The DoC is committed to supporting our researchers and academics in their endeavors to explore uncharted territories and solve complex challenges.
The Department of Computing at Imperial College London is proud to announce that our research papers have been accepted at the Conference on Computer Vision and Pattern Recognition (CVPR). As the most prestigious event in the field of computer vision, CVPR is the ultimate platform for showcasing ground-breaking research and innovations that have the potential to redefine the technological landscape.
Research papers accepted at CVPR'24:
NRDF: Neural Riemannian Distance Fields for Learning Articulated Pose Priors Yannan He (University of Tübingen) · Garvita Tiwari (University of Tuebingen and MPI-Saarbrucken) · Tolga Birdal (Imperial College London) · Jan Lenssen (Saarland Informatics Campus, Max-Planck Institute) · Gerard Pons-Moll (University of Tübingen)
HyperSDFusion: Bridging Hierarchical Structures in Language and Geometry for Enhanced 3D Text2Shape Generation · Zhiying Leng (Technical University of Munich) · Tolga Birdal (Imperial College London) · Xiaohui Liang (Zhongguancun Laboratory) · Federico Tombari (Google, TUM)
Fun with Flags: Robust Principal Directions via Flag Manifolds · Tolga Birdal (Imperial College London) · Nathan Mankovich (University of Valencia)
Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing · Jan-Nico Zaech (ETH Zürich) · Martin Danelljan (ETH Zurich) · Tolga Birdal (Imperial College London) · Luc Van Gool (ETH Zurich)
Gaussian Splatting SLAM · Hidenobu Matsuki (Imperial College London) · Riku Murai (Imperial College London) · Paul Kelly (Imperial College London) · Andrew J. Davison (Imperial College London)
SuperPrimitive: Scene Reconstruction at a Primitive Level · Kirill Mazur (Imperial College London) · Gwangbin Bae (Imperial College London) · Andrew J. Davison (Imperial College London)
EscherNet: A Generative Model for Scalable View Synthesis · Xin Kong (Imperial College London) · Shikun Liu (Imperial College London) · Xiaoyang Lyu (University of Hong Kong) · Marwan Taher (Imperial College London) · Xiaojuan Qi (University of Hong Kong) · Andrew J. Davison (Imperial College London)
Rethinking Inductive Biases for Surface Normal Estimation · Gwangbin Bae (Imperial College London) · Andrew J. Davison (Imperial College London)
Design2Cloth: 3D Cloth Generation from 2D Masks · Jiali Zheng (Imperial College London) · Rolandos Alexandros Potamias (Imperial College London) · Stefanos Zafeiriou (Imperial College London)
Locally Adaptive Neural 3D Morphable Models · Michail Tarasiou (Imperial College London) · Rolandos Alexandros Potamias (Imperial College London) · Eimear O' Sullivan (Huawei Technologies Ltd.) · Stylianos Ploumpis (Imperial College London) · Stefanos Zafeiriou (Imperial College London)
Neural Sign Actors: A diffusion model for 3D sign language production from text · Vasileios Baltatzis (None) · Rolandos Alexandros Potamias (Imperial College London) · Evangelos Ververas (Huawei Technologies Ltd.) · Guanxiong Sun (Huawei Technologies Ltd.) · Jiankang Deng (Imperial College London & Huawei UKRD) · Stefanos Zafeiriou (Imperial College London)
G-FARS: Gradient-Field-based Auto-Regressive Sampling for 3D Part Grouping · Junfeng Cheng (Imperial College London) · Tania Stathaki (Imperial College London)
CVPR provides an unparalleled forum for researchers from around the globe to share their insights and discoveries, fostering collaboration and sparking innovation. Dr. Birdal’s presence at this esteemed conference places him among the world's leading visionaries in computer vision, highlighting the department's role as a hub of pioneering research and technological progress.
Research papers accepted at ICLR'24:
Research papers were also accepted at the International Conference on Learning Representations (ICLR) 2024 which is is a machine learning conference.
Variational Inference for SDEs Driven by Fractional Noise (spotlight) · Rembert Daems · Manfred Opper · Guillaume Crevecoeur · Tolga Birdal (Imperial College London)
C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion · Hee Suk Yoon · Eunseop Yoon · Joshua Tian Jin Tee · Mark A. Hasegawa-Johnson · Yingzhen Li (Imperial College London) · Chang D. Yoo
Grounded Object-Centric Learning · Avinash Kori · Francesco Locatello · Fabio De Sousa Ribeiro · Francesca Toni · Ben Glocker (Imperial College London)
Post-hoc Bias Scoring Is Optimal For Fair Classification. ICLR 2024 (spotlight) · Wenlong Chen* (Imperial College London) · Yegor Klochkov* · Yang Liu
The Department of Computing is committed to supporting our researchers and academics in their endeavors to explore uncharted territories and solve complex challenges. The success is a testament to the vibrant research culture and intellectual rigor that define our community.
We invite the Imperial College community and the wider public to join us in congratulating our researchers on this outstanding achievement. their work not only advances the field of computer vision but also inspires the next generation of scientists and engineers to pursue their own path of innovation and discovery.
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Reporter
Mr Ahmed Idle
Department of Computing
