HQ-SAM is only trained on the introduced detaset of 44k masks, which takes only 4 hours on 8 GPUs.
In this paper, we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence.
Augmented Language Models (ALMs) blend the reasoning capabilities of Large Language Models (LLMs) with tools that allow for knowledge retrieval and action execution.
Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities.
Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal.
Ranked #8 on Speech Enhancement on VoiceBank + DEMAND
In addition, to facilitate a comprehensive evaluation of video-language models, we carefully build the largest human-annotated Chinese benchmarks covering three popular video-language tasks of cross-modal retrieval, video captioning, and video category classification.
For the second challenge, we leverage ImageBind, a universal embedding model aligning multiple modalities as the pre-trained audio encoder, and introduce an Audio Q-former on top of ImageBind to learn reasonable auditory query embeddings for the LLM module.
In this survey, we aim to collect and discuss the usage of word embedding techniques on programs and source code.
Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but as LMs grow in size, backpropagation requires a prohibitively large amount of memory.
Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis.