1 citations · 1 across the 7 of their papers we have counts for
7 papers
Semi-Supervised Synthetic Data Generation with Fine-Grained Relevance Control for Short Video Search Relevance Modeling
Haoran Li, Zhiming Su, Junyan Yao +6
Synthetic data is widely adopted in embedding models to ensure diversity in training data distributions across dimensions such as difficulty, length, and language. However, existin…
Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing
Haonan Lin, Mengmeng Wang, Jiahao Wang +7
Text-guided diffusion models have significantly advanced image editing, enabling high-quality and diverse modifications driven by text prompts. However, effective editing requires…
Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery
Haonan Lin, Wenbin An, Jiahao Wang +6
Recent advancements have shown promise in applying traditional Semi-Supervised Learning strategies to the task of Generalized Category Discovery (GCD). Typically, this involves a t…
Knowledge Acquisition Disentanglement for Knowledge-based Visual Question Answering with Large Language Models
Wenbin An, Feng Tian, Jiahao Nie +7
Knowledge-based Visual Question Answering (KVQA) requires both image and world knowledge to answer questions. Current methods first retrieve knowledge from the image and external k…
DreamSalon: A Staged Diffusion Framework for Preserving Identity-Context in Editable Face Generation
Haonan Lin, Mengmeng Wang, Yan Chen +6
While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centered images, novel challenges arise with a nuanced task of "identity fine editi…
Transfer and Alignment Network for Generalized Category Discovery
Wenbin An, Feng Tian, Wenkai Shi +4
Generalized Category Discovery is a crucial real-world task. Despite the improved performance on known categories, current methods perform poorly on novel categories. We attribute…
Generalized Category Discovery with Large Language Models in the Loop
Wenbin An, Wenkai Shi, Feng Tian +8
Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only…