Trending Research
Segment Anything in High Quality
HQ-SAM is only trained on the introduced detaset of 44k masks, which takes only 4 hours on 8 GPUs.
DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement
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
CodeTF: One-stop Transformer Library for State-of-the-art Code LLM
In this paper, we present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence.
ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models
Augmented Language Models (ALMs) blend the reasoning capabilities of Large Language Models (LLMs) with tools that allow for knowledge retrieval and action execution.
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
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.
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities.
Fine-Tuning Language Models with Just Forward Passes
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.
Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks
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.
A Literature Study of Embeddings on Source Code
boyter/cs
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In this survey, we aim to collect and discuss the usage of word embedding techniques on programs and source code.
Matting Anything
In this paper, we propose the Matting Anything Model (MAM), an efficient and versatile framework for estimating the alpha matte of any instance in an image with flexible and interactive visual or linguistic user prompt guidance.
