Search Results for author:
Found 5 papers, 2 papers with code
Mipha: A Comprehensive Overhaul of Multimodal Assistant with Small Language Models
Multimodal Large Language Models (MLLMs) have showcased impressive skills in tasks related to visual understanding and reasoning.
Ranked #64 on
Visual Question Answering
on MM-Vet
LLaVA-Phi: Efficient Multi-Modal Assistant with Small Language Model
In this paper, we introduce LLaVA-$\phi$ (LLaVA-Phi), an efficient multi-modal assistant that harnesses the power of the recently advanced small language model, Phi-2, to facilitate multi-modal dialogues.
Ranked #77 on
Visual Question Answering
on MM-Vet
ScaleKD: Distilling Scale-Aware Knowledge in Small Object Detector
Unlike existing works that struggle to balance the trade-off between inference speed and SOD performance, in this paper, we propose a novel Scale-aware Knowledge Distillation (ScaleKD), which transfers knowledge of a complex teacher model to a compact student model.
Make A Long Image Short: Adaptive Token Length for Vision Transformers
The TLA enables the ReViT to process the image with the minimum sufficient number of tokens during inference.
Training BatchNorm Only in Neural Architecture Search and Beyond
Critically, there is no effort to understand 1) why training BatchNorm only can find the perform-well architectures with the reduced supernet-training time, and 2) what is the difference between the train-BN-only supernet and the standard-train supernet.
