6 citations · 16 across the 7 of their papers we have counts for
7 papers
Improving Audio-Visual Segmentation with Bidirectional Generation
Dawei Hao, Yuxin Mao, Bowen He +3
The aim of audio-visual segmentation (AVS) is to precisely differentiate audible objects within videos down to the pixel level. Traditional approaches often tackle this challenge b…
Linearized Relative Positional Encoding
Zhen Qin, Weixuan Sun, Kaiyue Lu +6
Relative positional encoding is widely used in vanilla and linear transformers to represent positional information. However, existing encoding methods of a vanilla transformer are…
TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer
Zhen Qin, Dong Li, Weigao Sun +8
We present TransNormerLLM, the first linear attention-based Large Language Model (LLM) that outperforms conventional softmax attention-based models in terms of both accuracy and ef…
Toeplitz Neural Network for Sequence Modeling
Zhen Qin, Xiaodong Han, Weixuan Sun +6
Sequence modeling has important applications in natural language processing and computer vision. Recently, the transformer-based models have shown strong performance on various seq…
Fine-grained Audible Video Description
Xuyang Shen, Dong Li, Jinxing Zhou +9
We explore a new task for audio-visual-language modeling called fine-grained audible video description (FAVD). It aims to provide detailed textual descriptions for the given audibl…
The Devil in Linear Transformer
Zhen Qin, XiaoDong Han, Weixuan Sun +4
Linear transformers aim to reduce the quadratic space-time complexity of vanilla transformers. However, they usually suffer from degraded performances on various tasks and corpus.…
Linear Video Transformer with Feature Fixation
Kaiyue Lu, Zexiang Liu, Jianyuan Wang +8
Vision Transformers have achieved impressive performance in video classification, while suffering from the quadratic complexity caused by the Softmax attention mechanism. Some stud…