9 accepted papers
Dataset Distillation (DD) aims to compress large-scale datasets into a small number of condensed Images Per Class (IPC), enabling efficient network training. Previous coreset selection and synthetic-based DD methods achieve reasonable performance. However, our in-depth investigation reveals that exi
Order learning has been proven to improve the generalization of facial beauty prediction (FBP) models. However, the scope for advancement remains due to the constraints of current FBP datasets and model scales. In this study, we propose a Large Multimodal Model based Order Learning (LMOL), pioneerin…
Learning fine-grained representations from coarse labels for fine-grained visual recognition (FGVR) is a challenging yet valuable task, as it alleviates the reliance on labor-intensive fine-grained annotations. Early approaches focused primarily on minimizing intra-fine-grained-class variation but o…
Lipreading in low-resource languages is a highly demanded yet extremely challenging task. Recent research has shown that transferring the lipreading knowledge from data-rich languages to low-resource ones can improve the performance. However, unique lip movements in low-resource languages and hetero…
The issue of multimodal imbalance has attracted widespread attention recently, and spurred the proposal of numerous dataset-level modulation strategies. However, we observe that the degree of modality imbalance may vary significantly across different samples, suggesting that dataset-level strategies…
Multimodal learning has demonstrated a great advantage in emotion recognition tasks due to the richer information from different modalities. However, multimodal models may incline to rely on some modalities that are easier to be learned, while under-fit the other modalities and lead to sub-optimal r…
Speech emotion recognition is a challenging task due to the ambiguity of emotion, which makes it difficult to learn the features of emotion data using machine learning algorithms. However, previous studies conventionally ignore the ambiguity of emotion and treat the emotion data as the same difficul…
Image patch matching across different spectral domains is more challenging than in a single spectral domain. We consider the reason is twofold: 1. the weaker discriminative feature learned by conventional methods; 2. the significant appearance difference between two images domains. To tackle these p…
Recent studies on image patch matching are paying more attention on hard sample learning, because easy samples do not contribute much to the network optimization. They have proposed various hard negative sample mining strategies, but very few addressed this problem from the perspective of loss funct…