We propose an encoder-decoder for open-vocabulary semantic segmentation comprising a hierarchical encoder-based cost map generation and a gradual fusion decoder. We introduce a category early ...

Abstract: Encoder-decoder models have been widely used in image captioning, and most of them are designed via single long short term memory (LSTM). The capacity of single-layer network, whose encoder ...

Google has launched T5Gemma, a new collection of encoder-decoder large language models (LLMs) that promise improved quality and inference efficiency compared to their decoder-only counterparts. It is ...

Abstract: Encoder-decoder networks have become the standard solution for a variety of segmentation tasks. Many of these approaches use a symmetrical design where both the encoder as well as the ...

Large language models (LLMs) have changed the game for machine translation (MT). LLMs vary in architecture, ranging from decoder-only designs to encoder-decoder frameworks. Encoder-decoder models, ...

CNNs are specialized deep neural networks for processing data with a grid-like topology, such as images. A CNN automatically detects the important features without any human supervision. They are ...

What Is An Encoder-Decoder Architecture? An encoder-decoder architecture is a powerful tool used in machine learning, specifically for tasks involving sequences like text or speech. It’s like a ...

VVenC and VVdeC are open-source software H.266/VCC video encoder and decoder respectively that are optimized to use SIMD instructions on x86 (SSE42/SIMDe and AVX2) and Arm, and the decoder runs on ...