Video understanding has long presented unique challenges for AI researchers. Unlike static images, videos involve intricate temporal dynamics and spatial-temporal reasoning, making it difficult for ...

The growth of data in the digital age presents both opportunities and challenges. An immense volume of text, images, audio, and video is generated daily across platforms. Traditional machine learning ...

When it comes to AI tools, chatbots are often the first thing that comes to mind —conversation-based interfaces for users to write queries and receive responses. These dialogue interfaces are ...

Biometric authentication has emerged as a promising solution to enhance security by offering a more robust defense against cyber threats. However, hackers can increasingly develop sophisticated ...

Generating time series data is important for many applications, including data augmentation, synthetic datasets, and scenarios. However, when there is more than one, this process becomes too complex ...

Speech processing systems often struggle to deliver clear audio in noisy environments. This challenge impacts applications such as hearing aids, automatic speech recognition (ASR), and speaker ...

Generative Large Multimodal Models (LMMs), such as LLaVA and Qwen-VL, excel in vision-language (VL) tasks like image captioning and visual question answering (VQA). However, these models face ...

The growth of data in the digital age presents both opportunities and challenges. An immense volume of text, images, audio, and video is generated daily across platforms. Traditional machine learning ...

Speech processing systems often struggle to deliver clear audio in noisy environments. This challenge impacts applications such as hearing aids, automatic speech recognition (ASR), and speaker ...

Blockchain systems face significant challenges in efficiently managing and updating state storage due to high write amplification (WA) and extensive I/O operations. In traditional architecture, such ...

Speech processing systems often struggle to deliver clear audio in noisy environments. This challenge impacts applications such as hearing aids, automatic speech recognition (ASR), and speaker ...

Developing effective multi-modal AI systems for real-world applications requires handling diverse tasks such as fine-grained recognition, visual grounding, reasoning, and multi-step problem-solving.