6 accepted papers
Omnidirectional image super-resolution (ODISR) remains challenging due to extreme magnification factors (e.g., 8x, 16x) and projection-specific distortions, which degrade edge integrity and limit model performance. This paper proposes an edge-focused framework combined with spherical geometric augme
Tool-augmented language model agents have shown great potential in solving complex real-world tasks, but a key challenge remains balancing planning flexibility with the reliability required for production deployment. Existing approaches either execute tools sequentially without parallelism or genera…
Centralized multimodal learning commonly compresses language, acoustic, and visual signals into a single fused representation for prediction. While effective, this paradigm suffers from two limitations: modality dominance, where optimization gravitates towards the path of least resistance, ignoring …
Retrieval-Augmented Generation (RAG) is a state-of-the-art technique that mitigates issues such as hallucinations and knowledge staleness in Large Language Models (LLMs) by retrieving relevant knowledge from an external database to assist in content generation. Existing research has demonstrated pot…
Reverberation time, T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">60</inf> , directly influences the amount of reverberation in a signal, and its direct estimation may help with dereverberation. Traditionally, T <inf xmlns:mml="http://www.w3.org/199…
Dereverberation is often performed in the time-frequency domain using mostly deep learning approaches. Time-frequency domain processing, however, may not be necessary when reverberation is modeled by the convolution operation. In this paper, we investigate whether deverberation can be effectively pe…