16 accepted papers
In recent years, the technology for converting character images into cartoons has gained widespread attention on short video platforms, with diffusion models particularly standing out in the field of image style transfer. However, existing methods still have shortcomings in detail handling and chara…
Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet either through semantic matching or distance scoring functions. However, it appear…
Multimodal knowledge graph embedding refers to learning multimodal entities and their relation representations in a low-dimensional space. However, existing multimodal embedding models tend to ignore the inherent structure of knowledge graphs. To address this issue, we propose a novel multimodal kno…
Adapter-based Multilingual Neural Machine Translation (MNMT) has become a significant approach in low-resource language translation by mitigating data imbalances between high-resource and low-resource language pairs and reducing training costs. However, existing adapter-based methods lack generaliza…
Multi-modal Entity Alignment (MMEA) aims to identify the same entities exhibited in different knowledge graphs (KGs), where the entities are enriched by structure and visual information. Existing MMEA methods learn multi-modal joint entity embeddings by encompassing both modality interaction and mod…
Large language models (LLMs) generate human-aligned content under certain safety constraints. However, the current known technique “jailbreak prompt” can circumvent safety-aligned measures and induce LLMs to output malicious content. Research on Jailbreaking can help identify vulnerabilities in LLMs
The zero-shot sketch-based image retrieval task faces two key challenges: domain gap and knowledge transfer. Our innovation is recognizing that directly aligning cross-domain features weakens the discriminative ability of the model, as it overlooks the asymmetry between sketches and images. Addition…
Entity alignment (EA) aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Euclidean space. However, KGs often contain complex local and hierarchical structures, which are hard to represent in…
Continuous prompt tuning has gained significant attention for its ability to train only continuous prompts while freezing the language model. This approach greatly reduces the training time and storage for downstream tasks. In this work, we delve into the hierarchical relationship between the prompt…
Linear Graph Convolutional Networks (GCNs) are used to classify the node in the graph data. However, we note that most existing linear GCN models perform neural network operations in Euclidean space, which do not explicitly capture the tree-like hierarchical structure exhibited in real-world dataset…
Non-autoregressive transformer (NAT) based speech recognition models have gained more and more attention since they perform faster inference speed compared with autoregressive counterparts, especially when the single-step decoding is applied. However, the single-step decoding process with length pre…
Sequence-to-sequence attention-based models for grapheme-to-phoneme (G2P) conversion have gained significant interests. The attention-based encoder-decoder framework learns the mapping of input to output tokens by selectively focusing on relevant information, and has been shown well performance. How…
Biomedical image segmentation has been widely studied, and lots of methods have been proposed. Among these methods, attention U-Net has achieved a promising performance. However, it has drawbacks of extracting the multi-scaled receptive field features at the high-level feature maps, resulting in the…
Mongolian morphological segmentation is regarded as a crucial preprocessing step in many Mongolian related NLP applications and has received extensive attention. Recently, end-to-end segmentation approaches with long short-term memory networks (LSTM) have achieved excellent results. However, the inn…
Currently, low signal-to-noise ratio (SNR) and non-stationary noise cause severe performance degradation for most of speech enhancement models. For better speech enhancement at the above scenarios, this paper proposes a two-stage approach that consists of binary masking and spectrogram inpainting. I…
While neural end-to-end text-to-speech (TTS) is superior to conventional statistical methods in many ways, the exposure bias problem in the autoregressive models remains an issue to be resolved. The exposure bias problem arises from the mismatch between the training and inference process, that resul…