5 citations · 16 across the 15 of their papers we have counts for
16 papers
Dynamic self-ensemble on multi-exit residual vision transformer for small-data image classification
Stephen Ekaputra Limantoro, Yan-Kai Wang, Ching-Chun Huang +4
Vision Transformer (ViT) has garnered increased attention for image classification in recent years. However, ViT is notorious for its data-hungry problem as it achieves high-perfor…
Enhancing Robustness in Post-Processing Watermarking: An Ensemble Attack Network Using CNNs and Transformers
Tzuhsuan Huang, Cheng Yu Yeo, Tsai-Ling Huang +3
Recent studies on deep watermarking have predominantly focused on in-processing watermarking, which integrates the watermarking process into image generation. However, post-process…
RecipeGen: A Step-Aligned Multimodal Benchmark for Real-World Recipe Generation
Ruoxuan Zhang, Jidong Gao, Bin Wen +4
Creating recipe images is a key challenge in food computing, with applications in culinary education and multimodal recipe assistants. However, existing datasets lack fine-grained…
EmoArt: A Multidimensional Dataset for Emotion-Aware Artistic Generation
Cheng Zhang, Hongxia xie, Bin Wen +3
With the rapid advancement of diffusion models, text-to-image generation has achieved significant progress in image resolution, detail fidelity, and semantic alignment, particularl…
Swapped Logit Distillation via Bi-level Teacher Alignment
Stephen Ekaputra Limantoro, Jhe-Hao Lin, Chih-Yu Wang +4
Knowledge distillation (KD) compresses the network capacity by transferring knowledge from a large (teacher) network to a smaller one (student). It has been mainstream that the tea…
Single Document Image Highlight Removal via A Large-Scale Real-World Dataset and A Location-Aware Network
Lu Pan, Yu-Hsuan Huang, Hongxia Xie +4
Reflective documents often suffer from specular highlights under ambient lighting, severely hindering text readability and degrading overall visual quality. Although recent deep le…
Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation
Pei-Chi Chen, Yi Yao, Chan-Feng Hsu +4
Camouflaged image generation is emerging as a solution to data scarcity in camouflaged vision perception, offering a cost-effective alternative to data collection and labeling. Rec…
RecipeGen: A Benchmark for Real-World Recipe Image Generation
Ruoxuan Zhang, Hongxia Xie, Yi Yao +6
Recipe image generation is an important challenge in food computing, with applications from culinary education to interactive recipe platforms. However, there is currently no real-…
Perspective-Aware Teaching: Adapting Knowledge for Heterogeneous Distillation
Jhe-Hao Lin, Yi Yao, Chan-Feng Hsu +3
Knowledge distillation (KD) involves transferring knowledge from a pre-trained heavy teacher model to a lighter student model, thereby reducing the inference cost while maintaining…
Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRec
Yu-Hsuan Huang, Ling Lo, Hongxia Xie +2
Sequential recommendation (SR) systems predict user preferences by analyzing time-ordered interaction sequences. A common challenge for SR is data sparsity, as users typically inte…
ReCorD: Reasoning and Correcting Diffusion for HOI Generation
Jian-Yu Jiang-Lin, Kang-Yang Huang, Ling Lo +5
Diffusion models revolutionize image generation by leveraging natural language to guide the creation of multimedia content. Despite significant advancements in such generative mode…
The Fabrication of Reality and Fantasy: Scene Generation with LLM-Assisted Prompt Interpretation
Yi Yao, Chan-Feng Hsu, Jhe-Hao Lin +5
In spite of recent advancements in text-to-image generation, limitations persist in handling complex and imaginative prompts due to the restricted diversity and complexity of train…
A DeNoising FPN With Transformer R-CNN for Tiny Object Detection
Hou-I Liu, Yu-Wen Tseng, Kai-Cheng Chang +3
Despite notable advancements in the field of computer vision, the precise detection of tiny objects continues to pose a significant challenge, largely owing to the minuscule pixel…
EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning
Hongxia Xie, Chu-Jun Peng, Yu-Wen Tseng +4
Visual Instruction Tuning represents a novel learning paradigm involving the fine-tuning of pre-trained language models using task-specific instructions. This paradigm shows promis…
Lightweight Deep Learning for Resource-Constrained Environments: A Survey
Hou-I Liu, Marco Galindo, Hongxia Xie +4
Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and bi…
DQ-DETR: DETR with Dynamic Query for Tiny Object Detection
Yi-Xin Huang, Hou-I Liu, Hong-Han Shuai +1
Despite previous DETR-like methods having performed successfully in generic object detection, tiny object detection is still a challenging task for them since the positional inform…