225 citations · 1.4k across the 41 of their papers we have counts for
41 papers
T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback
Zehan Wang, Ke Lei, Chen Zhu +8
Text-to-audio (T2A) generation has achieved remarkable progress in generating a variety of audio outputs from language prompts. However, current state-of-the-art T2A models still s…
MimicTalk: Mimicking a personalized and expressive 3D talking face in minutes
Zhenhui Ye, Tianyun Zhong, Yi Ren +10
Talking face generation (TFG) aims to animate a target identity's face to create realistic talking videos. Personalized TFG is a variant that emphasizes the perceptual identity sim…
MulliVC: Multi-lingual Voice Conversion With Cycle Consistency
Jiawei Huang, Chen Zhang, Yi Ren +6
Voice conversion aims to modify the source speaker's voice to resemble the target speaker while preserving the original speech content. Despite notable advancements in voice conver…
Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching
Yongqi Wang, Wenxiang Guo, Rongjie Huang +5
Video-to-audio (V2A) generation aims to synthesize content-matching audio from silent video, and it remains challenging to build V2A models with high generation quality, efficiency…
Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis
Zhenhui Ye, Tianyun Zhong, Yi Ren +11
One-shot 3D talking portrait generation aims to reconstruct a 3D avatar from an unseen image, and then animate it with a reference video or audio to generate a talking portrait vid…
Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data
Zhichao Chen, Leilei Ding, Zhixuan Chu +3
Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios. However, existing time-series forecasting meth…
Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation
Jiawei Huang, Yi Ren, Rongjie Huang +7
Large diffusion models have been successful in text-to-audio (T2A) synthesis tasks, but they often suffer from common issues such as semantic misalignment and poor temporal consist…
GeneFace++: Generalized and Stable Real-Time Audio-Driven 3D Talking Face Generation
Zhenhui Ye, Jinzheng He, Ziyue Jiang +7
Generating talking person portraits with arbitrary speech audio is a crucial problem in the field of digital human and metaverse. A modern talking face generation method is expecte…
AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head
Rongjie Huang, Mingze Li, Dongchao Yang +10
Large language models (LLMs) have exhibited remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. Despite the rece…
Structure-aware registration network for liver DCE-CT images
Peng Xue, Jingyang Zhang, Lei Ma +7
Image registration of liver dynamic contrast-enhanced computed tomography (DCE-CT) is crucial for diagnosis and image-guided surgical planning of liver cancer. However, intensity v…
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities
Zhixuan Chu, Jianmin Huang, Ruopeng Li +2
Causal inference has numerous real-world applications in many domains, such as health care, marketing, political science, and online advertising. Treatment effect estimation, a fun…
Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models
Rongjie Huang, Jiawei Huang, Dongchao Yang +7
Large-scale multimodal generative modeling has created milestones in text-to-image and text-to-video generation. Its application to audio still lags behind for two main reasons: th…
ESCM: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation
Hao Wang, Tai-Wei Chang, Tianqiao Liu +5
Accurate estimation of post-click conversion rate is critical for building recommender systems, which has long been confronted with sample selection bias and data sparsity issues.…
Instance-aware Image Colorization
Jheng-Wei Su, Hung-Kuo Chu, Jia-Bin Huang
Image colorization is inherently an ill-posed problem with multi-modal uncertainty. Previous methods leverage the deep neural network to map input grayscale images to plausible col…
Deep Semantic Matching with Foreground Detection and Cycle-Consistency
Yun-Chun Chen, Po-Hsiang Huang, Li-Yu Yu +3
Establishing dense semantic correspondences between object instances remains a challenging problem due to background clutter, significant scale and pose differences, and large intr…
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency
Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang +1
Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one domain to another (e.g., synthetic to real images). The adapted representations often do no…
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang +1
Few-shot classification aims to recognize novel categories with only few labeled images in each class. Existing metric-based few-shot classification algorithms predict categories b…
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition
Jinwoo Choi, Chen Gao, Joseph C. E. Messou +1
Human activities often occur in specific scene contexts, e.g., playing basketball on a basketball court. Training a model using existing video datasets thus inevitably captures and…
Guided Image-to-Image Translation with Bi-Directional Feature Transformation
Badour AlBahar, Jia-Bin Huang
We address the problem of guided image-to-image translation where we translate an input image into another while respecting the constraints provided by an external, user-provided g…
Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification
Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang +1
Recent advances in semi-supervised learning methods rely on estimating the categories of unlabeled data using a model trained on the labeled data (pseudo-labeling) and using the un…
Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-segmentation
Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang +1
We present an approach for jointly matching and segmenting object instances of the same category within a collection of images. In contrast to existing algorithms that tackle the t…
DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
Hsin-Ying Lee, Hung-Yu Tseng, Qi Mao +4
Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for this task: 1) lack of aligned training pairs and 2) multiple poss…
A Closer Look at Few-shot Classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira +2
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing…
Deep Paper Gestalt
Jia-Bin Huang
Recent years have witnessed a significant increase in the number of paper submissions to computer vision conferences. The sheer volume of paper submissions and the insufficient num…