15 citations · 28 across the 11 of their papers we have counts for
11 papers
Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models
Kaican Li, Weiyan Xie, Yongxiang Huang +5
Fine-tuning foundation models often compromises their robustness to distribution shifts. To remedy this, most robust fine-tuning methods aim to preserve the pre-trained features. H…
Robustness May be More Brittle than We Think under Different Degrees of Distribution Shifts
Kaican Li, Yifan Zhang, Lanqing Hong +2
Out-of-distribution (OOD) generalization is a complicated problem due to the idiosyncrasies of possible distribution shifts between training and test domains. Most benchmarks emplo…
A Causal Framework to Unify Common Domain Generalization Approaches
Nevin L. Zhang, Kaican Li, Han Gao +5
Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s). It is a fundamental problem…
Exploring Compositional Visual Generation with Latent Classifier Guidance
Changhao Shi, Haomiao Ni, Kai Li +3
Diffusion probabilistic models have achieved enormous success in the field of image generation and manipulation. In this paper, we explore a novel paradigm of using the diffusion m…
Consistency Regularization for Domain Generalization with Logit Attribution Matching
Han Gao, Kaican Li, Weiyan Xie +5
Domain generalization (DG) is about training models that generalize well under domain shift. Previous research on DG has been conducted mostly in single-source or multi-source sett…
Conditional Image-to-Video Generation with Latent Flow Diffusion Models
Haomiao Ni, Changhao Shi, Kai Li +2
Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label li…
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms
Runpeng Yu, Hong Zhu, Kaican Li +5
Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms…
CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving
Kaican Li, Kai Chen, Haoyu Wang +10
Contemporary deep-learning object detection methods for autonomous driving usually assume prefixed categories of common traffic participants, such as pedestrians and cars. Most exi…
OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization
Nanyang Ye, Kaican Li, Haoyue Bai +5
Deep learning has achieved tremendous success with independent and identically distributed (i.i.d.) data. However, the performance of neural networks often degenerates drastically…
MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition
Zhanghan Ke, Jiayu Sun, Kaican Li +2
Existing portrait matting methods either require auxiliary inputs that are costly to obtain or involve multiple stages that are computationally expensive, making them less suitable…
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning
Zhanghan Ke, Di Qiu, Kaican Li +2
We investigate the generalization of semi-supervised learning (SSL) to diverse pixel-wise tasks. Although SSL methods have achieved impressive results in image classification, the…