Ziyi Ye

Yiweng Xie, Bo He, Junke Wang et al.

This paper presents FluxMem, a training-free framework for efficient streaming video understanding. FluxMem adaptively compresses redundant visual memory through a hierarchical, two-stage design: (1) a Temporal Adjacency Selection (TAS) module removes redundant visual tokens across adjacent frames, and (2) a Spatial Domain Consolidation (SDC) module further merges spatially repetitive regions within each frame into compact representations. To adapt effectively to dynamic scenes, we introduce a self-adaptive token compression mechanism in both TAS and SDC, which automatically determines the compression rate based on intrinsic scene statistics rather than manual tuning. Extensive experiments demonstrate that FluxMem achieves new state-of-the-art results on existing online video benchmarks, reaching 76.4 on StreamingBench and 67.2 on OVO-Bench under real-time settings, while reducing latency by 69.9% and peak GPU memory by 34.5% on OVO-Bench. Furthermore, it maintains strong offline performance, achieving 73.1 on MLVU while using 65% fewer visual tokens.

Congchi Yin, Ziyi Ye, Piji Li

Many recent studies have shown that the perception of speech can be decoded from brain signals and subsequently reconstructed as continuous language. However, there is a lack of neurological basis for how the semantic information embedded within brain signals can be used more effectively to guide language reconstruction. The theory of predictive coding suggests that human brain naturally engages in continuously predicting future word representations that span multiple timescales. This implies that the decoding of brain signals could potentially be associated with a predictable future. To explore the predictive coding theory within the context of language reconstruction, this paper proposes a novel model \textsc{PredFT} for jointly modeling neural decoding and brain prediction. It consists of a main decoding network for language reconstruction and a side network for predictive coding. The side network obtains brain predictive coding representation from related brain regions of interest with a multi-head self-attention module. This representation is fused into the main decoding network with cross-attention to facilitate the language models' generation process. Experiments are conducted on the largest naturalistic language comprehension fMRI dataset Narratives. \textsc{PredFT} achieves current state-of-the-art decoding performance with a maximum BLEU-1 score of $27.8\%$.