Merge pull request #118 from castacks/yutian/update

Update Yutian profile & add new publication entry

Author: seungchan-kim

_bibliography/references.bib

@@ -1,3 +1,11 @@
+@article{chen2025cometokens,
+	title	  	 = {Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers},
+	author 		 = {Chen, Yutian and Qiu, Yuheng and Li, Ruogu and Agha, Ali and Omidshafiei, Shayegan and Patrikar, Jay and Scherer, Sebastian},
+	year 	     = {2025},
+	url          = {https://arxiv.org/abs/2511.14751},
+	journal      = {arXiv preprint arXiv:2511.14751},
+	abstract 	 = {We propose Confidence-Guided Token Merging (Co-Me), an acceleration mechanism for visual geometric transformers without retraining or finetuning the base model. Co-Me distilled a light-weight confidence predictor to rank tokens by uncertainty and selectively merge low-confidence ones, effectively reducing computation while maintaining spatial coverage. Compared to similarity-based merging or pruning, the confidence signal in Co-Me reliably indicates regions emphasized by the transformer, enabling substantial acceleration without degrading performance. Co-Me applies seamlessly to various multi-view and streaming visual geometric transformers, achieving speedups that scale with sequence length. When applied to VGGT and MapAnything, Co-Me achieves up to 11.3x and 7.2x speedup, making visual geometric transformers practical for real-time 3D perception and reconstruction.}
+}
 @misc{yu2025unified,
 	title        = {Unified Spherical Frontend: Learning Rotation-Equivariant Representations of Spherical Images from Any Camera},
 	shorttitle   = {Unified Spherical Frontend},
@@ -667,7 +675,7 @@ @article{moon2023time-optimal
 }
 @article{qiu2023airimu,
 	title        = {{AirIMU: Learning} Uncertainty Propagation for Inertial Odometry},
-	author       = {Qiu, Yuheng and Wang, Chen and Zhou, Xunfei and Xia, Youjie and Scherer, Sebastian},
+	author 	     = {Qiu, Yuheng and Wang, Chen and Xu, Can and Chen, Yutian and Zhou, Xunfei and Xia, Youjie and Scherer, Sebastian},
 	year         = {2023},
 	journal      = {arXiv preprint arXiv:2310.04874},
 	url          = {https://arxiv.org/pdf/2310.04874}
@@ -1391,7 +1399,7 @@ @article{fang20203d-siamrpn
 	doi          = {10.1109/JSEN.2020.3033034},
 	url          = {https://arxiv.org/pdf/2108.05630}
 }
-@inproceedings{ho2020``provably,
+@inproceedings{ho2020provably,
 	title        = {``{{Provably Safe}}'' in the {{Wild}}: {{Testing Control Barrier Functions}} on a {{Vision-Based Quadrotor}} in an {{Outdoor Environment}}},
 	author       = {Ho, Cherie and Shih, Katherine and Grover, Jaskaran and Liu, Changliu and Scherer, Sebastian},
 	year         = {2020},

_team/yutianch.md

@@ -7,10 +7,12 @@ layout: team_member_personal_page
 image: /img/team/yutianch.jpg
 ---
 
-Yutian is dedicated to improve the spatial understanding ability of autonomous systems, which requires a combination of geometry and semantic information about the surroundings. His research experience on visual-inertial SLAM at the AirLAB focuses on improving the recognition of scene geometry in adversarial conditions, while his previous experience in Natural Language Processing (NLP) and semantic segmentation focuses on grasping and interpreting the semantic of scenes.
+Yutian is an M.S. Robotics student at Carnegie Mellon University, advised by Prof. Sebastian Scherer in the AirLab. His research focuses on enabling machines to understand and interact with the physical reality through robust geometric and semantic perception. Yutian is broadly interested in visual geometry, spatial reasoning, and the development of scalable algorithms that bridge perception and action for autonomous systems.
 
 # Email #
+
 yutianch@andrew.cmu.edu
 
 # Personal webpage #
-<a href="https://www.yutianchen.blog" target="_blank">www.yutianchen.blog</a>
+
+<a href="https://www.yutianchen.blog/" target="_blank">Personal Homepage</a>