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20232026
most citedFSPGD: Rethinking Black-box Attacks on Semantic Segmentation

1 citations · 1 across the 9 of their papers we have counts for

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cs.NE2025

Stabilizing Direct Training of Spiking Neural Networks: Membrane Potential Initialization and Threshold-robust Surrogate Gradient

Hyunho Kook, Byeongho Yu, Jeong Min Oh +1

Recent advancements in the direct training of Spiking Neural Networks (SNNs) have demonstrated high-quality outputs even at early timesteps, paving the way for novel energy-efficie…

cs.RO2025

Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking

Junhyuk So, Chiwoong Lee, Shinyoung Lee +2

Generative Behavior Cloning (GBC) is a simple yet effective framework for robot learning, particularly in multi-task settings. Recent GBC methods often employ diffusion policies wi…

cs.CV2025

Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation

Junhyuk So, Hyunho Kook, Chaeyeon Jang +1

Autoregressive (AR) modeling has recently emerged as a promising new paradigm in visual generation, but its practical adoption is severely constrained by the slow inference speed o…

cs.CV2025

Grouped Speculative Decoding for Autoregressive Image Generation

Junhyuk So, Juncheol Shin, Hyunho Kook +1

Recently, autoregressive (AR) image models have demonstrated remarkable generative capabilities, positioning themselves as a compelling alternative to diffusion models. However, th…

cs.LG2025

The Oversmoothing Fallacy: A Misguided Narrative in GNN Research

MoonJeong Park, Sunghyun Choi, Jaeseung Heo +2

Oversmoothing has been recognized as a main obstacle to building deep Graph Neural Networks (GNNs), limiting the performance. This position paper argues that the influence of overs…

cs.CV2025

PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models

Junhyuk So, Jiwoong Shin, Chaeyeon Jang +1

Recently, diffusion models have achieved significant advances in vision, text, and robotics. However, they still face slow generation speeds due to sequential denoising processes.…

cs.CV2025★ 1 cited

FSPGD: Rethinking Black-box Attacks on Semantic Segmentation

Eun-Sol Park, MiSo Park, Yong-Goo Shin

Black-box adversarial attacks on semantic segmentation remain a challenging problem, particularly in the black-box transfer attack setting where perturbations crafted on a surrogat…