88 citations · 106 across the 20 of their papers we have counts for
11 papers · 1 filter
Temporal State Transport in Video Generation: Diagnosing and Correcting Spectral Imbalance
Luyao Tang, Bingjun Luo, Dong Yi +5
Reliable video generation requires more than high-quality frames to form a coherent story: a model must maintain a persistent state, transporting visual attributes such as identity…
One Adapter, Many Tasks: Task-Conditioned Feature Transformations for Continual Learning
Yunxiang Fu, Meng Lou, Yizhou Yu
Class-incremental learning (CIL) requires a model to incrementally learn tasks that contain new classes without accessing earlier training data while preserving the ability to reco…
Identifying Latent Concepts and Structures for Generalized Category Discovery
Boyang Dai, Chaoqi Chen, Yizhou Yu
Generalized Category Discovery (GCD) aims to recognize known classes while autonomously discovering novel ones in open-world settings. However, current approaches primarily focus o…
Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysis
Boyang Dai, Chaoqi Chen, Yizhou Yu
Out-of-distribution (OOD) detection is crucial for ensuring the reliability of deep learning models. Existing methods mostly focus on regular entangled representations to discrimin…
Leveraging Verifier-Based Reinforcement Learning in Image Editing
Hanzhong Guo, Jie Wu, Jie Liu +6
While Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm for text-to-image generation, its application to image editing remains largely unexplored. A k…
Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
Meng Lou, Hanzhong Guo, Linwei Chen +1
Recent studies suggest that Reinforcement Fine-Tuning (RFT) is inherently more resilient to catastrophic forgetting than Supervised Fine-Tuning (SFT). However, whether RFT (e.g., G…
Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms
Muyang He, Hanzhong Guo, Junxiong Lin +1
The rapid evolution of video generation has enabled models to simulate complex physical dynamics and long-horizon causalities, positioning them as potential world simulators. Howev…
CGSA: Class-Guided Slot-Aware Adaptation for Source-Free Object Detection
Boyang Dai, Zeng Fan, Zihao Qi +2
Source-Free Domain Adaptive Object Detection (SF-DAOD) aims to adapt a detector trained on a labeled source domain to an unlabeled target domain without retaining any source data.…
Vision Transformers Need More Than Registers
Cheng Shi, Yizhou Yu, Sibei Yang
Vision Transformers (ViTs), when pre-trained on large-scale data, provide general-purpose representations for diverse downstream tasks. However, artifacts in ViTs are widely observ…
Parameters as Experts: Adapting Vision Models with Dynamic Parameter Routing
Meng Lou, Stanley Yu, Yizhou Yu
Adapting pre-trained vision models using parameter-efficient fine-tuning (PEFT) remains challenging, as it aims to achieve performance comparable to full fine-tuning using a minima…
Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts
Meng Lou, Yunxiang Fu, Yizhou Yu
Continual learning, especially class-incremental learning (CIL), on the basis of a pre-trained model (PTM) has garnered substantial research interest in recent years. However, how…