14 citations · 51 across the 18 of their papers we have counts for
11 papers · 1 filter
TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance
Kan Wu, Houwen Peng, Zhenghong Zhou +10
In this paper, we propose a novel cross-modal distillation method, called TinyCLIP, for large-scale language-image pre-trained models. The method introduces two core techniques: af…
Exploring Non-additive Randomness on ViT against Query-Based Black-Box Attacks
Jindong Gu, Fangyun Wei, Philip Torr +1
Deep Neural Networks can be easily fooled by small and imperceptible perturbations. The query-based black-box attack (QBBA) is able to create the perturbations using model output p…
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
Zigang Geng, Binxin Yang, Tiankai Hang +8
We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions. Unlike existing approaches that integrate prior knowledge…
PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning
Mengya Han, Heliang Zheng, Chaoyue Wang +4
In this work, we address the task of few-shot part segmentation, which aims to segment the different parts of an unseen object using very few labeled examples. It is found that lev…
ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation
Yasheng Sun, Yifan Yang, Houwen Peng +5
While language-guided image manipulation has made remarkable progress, the challenge of how to instruct the manipulation process faithfully reflecting human intentions persists. An…
VanillaKD: Revisit the Power of Vanilla Knowledge Distillation from Small Scale to Large Scale
Zhiwei Hao, Jianyuan Guo, Kai Han +3
The tremendous success of large models trained on extensive datasets demonstrates that scale is a key ingredient in achieving superior results. Therefore, the reflection on the rat…