Search Results for author: Tiejian Luo

Found 17 papers, 4 papers with code

Local Compressed Video Stream Learning for Generic Event Boundary Detection

1 code implementation27 Sep 2023 Libo Zhang, Xin Gu, CongCong Li, Tiejian Luo, Heng Fan

Specifically, we use lightweight ConvNets to extract features of the P-frames in the GOPs and spatial-channel attention module (SCAM) is designed to refine the feature representations of the P-frames based on the compressed information with bidirectional information flow.

Boundary Detection Representation Learning

Collaborative Three-Stream Transformers for Video Captioning

no code implementations18 Sep 2023 Hao Wang, Libo Zhang, Heng Fan, Tiejian Luo

Meanwhile, we propose a cross-granularity attention module to align the interactions modeled by the three branches of transformers, then the three branches of transformers can support each other to exploit the most discriminative semantic information of different granularities for accurate predictions of captions.

Video Captioning

Unsupervised Domain Adaptive Detection with Network Stability Analysis

1 code implementation ICCV 2023 Wenzhang Zhou, Heng Fan, Tiejian Luo, Libo Zhang

In this work, drawing inspiration from the concept of stability from the control theory that a robust system requires to remain consistent both externally and internally regardless of disturbances, we propose a novel framework that achieves unsupervised domain adaptive detection through stability analysis.

Domain Adaptation

MaGIC: Multi-modality Guided Image Completion

no code implementations19 May 2023 Yongsheng Yu, Hao Wang, Tiejian Luo, Heng Fan, Libo Zhang

In this paper, we propose a novel, simple yet effective method for Multi-modal Guided Image Completion, dubbed MaGIC, which not only supports a wide range of single modality as the guidance (e. g., text, canny edge, sketch, segmentation, depth, and pose), but also adapts to arbitrarily customized combination of these modalities (i. e., arbitrary multi-modality) for image completion.

Text with Knowledge Graph Augmented Transformer for Video Captioning

no code implementations CVPR 2023 Xin Gu, Guang Chen, YuFei Wang, Libo Zhang, Tiejian Luo, Longyin Wen

Meanwhile, the internal stream is designed to exploit the multi-modality information in videos (e. g., the appearance of video frames, speech transcripts, and video captions) to ensure the quality of caption results.

Video Captioning

Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment

no code implementations1 Jan 2023 Libo Zhang, Wenzhang Zhou, Heng Fan, Tiejian Luo, Haibin Ling

To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning.

Domain Adaptation object-detection +1

Unbiased Multi-Modality Guidance for Image Inpainting

1 code implementation25 Aug 2022 Yongsheng Yu, Dawei Du, Libo Zhang, Tiejian Luo

Image inpainting is an ill-posed problem to recover missing or damaged image content based on incomplete images with masks.

Image Inpainting Semantic Segmentation

High-Fidelity Image Inpainting with GAN Inversion

no code implementations25 Aug 2022 Yongsheng Yu, Libo Zhang, Heng Fan, Tiejian Luo

Addressing this problem, in this paper, we devise a novel GAN inversion model for image inpainting, dubbed InvertFill, mainly consisting of an encoder with a pre-modulation module and a GAN generator with F&W+ latent space.

Image Inpainting Vocal Bursts Intensity Prediction

Structured Context Transformer for Generic Event Boundary Detection

no code implementations7 Jun 2022 CongCong Li, Xinyao Wang, Dexiang Hong, YuFei Wang, Libo Zhang, Tiejian Luo, Longyin Wen

To capture temporal context information of each frame, we design the structure context transformer (SC-Transformer) by re-partitioning input frame sequence.

Boundary Detection

Multi-Granularity Alignment Domain Adaptation for Object Detection

1 code implementation CVPR 2022 Wenzhang Zhou, Dawei Du, Libo Zhang, Tiejian Luo, Yanjun Wu

Domain adaptive object detection is challenging due to distinctive data distribution between source domain and target domain.

Domain Adaptation object-detection +1

Learning to Infer User Hidden States for Online Sequential Advertising

no code implementations3 Sep 2020 Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang, Wei-Nan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.

Towards Interpretable and Robust Hand Detection via Pixel-wise Prediction

no code implementations13 Jan 2020 Dan Liu, Libo Zhang, Tiejian Luo, Lili Tao, Yanjun Wu

The lack of interpretability of existing CNN-based hand detection methods makes it difficult to understand the rationale behind their predictions.

Hand Detection

SiamMan: Siamese Motion-aware Network for Visual Tracking

no code implementations11 Dec 2019 Wenzhang Zhou, Longyin Wen, Libo Zhang, Dawei Du, Tiejian Luo, Yanjun Wu

To reduce the impact of manually designed anchor boxes to adapt to different target motion patterns, we design the localization branch, which aims to coarsely localize the target to help the regression branch to generate accurate results.

General Classification regression +1

Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently

no code implementations11 Jun 2019 Dan Liu, Dawei Du, Libo Zhang, Tiejian Luo, Yanjun Wu, Feiyue Huang, Siwei Lyu

Existing hand detection methods usually follow the pipeline of multiple stages with high computation cost, i. e., feature extraction, region proposal, bounding box regression, and additional layers for rotated region detection.

Hand Detection Region Proposal

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