4 accepted papers
Recent Large Vision-Language Models (LVLMs) have shown promising reasoning capabilities on text-rich images from charts, tables, and documents. However, the abundant text within such images may increase the model's sensitivity to language. This raises the need to evaluate LVLM performance on cross-…
Graphical User Interface (GUI) agents are expected to precisely operate on the screens of digital devices. Existing GUI agents merely depend on current visual observations and plain-text action history, ignoring the significance of history screens. To mitigate this issue, we propose **UI-Hawk**, a m
Performance degradation caused by noise has been a long-standing challenge for speaker verification. Previous methods usually involve applying a denoising transformation to speaker embeddings or enhancing input features. Nevertheless, these methods are lossy and inefficient for speaker embedding. In…
End-to-end speaker embedding systems have shown promising performance on speaker verification tasks. Traditional end-to-end systems typically adopt softmax loss as training criterion, which is not strong enough for training discriminative models. In this paper, we adapt the additive margin softmax (…