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20182026
most citedCounterfactual Visual Explanations

38 citations · 82 across the 40 of their papers we have counts for

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50 papers · 1 filter

cs.CV2026

XClipGS: Exact Half-Space Clipping for Medical Volume Gaussian Splatting

Zhongpai Gao, Benjamin Planche, Meng Zheng +4

Gaussian-splatting proxies enable interactive rendering of volumetric medical scans, but a clipping plane exposes anatomy not constrained by external-view training and intersects p…

cs.CV2026

Point Tracking in Surgery--The 2025 Surgical Tattoos in Infrared Challenge (STIRC2025)

Adam Schmidt, Mert Asim Karaoglu, Zijian Wu +32

Point tracking in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning…

cs.CV2026

In defense of the two-stage framework for open-set domain adaptive semantic segmentation

Wenqi Ren, Weijie Wang, Meng Zheng +4

Open-Set Domain Adaptation for Semantic Segmentation (OSDA-SS) presents a significant challenge, as it requires both domain adaptation for known classes and the distinction of unkn…

cs.CV2025

Consistent Instance Field for Dynamic Scene Understanding

Junyi Wu, Van Nguyen Nguyen, Benjamin Planche +11

We introduce Consistent Instance Field, a continuous and probabilistic spatio-temporal representation for dynamic scene understanding. Unlike prior methods that rely on discrete tr…

cs.CV2025

From Particles to Fields: Reframing Photon Mapping with Continuous Gaussian Photon Fields

Jiachen Tao, Benjamin Planche, Van Nguyen Nguyen +13

Accurately modeling light transport is essential for realistic image synthesis. Photon mapping provides physically grounded estimates of complex global illumination effects such as…

cs.CV2025

MedGRPO: Multi-Task Reinforcement Learning for Heterogeneous Medical Video Understanding

Yuhao Su, Anwesa Choudhuri, Zhongpai Gao +8

Large vision-language models struggle with medical video understanding, where spatial precision, temporal reasoning, and clinical semantics are critical. To address this, we first…