Speech processing focuses on developing systems to analyze, interpret, and generate human speech. These technologies encompass a range of applications, such as automatic speech recognition (ASR), speaker verification, speech-to-text translation, and speaker diarization. With the growing reliance on virtual assistants, transcription services, and multilingual communication tools, efficient and accurate speech processing has become essential. Researchers… →

Fish Audio has officially launched Fish Speech 1.4, an advanced iteration of its powerful text-to-speech (TTS) model. With the release, Fish Audio aims to democratize cutting-edge voice technology by making it more accessible to developers, researchers, and businesses worldwide. The latest version of Fish Speech significantly enhances its predecessor by expanding the training data, adding… →

Recent advancements in sparse-view 3D reconstruction have focused on novel view synthesis and scene representation techniques. Methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have shown significant success in accurately reconstructing complex real-world scenes. Researchers have proposed various enhancements to improve performance, speed, and quality. Sparse view scene reconstruction techniques employ regularization… →

Classical randomness has emerged as an important tool in addressing the challenge of designing quantum protocols and algorithms. Current methods for calibrating and evaluating quantum gates, like randomized benchmarking, depend heavily on classical randomness. Many researchers are exploring ways to incorporate classical randomness to reduce the requirements of traditional quantum algorithms due to the progress… →

Constructing Knowledge Graphs (KGs) from unstructured data is a complex task due to the difficulties of extracting and structuring meaningful information from raw text. Unstructured data often contains unresolved or duplicated entities and inconsistent relationships, which complicates its transformation into a coherent knowledge graph. Additionally, the vast amount of unstructured data available across various fields… →

The strong generalization abilities of large-scale vision foundation models have contributed to their amazing performance in various computer vision tasks. These models are quite adaptable since they can handle a number of jobs without requiring a lot of task-specific training. Two-view correspondence, the act of matching points or features in one image with corresponding points… →

Artificial intelligence (AI) has advanced rapidly, especially in multi-modal large language models (MLLMs), which integrate visual and textual data for diverse applications. These models are increasingly applied in video analysis, high-resolution image processing, and multi-modal agents. Their capacity to process and understand vast amounts of information from different sources is essential for applications in healthcare,… →

Supervised learning in medical image classification faces challenges due to the scarcity of labeled data, as expert annotations are difficult to obtain. Vision-Language Models (VLMs) address this issue by leveraging visual-text alignment, allowing unsupervised learning, and reducing reliance on labeled data. Pre-training on large medical image-text datasets enables VLMs to generate accurate labels and captions,… →

Graph-based methods have become increasingly important in data retrieval and machine learning, particularly in nearest neighbor (NN) search. NN search helps identify data points closest to a given query, which becomes critical with high-dimensional data such as text, images, or audio. Approximate nearest neighbor (ANN) methods emerged due to the inefficiency of exact searches in… →

The release of Reader-LM-0.5B and Reader-LM-1.5B by Jina AI marks a significant milestone in small language model (SLM) technology. These models are designed to solve a unique and specific challenge: converting raw, noisy HTML from the open web into clean markdown format. While seemingly straightforward, this task poses complex challenges, particularly in handling the vast… →