Leveraging this insight, we propose two novel methods to further enhance generalization: (1) Noise Unconditioning enables each training sample to adaptively determine its score function weight to increase the effect of more training samples, thereby preventing single-point dominance and mitigating collapse. (2) Temperature Smoothing introduces an explicit parameter to control the smoothness. By increasing the temperature in the softmax weights, we naturally reduce the dominance of any single training sample and mitigate memorization. Experiments across multiple datasets validate our theoretical analysis and demonstrate the effectiveness of the proposed methods in improving generalization while maintaining high generation quality.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗