30 citations · 61 across the 18 of their papers we have counts for
7 papers · 1 filter
Content-aware Balanced Spectrum Encoding in Masked Modeling for Time Series Classification
Yudong Han, Haocong Wang, Yupeng Hu +3
Due to the superior ability of global dependency, transformer and its variants have become the primary choice in Masked Time-series Modeling (MTM) towards time-series classificatio…
Vision-guided and Mask-enhanced Adaptive Denoising for Prompt-based Image Editing
Kejie Wang, Xuemeng Song, Meng Liu +2
Text-to-image diffusion models have demonstrated remarkable progress in synthesizing high-quality images from text prompts, which boosts researches on prompt-based image editing th…
Pseudo-triplet Guided Few-shot Composed Image Retrieval
Bohan Hou, Haoqiang Lin, Haokun Wen +3
Composed Image Retrieval (CIR) is a challenging task that aims to retrieve the target image with a multimodal query, i.e., a reference image, and its complementary modification tex…
Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image Retrieval
Haokun Wen, Xuemeng Song, Xiaolin Chen +3
Composed image retrieval (CIR) aims to retrieve the target image based on a multimodal query, i.e., a reference image paired with corresponding modification text. Recent CIR studie…
MMGRec: Multimodal Generative Recommendation with Transformer Model
Han Liu, Yinwei Wei, Xuemeng Song +3
Multimodal recommendation aims to recommend user-preferred candidates based on her/his historically interacted items and associated multimodal information. Previous studies commonl…
Interactive Garment Recommendation with User in the Loop
Federico Becattini, Xiaolin Chen, Andrea Puccia +4
Recommending fashion items often leverages rich user profiles and makes targeted suggestions based on past history and previous purchases. In this paper, we work under the assumpti…
Prompt-based Multi-interest Learning Method for Sequential Recommendation
Xue Dong, Xuemeng Song, Tongliang Liu +1
Multi-interest learning method for sequential recommendation aims to predict the next item according to user multi-faceted interests given the user historical interactions. Existin…