3 citations · 5 across the 14 of their papers we have counts for
14 papers · 1 filter
UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing
Yung-Hsuan Lai, Janek Ebbers, Yu-Chiang Frank Wang +3
Audio-Visual Video Parsing (AVVP) entails the challenging task of localizing both uni-modal events (i.e., those occurring exclusively in either the visual or acoustic modality of a…
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models
Chung-Ting Tsai, Ching-Yun Ko, I-Hsin Chung +2
The rapid advancement of generative models has introduced serious risks, including deepfake techniques for facial synthesis and editing. Traditional approaches rely on training cla…
TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators
Yuan-Chia Cheng, Zu-Yun Shiau, Fu-En Yang +1
To understand how deep neural networks perform classification predictions, recent research attention has been focusing on developing techniques to offer desirable explanations. How…
Target-Free Text-guided Image Manipulation
Wan-Cyuan Fan, Cheng-Fu Yang, Chiao-An Yang +1
We tackle the problem of target-free text-guided image manipulation, which requires one to modify the input reference image based on the given text instruction, while no ground tru…
Paraphrasing Is All You Need for Novel Object Captioning
Cheng-Fu Yang, Yao-Hung Hubert Tsai, Wan-Cyuan Fan +3
Novel object captioning (NOC) aims to describe images containing objects without observing their ground truth captions during training. Due to the absence of caption annotation, ca…
Self-Supervised Pyramid Representation Learning for Multi-Label Visual Analysis and Beyond
Cheng-Yen Hsieh, Chih-Jung Chang, Fu-En Yang +1
While self-supervised learning has been shown to benefit a number of vision tasks, existing techniques mainly focus on image-level manipulation, which may not generalize well to do…
Learning Facial Liveness Representation for Domain Generalized Face Anti-spoofing
Zih-Ching Chen, Lin-Hsi Tsao, Chin-Lun Fu +2
Face anti-spoofing (FAS) aims at distinguishing face spoof attacks from the authentic ones, which is typically approached by learning proper models for performing the associated cl…
Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis
Wan-Cyuan Fan, Yen-Chun Chen, Dongdong Chen +3
Diffusion models (DMs) have shown great potential for high-quality image synthesis. However, when it comes to producing images with complex scenes, how to properly describe both im…
Scene Graph Expansion for Semantics-Guided Image Outpainting
Chiao-An Yang, Cheng-Yo Tan, Wan-Cyuan Fan +3
In this paper, we address the task of semantics-guided image outpainting, which is to complete an image by generating semantically practical content. Different from most existing i…
Domain-Generalized Textured Surface Anomaly Detection
Shang-Fu Chen, Yu-Min Liu, Chia-Ching Lin +2
Anomaly detection aims to identify abnormal data that deviates from the normal ones, while typically requiring a sufficient amount of normal data to train the model for performing…
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains
Yuan-Chia Cheng, Ci-Siang Lin, Fu-En Yang +1
Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest. Though several approaches have been proposed, most existing…
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation
Zu-Yun Shiau, Wei-Wei Lin, Ci-Siang Lin +1
How to handle domain shifts when recognizing or segmenting visual data across domains has been studied by learning and vision communities. In this paper, we address domain generali…
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation
Yuan-Hao Lee, Fu-En Yang, Yu-Chiang Frank Wang
Few-shot semantic segmentation addresses the learning task in which only few images with ground truth pixel-level labels are available for the novel classes of interest. One is typ…
Representation Decomposition for Image Manipulation and Beyond
Shang-Fu Chen, Jia-Wei Yan, Ya-Fan Su +1
Representation disentanglement aims at learning interpretable features, so that the output can be recovered or manipulated accordingly. While existing works like infoGAN and AC-GAN…