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Wen-Huang Cheng

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7 papers

2 author rows

AAAI Conference 2025 Conference Paper

Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRec

Yu-Hsuan Huang
Ling Lo
Hongxia Xie
Hong-Han Shuai
Wen-Huang Cheng

Sequential recommendation (SR) systems predict user preferences by analyzing time-ordered interaction sequences. A common challenge for SR is data sparsity, as users typically interact with only a limited number of items. While contrastive learning has been employed in previous approaches to address the challenges, these methods often adopt binary labels, missing finer patterns and overlooking detailed information in subsequent behaviors of users. Additionally, they rely on random sampling to select negatives in contrastive learning, which may not yield sufficiently hard negatives during later training stages. In this paper, we propose Future data utilization with Enduring Negatives for contrastive learning in sequential Recommendation (FENRec). Our approach aims to leverage future data with time-dependent soft labels and generate enduring hard negatives from existing data, thereby enhancing the effectiveness in tackling data sparsity. Experiment results demonstrate our state-of-the-art performance across four benchmark datasets, with an average improvement of 6.16% across all metrics.

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AAAI Conference 2025 Conference Paper

Memory-Augmented Re-Completion for 3D Semantic Scene Completion

Yu-Wen Tseng
Sheng-Ping Yang
Jhih-Ciang Wu
I-Bin Liao
Yung-Hui Li
Hong-Han Shuai
Wen-Huang Cheng

Semantic Scene Completion (SSC) aims to reconstruct a 3D voxel representation occupied by semantic classes based on ordinary inputs such as 2D RGB images, depth maps, or point clouds. Given the cost-effective and promising applications in autonomous driving, camera-based SSC has attracted considerable attention to developing various approaches. However, current methods mainly focus on precise 2D-to-3D projection while overlooking the challenge of completing invisible regions, leading to numerous false negatives and suboptimal SSC performance. To address this issue, we propose a novel architecture, Memory-augmented Re-completion (MARE), designed to enhance completion capability. Our MARE model encapsulates regional relationships by incorporating a memory bank that stores vital region-tokens while two protocols concerning diversity and age are adopted to optimize the bank adversarially. Additionally, we introduce a Re-completion pipeline incorporated with an Information Spreading module to progressively complete the invisible regions while bridging the scale gap between region-level and voxel-level information. Extensive experiments conducted on the SSCBench-KITTI-360 and SemanticKITTI datasets validate the effectiveness of our approach.

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AAAI Conference 2025 Conference Paper

Personalized Lip Reading: Adapting to Your Unique Lip Movements with Vision and Language

Jeong Hun Yeo
Chae Won Kim
Hyunjun Kim
Hyeongseop Rha
Seunghee Han
Wen-Huang Cheng
Yong Man Ro

Lip reading aims to predict spoken language by analyzing lip movements. Despite advancements in lip reading technologies, performance degrades when models are applied to unseen speakers due to their sensitivity to variations in visual information such as lip appearances. To address this challenge, speaker adaptive lip reading technologies have advanced by focusing on effectively adapting a lip reading model to target speakers in the visual modality. However, the effectiveness of adapting language information, such as vocabulary choice, of the target speaker has not been explored in previous works. Additionally, existing datasets for speaker adaptation have limited vocabulary sizes and pose variations, which restrict the validation of previous speaker-adaptive methods in real-world scenarios. To address these issues, we propose a novel speaker-adaptive lip reading method that adapts a pre-trained model to target speakers at both vision and language levels. Specifically, we integrate prompt tuning and the LoRA approach, applying them to a pre-trained lip reading model to effectively adapt the model to target speakers. Furthermore, to validate its effectiveness in real-world scenarios, we introduce a new dataset, VoxLRS-SA, derived from VoxCeleb2 and LRS3. It contains a vocabulary of approximately 100K words, offers diverse pose variations, and enables the validation of adaptation methods in the wild, sentence-level lip reading for the first time in English. Through various experiments, we demonstrate that the existing speaker-adaptive method also improves performance in the wild at the sentence level. Moreover, we show that the proposed method achieves larger improvements compared to the previous works.

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AAAI Conference 2023 Conference Paper

Zero-Shot Face-Based Voice Conversion: Bottleneck-Free Speech Disentanglement in the Real-World Scenario

Shao-En Weng
Hong-Han Shuai
Wen-Huang Cheng

Often a face has a voice. Appearance sometimes has a strong relationship with one's voice. In this work, we study how a face can be converted to a voice, which is a face-based voice conversion. Since there is no clean dataset that contains face and speech, voice conversion faces difficult learning and low-quality problems caused by background noise or echo. Too much redundant information for face-to-voice also causes synthesis of a general style of speech. Furthermore, previous work tried to disentangle speech with bottleneck adjustment. However, it is hard to decide on the size of the bottleneck. Therefore, we propose a bottleneck-free strategy for speech disentanglement. To avoid synthesizing the general style of speech, we utilize framewise facial embedding. It applied adversarial learning with a multi-scale discriminator for the model to achieve better quality. In addition, the self-attention module is added to focus on content-related features for in-the-wild data. Quantitative experiments show that our method outperforms previous work.

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IROS Conference 2021 Conference Paper

Re-Attention Is All You Need: Memory-Efficient Scene Text Detection via Re-Attention on Uncertain Regions

Hsiang-Chun Chang
Hung-Jen Chen 0001
Yu-Chia Shen
Hong-Han Shuai
Wen-Huang Cheng

Scene text detection plays an important role on vision-based robot navigation to many potential landmarks such as nameplates, information signs, floor button in the elevators. Recently, scene text detection with segmentation-based methods has been receiving more and more attention. The segmentation results can be used to efficiently predict scene text of various shapes, such as irregular text in most scene text images. However, two kinds of texts remain unsolved: 1) tiny and 2) blurry instances. Moreover, the annotations for tiny/blurry texts are usually ignored during training, while tiny/blurry texts can still offer visual auxiliaries for robots to understand the world. Therefore, in this paper, we propose a new approach to effectively detect both clear and blurry texts. Specifically, we propose a re-attention module without increasing the learnable parameters, which first predicts the region of texts as the candidate region and leverages the same network to detect the candidate region again for reducing the required memory. Moreover, to avoid the errors from the first detection propagating to the re-attended area, we propose a new fusion module that learns to integrate the results of the re-attended regions and the first prediction. Experimental results manifest that the proposed method outperforms state-of-the-art methods on four challenging datasets.

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IJCAI Conference 2017 Conference Paper

Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

Bo Wu
Wen-Huang Cheng
Yongdong Zhang
Qiushi Huang
Jintao Li
Tao Mei

Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i. e. , sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21. 51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).

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AAAI Conference 2016 Conference Paper

Unfolding Temporal Dynamics: Predicting Social Media Popularity Using Multi-scale Temporal Decomposition

Bo Wu
Tao Mei
Wen-Huang Cheng
Yongdong Zhang

Time information plays a crucial role on social media popularity. Existing research on popularity prediction, effective though, ignores temporal information which is highly related to user-item associations and thus often results in limited success. An essential way is to consider all these factors (user, item, and time), which capture the dynamic nature of photo popularity. In this paper, we present a novel approach to factorize the popularity into user-item context and time-sensitive context for exploring the mechanism of dynamic popularity. The user-item context provides a holistic view of popularity, while the time-sensitive context captures the temporal dynamics nature of popularity. Accordingly, we develop two kinds of time-sensitive features, including user activeness variability and photo prevalence variability. To predict photo popularity, we propose a novel framework named Multi-scale Temporal Decomposition (MTD), which decomposes the popularity matrix in latent spaces based on contextual associations. Speciﬁcally, the proposed MTD models time-sensitive context on different time scales, which is beneﬁcial to automatically learn temporal patterns. Based on the experiments conducted on a real-world dataset with 1. 29M photos from Flickr, our proposed MTD can achieve the prediction accuracy of 79. 8% and outperform the best three state-of-the-art methods with a relative improvement of 9. 6% on average.

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