Search Results for author: Qiankun Liu

Found 11 papers, 4 papers with code

Transformer based Pluralistic Image Completion with Reduced Information Loss

1 code implementation31 Mar 2024 Qiankun Liu, Yuqi Jiang, Zhentao Tan, Dongdong Chen, Ying Fu, Qi Chu, Gang Hua, Nenghai Yu

The indices of quantized pixels are used as tokens for the inputs and prediction targets of the transformer.

Image Inpainting Quantization

Infrared Small Target Detection with Scale and Location Sensitivity

1 code implementation28 Mar 2024 Qiankun Liu, Rui Liu, Bolun Zheng, Hongkui Wang, Ying Fu

In this paper, we focus on boosting detection performance with a more effective loss but a simpler model structure.

Towards More Unified In-context Visual Understanding

no code implementations5 Dec 2023 Dianmo Sheng, Dongdong Chen, Zhentao Tan, Qiankun Liu, Qi Chu, Jianmin Bao, Tao Gong, Bin Liu, Shengwei Xu, Nenghai Yu

Thanks to this design, the model is capable of handling in-context vision understanding tasks with multimodal output in a unified pipeline. Experimental results demonstrate that our model achieves competitive performance compared with specialized models and previous ICL baselines.

Image Captioning In-Context Learning +1

Siamese-DETR for Generic Multi-Object Tracking

no code implementations27 Oct 2023 Qiankun Liu, Yichen Li, Yuqi Jiang, Ying Fu

Recently, Open-Vocabulary MOT (OVMOT) and Generic MOT (GMOT) are proposed to track interested objects beyond pre-defined categories with the given text prompt and template image.

Autonomous Driving Language Modelling +3

Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal

no code implementations15 Jun 2023 Zhentao Tan, Yue Wu, Qiankun Liu, Qi Chu, Le Lu, Jieping Ye, Nenghai Yu

Inspired by the various successful applications of large-scale pre-trained models (e. g, CLIP), in this paper, we explore the potential benefits of them for this task through both spatial feature representation learning and semantic information embedding aspects: 1) for spatial feature representation learning, we design a Spatially-Adaptive Residual (\textbf{SAR}) Encoder to extract degraded areas adaptively.

Image Restoration Representation Learning

HQ-50K: A Large-scale, High-quality Dataset for Image Restoration

1 code implementation8 Jun 2023 Qinhong Yang, Dongdong Chen, Zhentao Tan, Qiankun Liu, Qi Chu, Jianmin Bao, Lu Yuan, Gang Hua, Nenghai Yu

This paper introduces a new large-scale image restoration dataset, called HQ-50K, which contains 50, 000 high-quality images with rich texture details and semantic diversity.

Denoising Image Restoration +2

Real-time Online Multi-Object Tracking in Compressed Domain

no code implementations5 Apr 2022 Qiankun Liu, Bin Liu, Yue Wu, Weihai Li, Nenghai Yu

Recent online Multi-Object Tracking (MOT) methods have achieved desirable tracking performance.

Multi-Object Tracking Object +1

Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation

no code implementations4 Jan 2022 Qiankun Liu, Dongdong Chen, Qi Chu, Lu Yuan, Bin Liu, Lei Zhang, Nenghai Yu

In addition, such practice of re-identification still can not track those highly occluded objects when they are missed by the detector.

Ranked #7 on Multi-Object Tracking on MOT16 (using extra training data)

Multi-Object Tracking Object +2

Joint Face Image Restoration and Frontalization for Recognition

no code implementations12 May 2021 Xiaoguang Tu, Jian Zhao, Qiankun Liu, Wenjie Ai, Guodong Guo, Zhifeng Li, Wei Liu, Jiashi Feng

First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart.

Face Recognition Image Restoration

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