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20232026
most citedAugmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation

4 citations · 4 across the 5 of their papers we have counts for

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cs.CV2026

The Perceptual Bandwidth Bottleneck in Vision-Language Models: Active Visual Reasoning via Sequential Experimental Design

Anjie Liu, Ziqin Gong, Yan Song +6

Visual perception in modern Vision-Language Models (VLMs) is constrained by a perceptual bandwidth bottleneck: a broad field of view preserves global context but sacrifices the fin…

cs.CV2026

Chain-of-Models Pre-Training: Rethinking Training Acceleration of Vision Foundation Models

Jiawei Fan, Shigeng Wang, Chao Li +2

In this paper, we present Chain-of-Models Pre-Training (CoM-PT), a novel performance-lossless training acceleration method for vision foundation models (VFMs). This approach fundam…

cs.CV2024

ScaleKD: Strong Vision Transformers Could Be Excellent Teachers

Jiawei Fan, Chao Li, Xiaolong Liu +1

In this paper, we question if well pre-trained vision transformer (ViT) models could be used as teachers that exhibit scalable properties to advance cross architecture knowledge di…

cs.CV2024

JoyType: A Robust Design for Multilingual Visual Text Creation

Chao Li, Chen Jiang, Xiaolong Liu +2

Generating images with accurately represented text, especially in non-Latin languages, poses a significant challenge for diffusion models. Existing approaches, such as the integrat…

cs.CV2023★ 4 cited

Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation

Jiawei Fan, Chao Li, Xiaolong Liu +2

In recent years, knowledge distillation methods based on contrastive learning have achieved promising results on image classification and object detection tasks. However, in this l…