3 citations · 11 across the 32 of their papers we have counts for
9 papers · 1 filter
Grid: Omni Visual Generation
Cong Wan, Xiangyang Luo, Hao Luo +7
Visual generation has witnessed remarkable progress in single-image tasks, yet extending these capabilities to temporal sequences remains challenging. Current approaches either bui…
LPT: Less-overfitting Prompt Tuning for Vision-Language Model
Chenhao Ding, Xinyuan Gao, Songlin Dong +5
Vision-language models (VLMs) have demonstrated exceptional generalization capabilities for downstream tasks. Due to its efficiency, prompt learning has gradually become a more eff…
Dynamic Integration of Task-Specific Adapters for Class Incremental Learning
Jiashuo Li, Shaokun Wang, Bo Qian +4
Non-exemplar class Incremental Learning (NECIL) enables models to continuously acquire new classes without retraining from scratch and storing old task exemplars, addressing privac…
Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
Cong Wan, Yuhang He, Xiang Song +1
Diffusion models have revolutionized customized text-to-image generation, allowing for efficient synthesis of photos from personal data with textual descriptions. However, these ad…
Beyond Prompt Learning: Continual Adapter for Efficient Rehearsal-Free Continual Learning
Xinyuan Gao, Songlin Dong, Yuhang He +2
The problem of Rehearsal-Free Continual Learning (RFCL) aims to continually learn new knowledge while preventing forgetting of the old knowledge, without storing any old samples an…
Projecting Points to Axes: Oriented Object Detection via Point-Axis Representation
Zeyang Zhao, Qilong Xue, Yuhang He +3
This paper introduces the point-axis representation for oriented object detection, emphasizing its flexibility and geometrically intuitive nature with two key components: points an…
Continual Novel Class Discovery via Feature Enhancement and Adaptation
Yifan Yu, Shaokun Wang, Yuhang He +2
Continual Novel Class Discovery (CNCD) aims to continually discover novel classes without labels while maintaining the recognition capability for previously learned classes. The ma…
I2CANSAY:Inter-Class Analogical Augmentation and Intra-Class Significance Analysis for Non-Exemplar Online Task-Free Continual Learning
Songlin Dong, Yingjie Chen, Yuhang He +3
Online task-free continual learning (OTFCL) is a more challenging variant of continual learning which emphasizes the gradual shift of task boundaries and learns in an online mode.…
CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar Class-Incremental Learning
Xinyuan Gao, Songlin Dong, Yuhang He +2
In real-world applications, dynamic scenarios require the models to possess the capability to learn new tasks continuously without forgetting the old knowledge. Experience-Replay m…