6 papers
Speak While Watching: Unleashing TRUE Real-Time Video Understanding Capability of Multimodal Large Language Models
Junyan Lin, Junlong Tong, Hao Wu +4
Multimodal Large Language Models (MLLMs) have achieved strong performance across many tasks, yet most systems remain limited to offline inference, requiring complete inputs before…
The Few Govern the Many:Unveiling Few-Layer Dominance for Time Series Models
Xin Qiu, Junlong Tong, Yirong Sun +2
Large-scale models are at the forefront of time series (TS) forecasting, dominated by two paradigms: fine-tuning text-based Large Language Models (LLM4TS) and training Time Series…
: Decoding Discontinuous Cross-Modal Dynamics for Efficient Multimodal LLMs
Yingqi Fan, Anhao Zhao, Jinlan Fu +5
Multimodal Large Language Models (MLLMs) have achieved strong performance across vision-language tasks, but suffer from significant computational overhead due to the quadratic grow…
Context Guided Transformer Entropy Modeling for Video Compression
Junlong Tong, Wei Zhang, Yaohui Jin +1
Conditional entropy models effectively leverage spatio-temporal contexts to reduce video redundancy. However, incorporating temporal context often introduces additional model compl…
SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling
Anhao Zhao, Fanghua Ye, Yingqi Fan +4
Large language models (LLMs) achieve remarkable performance across tasks but incur substantial computational costs due to their deep, multi-layered architectures. Layer pruning has…
LLM as Effective Streaming Processor: Bridging Streaming-Batch Mismatches with Group Position Encoding
Junlong Tong, Jinlan Fu, Zixuan Lin +4
Large Language Models (LLMs) are primarily designed for batch processing. Existing methods for adapting LLMs to streaming rely either on expensive re-encoding or specialized archit…