Poster "foundation models" Papers
100 papers found • Page 2 of 2
Conference
TAViS: Text-bridged Audio-Visual Segmentation with Foundation Models
Ziyang Luo, Nian Liu, Xuguang Yang et al.
The Indra Representation Hypothesis
Jianglin Lu, Hailing Wang, Kuo Yang et al.
This Time is Different: An Observability Perspective on Time Series Foundation Models
Ben Cohen, Emaad Khwaja, Youssef Doubli et al.
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
Xiaoming Shi, Shiyu Wang, Yuqi Nie et al.
ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools
Shaofeng Yin, Ting Lei, Yang Liu
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
Kanghui Ning, Zijie Pan, Yu Liu et al.
UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection
Zhaopeng Gu, Bingke Zhu, Guibo Zhu et al.
UniWav: Towards Unified Pre-training for Speech Representation Learning and Generation
Alexander Liu, Sang-gil Lee, Chao-Han Huck Yang et al.
vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation
Bastian Wittmann, Yannick Wattenberg, Tamaz Amiranashvili et al.
ZeroPatcher: Training-free Sampler for Video Inpainting and Editing
Shaoshu Yang, Yingya Zhang, Ran He
Zero-shot forecasting of chaotic systems
Yuanzhao Zhang, William Gilpin
3x2: 3D Object Part Segmentation by 2D Semantic Correspondences
Anh Thai, Weiyao Wang, Hao Tang et al.
Active Label Correction for Semantic Segmentation with Foundation Models
Hoyoung Kim, SEHYUN HWANG, Suha Kwak et al.
Adversarially Robust Hypothesis Transfer Learning
Yunjuan Wang, Raman Arora
Any2Point: Empowering Any-modality Transformers for Efficient 3D Understanding
YIWEN TANG, Renrui Zhang, Jiaming Liu et al.
Asymmetry in Low-Rank Adapters of Foundation Models
Jiacheng Zhu, Kristjan Greenewald, Kimia Nadjahi et al.
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Yair Schiff, Chia Hsiang Kao, Aaron Gokaslan et al.
Collaborating Foundation Models for Domain Generalized Semantic Segmentation
Yasser Benigmim, Subhankar Roy, Slim Essid et al.
Compute Better Spent: Replacing Dense Layers with Structured Matrices
Shikai Qiu, Andres Potapczynski, Marc Finzi et al.
Constructing Concept-based Models to Mitigate Spurious Correlations with Minimal Human Effort
Jeeyung Kim, Ze Wang, Qiang Qiu
Differentially Private Bias-Term Fine-tuning of Foundation Models
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha et al.
Diffusion Models for Open-Vocabulary Segmentation
Laurynas Karazija, Iro Laina, Andrea Vedaldi et al.
Discovering Bias in Latent Space: An Unsupervised Debiasing Approach
Dyah Adila, Shuai Zhang, Boran Han et al.
Enhancing Recipe Retrieval with Foundation Models: A Data Augmentation Perspective
Fangzhou Song, Bin Zhu, Yanbin Hao et al.
Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning
Kai Gan, Tong Wei
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
Shangchao Su, Bin Li, Xiangyang Xue
GS-Pose: Category-Level Object Pose Estimation via Geometric and Semantic Correspondence
Pengyuan Wang, Takuya Ikeda, Robert Lee et al.
Let Go of Your Labels with Unsupervised Transfer
Artyom Gadetsky, Yulun Jiang, Maria Brbic
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
Yuji Roh, Qingyun Liu, Huan Gui et al.
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
Jiang-Xin Shi, Tong Wei, Zhi Zhou et al.
MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
Kai Zhang, Yi Luan, Hexiang Hu et al.
MarineInst: A Foundation Model for Marine Image Analysis with Instance Visual Description
Ziqiang Zheng, Yiwei Chen, Huimin Zeng et al.
MESA: Matching Everything by Segmenting Anything
Yesheng Zhang, Xu Zhao
Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks
MohammadReza Davari, Eugene Belilovsky
MOMENT: A Family of Open Time-series Foundation Models
Mononito Goswami, Konrad Szafer, Arjun Choudhry et al.
One-Prompt to Segment All Medical Images
Wu, Min Xu
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla et al.
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
Golnoosh Farnadi, Mohammad Havaei, Negar Rostamzadeh
Position: On the Societal Impact of Open Foundation Models
Sayash Kapoor, Rishi Bommasani, Kevin Klyman et al.
Position: Open-Endedness is Essential for Artificial Superhuman Intelligence
Edward Hughes, Michael Dennis, Jack Parker-Holder et al.
Position: Towards Unified Alignment Between Agents, Humans, and Environment
Zonghan Yang, an liu, Zijun Liu et al.
Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models
Amrith Setlur, Saurabh Garg, Virginia Smith et al.
Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective
Yang Chen, Cong Fang, Zhouchen Lin et al.
RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
Yufei Wang, Zhou Xian, Feng Chen et al.
Robustness Tokens: Towards Adversarial Robustness of Transformers
Brian Pulfer, Yury Belousov, Slava Voloshynovskiy
Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention
Jiaqi Zhang, Joel Jennings, Agrin Hilmkil et al.
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation
Yunhe Gao
Transferring Knowledge From Large Foundation Models to Small Downstream Models
Shikai Qiu, Boran Han, Danielle Robinson et al.
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning
Shikun Feng, Yuyan Ni, Li et al.
ViP: A Differentially Private Foundation Model for Computer Vision
Yaodong Yu, Maziar Sanjabi, Yi Ma et al.