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Found 6 papers, 2 papers with code
E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling
Our approach not only enhances computational efficiency but also aligns naturally with image generation principles by operating in continuous token space and following a hierarchical generation process from coarse to fine details.
freePruner: A Training-free Approach for Large Multimodal Model Acceleration
Large Multimodal Models (LMMs) have demonstrated impressive capabilities in visual-language tasks but face significant deployment challenges due to their high computational demands.
Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner
In this paper, we first identify the primary challenges in interpolating Video-LLMs: (1) the video encoder and modality alignment projector are fixed, preventing the integration of additional frames into Video-LLMs, and (2) the LLM backbone is limited in its content length capabilities, which complicates the processing of an increased number of video tokens.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
In response, we propose PruMerge, a novel adaptive visual token reduction strategy that significantly reduces the number of visual tokens without compromising the performance of LMMs.
Causal-DFQ: Causality Guided Data-free Network Quantization
Inspired by the causal understanding, we propose the Causality-guided Data-free Network Quantization method, Causal-DFQ, to eliminate the reliance on data via approaching an equilibrium of causality-driven intervened distributions.
