209 citations · 494 across the 26 of their papers we have counts for
11 papers · 1 filter
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification
Fang Peng, Xiaoshan Yang, Linhui Xiao +2
Although significant progress has been made in few-shot learning, most of existing few-shot image classification methods require supervised pre-training on a large amount of sample…
Inversion-Based Style Transfer with Diffusion Models
Yuxin Zhang, Nisha Huang, Fan Tang +4
The artistic style within a painting is the means of expression, which includes not only the painting material, colors, and brushstrokes, but also the high-level attributes includi…
DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
Nisha Huang, Yuxin Zhang, Fan Tang +5
Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into a st…
Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models
Chengcheng Ma, Yang Liu, Jiankang Deng +3
Pretrained vision-language models (VLMs) such as CLIP have shown impressive generalization capability in downstream vision tasks with appropriate text prompts. Instead of designing…
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion
Nisha Huang, Fan Tang, Weiming Dong +1
Digital art synthesis is receiving increasing attention in the multimedia community because of engaging the public with art effectively. Current digital art synthesis methods usual…
Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning
Yuxin Zhang, Fan Tang, Weiming Dong +4
In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a…
Learning Muti-expert Distribution Calibration for Long-tailed Video Classification
Yufan Hu, Junyu Gao, Changsheng Xu
Most existing state-of-the-art video classification methods assume that the training data obey a uniform distribution. However, video data in the real world typically exhibit an im…
Learning Commonsense-aware Moment-Text Alignment for Fast Video Temporal Grounding
Ziyue Wu, Junyu Gao, Shucheng Huang +1
Grounding temporal video segments described in natural language queries effectively and efficiently is a crucial capability needed in vision-and-language fields. In this paper, we…
MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
Jun Hu, Bryan Hooi, Shengsheng Qian +2
Graph Neural Networks (GNNs) have recently been utilized to build Collaborative Filtering (CF) models to predict user preferences based on historical user-item interactions. Howeve…
Fine-grained Temporal Contrastive Learning for Weakly-supervised Temporal Action Localization
Junyu Gao, Mengyuan Chen, Changsheng Xu
We target at the task of weakly-supervised action localization (WSAL), where only video-level action labels are available during model training. Despite the recent progress, existi…
CrossRectify: Leveraging Disagreement for Semi-supervised Object Detection
Chengcheng Ma, Xingjia Pan, Qixiang Ye +3
Semi-supervised object detection has recently achieved substantial progress. As a mainstream solution, the self-labeling-based methods train the detector on both labeled data and u…