Title: Linear Video Transformer with Feature Fixation. Abstract: Vision Transformers have achieved impressive performance in video classification, while suffering from the quadratic complexity caused by the Softmax attention mechanism. Some studies alleviate the computational costs by reducing the number of tokens attended in attention calculation, but the complexity is still quadratic. Another promising way is to replace Softmax attention with linear attention, which owns linear complexity but presents a clear performance drop. We find that such a drop in linear attention results from the lack of attention concentration to critical features. Therefore, we propose a feature fixation module to reweight feature importance of the query and key prior to computing linear attention. Specifically, we regard the query, key, and value as latent representations of the input token, and learn the feature fixation ratio by aggregating Query-Key-Value information. This is beneficial for measuring the feature importance comprehensively. Furthermore, we improve the feature fixation by neighborhood association, which leverages additional guidance from spatial and temporal neighboring tokens. Our proposed method significantly improves the linear attention baseline, and achieves state-of-the-art performance among linear video Transformers on three popular video classification benchmarks. Our performance is even comparable to some quadratic Transformers with fewer parameters and higher efficiency.