Search Results for author: Chun-Hao Liu

Found 8 papers, 2 papers with code

PaPr: Training-Free One-Step Patch Pruning with Lightweight ConvNets for Faster Inference

no code implementations24 Mar 2024 Tanvir Mahmud, Burhaneddin Yaman, Chun-Hao Liu, Diana Marculescu

Using this insight, we introduce PaPr, a method for substantially pruning redundant patches with minimal accuracy loss using lightweight ConvNets across a variety of deep learning architectures, including ViTs, ConvNets, and hybrid transformers, without any re-training.

Detecting Multimedia Generated by Large AI Models: A Survey

1 code implementation22 Jan 2024 Li Lin, Neeraj Gupta, Yue Zhang, Hainan Ren, Chun-Hao Liu, Feng Ding, Xin Wang, Xin Li, Luisa Verdoliva, Shu Hu

The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life.

Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection

no code implementations14 May 2023 Burhaneddin Yaman, Tanvir Mahmud, Chun-Hao Liu

We propose an embarrassingly simple method -- instance-aware repeat factor sampling (IRFS) to address the problem of imbalanced data in long-tailed object detection.

Long-tailed Object Detection Object +2

Object Detection for Autonomous Dozers

no code implementations17 Aug 2022 Chun-Hao Liu, Burhaneddin Yaman

We introduce a new type of autonomous vehicle - an autonomous dozer that is expected to complete construction site tasks in an efficient, robust, and safe manner.

Object object-detection +1

Complement Objective Training

1 code implementation ICLR 2019 Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes.

Natural Language Understanding

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