Copertina

ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Feng, Tao, Li, Wei, Zhu, Didi, Yuan, Hangjie, Zheng, Wendi, Zhang, Dan, Tang, Jie
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computer Vision and Pattern Recognition Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

ZeroFlow: Scalable Scene Flow via Distillation
di: Vedder, Kyle, et al.
Pubblicazione: (2023)

Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
di: Garcia, Gonzalo Martin, et al.
Pubblicazione: (2024)

Fine-tuning MLLMs Without Forgetting Is Easier Than You Think
di: Li, He, et al.
Pubblicazione: (2026)

Representation Alignment for Just Image Transformers is not Easier than You Think
di: Shin, Jaeyo, et al.
Pubblicazione: (2026)

V-GRPO: Online Reinforcement Learning for Denoising Generative Models Is Easier than You Think
di: Tang, Bingda, et al.
Pubblicazione: (2026)

Representation Entanglement for Generation: Training Diffusion Transformers Is Much Easier Than You Think
di: Wu, Ge, et al.
Pubblicazione: (2025)

Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
di: Lou, Meng, et al.
Pubblicazione: (2026)

Extrapolating and Decoupling Image-to-Video Generation Models: Motion Modeling is Easier Than You Think
di: Tian, Jie, et al.
Pubblicazione: (2025)

CRAFT: Aligning Diffusion Models with Fine-Tuning Is Easier Than You Think
di: Sun, Zening, et al.
Pubblicazione: (2026)

Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
di: Yu, Sihyun, et al.
Pubblicazione: (2024)

Easier Painting Than Thinking: Can Text-to-Image Models Set the Stage, but Not Direct the Play?
di: Li, Ouxiang, et al.
Pubblicazione: (2025)

Aligning Text to Image in Diffusion Models is Easier Than You Think
di: Lee, Jaa-Yeon, et al.
Pubblicazione: (2025)

Solving the Catastrophic Forgetting Problem in Generalized Category Discovery
di: Cao, Xinzi, et al.
Pubblicazione: (2025)

Multimodal Representation Alignment for Image Generation: Text-Image Interleaved Control Is Easier Than You Think
di: Chen, Liang, et al.
Pubblicazione: (2025)

MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems
di: Chen, Shuhang, et al.
Pubblicazione: (2025)

DFVEdit: Conditional Delta Flow Vector for Zero-shot Video Editing
di: Cai, Lingling, et al.
Pubblicazione: (2025)

Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector
di: Wu, Qirui, et al.
Pubblicazione: (2025)

MuseumMaker: Continual Style Customization without Catastrophic Forgetting
di: Liu, Chenxi, et al.
Pubblicazione: (2024)

Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification
di: Zheng, Weixi, et al.
Pubblicazione: (2025)

Watch Wider and Think Deeper: Collaborative Cross-modal Chain-of-Thought for Complex Visual Reasoning
di: Lu, Wenting, et al.
Pubblicazione: (2026)

Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective
di: Lu, Aojun, et al.
Pubblicazione: (2025)

Make Domain Shift a Catastrophic Forgetting Alleviator in Class-Incremental Learning
di: Chen, Wei, et al.
Pubblicazione: (2024)

Why Does RL Generalize Better Than SFT? A Data-Centric Perspective on VLM Post-Training
di: Lu, Aojun, et al.
Pubblicazione: (2026)

EXACFS -- A CIL Method to mitigate Catastrophic Forgetting
di: Balasubramanian, S, et al.
Pubblicazione: (2024)

Continual Learning and Catastrophic Forgetting
di: van de Ven, Gido M., et al.
Pubblicazione: (2024)

Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation
di: Bafghi, Reza Akbarian, et al.
Pubblicazione: (2025)

Reducing Catastrophic Forgetting in Online Class Incremental Learning Using Self-Distillation
di: Nagata, Kotaro, et al.
Pubblicazione: (2024)

Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection
di: Zhou, You, et al.
Pubblicazione: (2025)

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases
di: Poggi, Matteo, et al.
Pubblicazione: (2025)

Preventing Catastrophic Forgetting through Memory Networks in Continuous Detection
di: Bhatt, Gaurav, et al.
Pubblicazione: (2024)

Quantified Task Misalignment to Inform PEFT: An Exploration of Domain Generalization and Catastrophic Forgetting in CLIP
di: Niss, Laura, et al.
Pubblicazione: (2024)

MCF-VC: Mitigate Catastrophic Forgetting in Class-Incremental Learning for Multimodal Video Captioning
di: Xiong, Huiyu, et al.
Pubblicazione: (2024)

Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting
di: Bafghi, Reza Akbarian, et al.
Pubblicazione: (2024)

C-Flat++: Towards a More Efficient and Powerful Framework for Continual Learning
di: Li, Wei, et al.
Pubblicazione: (2025)

Continual GUI Agents
di: Liu, Ziwei, et al.
Pubblicazione: (2026)

A Faster Path to Continual Learning
di: Li, Wei, et al.
Pubblicazione: (2026)

VideoMAR: Autoregressive Video Generatio with Continuous Tokens
di: Yu, Hu, et al.
Pubblicazione: (2025)

Adapt before Continual Learning
di: Lu, Aojun, et al.
Pubblicazione: (2025)

Revisiting Neural Networks for Continual Learning: An Architectural Perspective
di: Lu, Aojun, et al.
Pubblicazione: (2024)

Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models
di: Laurensi, Israel A., et al.
Pubblicazione: (2024)