Boxi Wu

Orcid: 0009-0007-0641-6065

According to our database1, Boxi Wu authored at least 34 papers between 1999 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
CrossFormer++: A Versatile Vision Transformer Hinging on Cross-Scale Attention.
IEEE Trans. Pattern Anal. Mach. Intell., May, 2024

Temporal Feature Fusion for 3D Detection in Monocular Video.
IEEE Trans. Image Process., 2024

Searching Priors Makes Text-to-Video Synthesis Better.
CoRR, 2024

Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection.
CoRR, 2024

Object Detectors in the Open Environment: Challenges, Solutions, and Outlook.
CoRR, 2024

LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models.
CoRR, 2024

UniHDA: Towards Universal Hybrid Domain Adaptation of Image Generators.
CoRR, 2024

A typology of artificial intelligence data work.
Big Data Soc., 2024

Towards Fine-Grained HBOE with Rendered Orientation Set and Laplace Smoothing.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Local Conditional Controlling for Text-to-Image Diffusion Models.
CoRR, 2023

Smooth Video Synthesis with Noise Constraints on Diffusion Models for One-shot Video Tuning.
CoRR, 2023

NormKD: Normalized Logits for Knowledge Distillation.
CoRR, 2023

Learning Occupancy for Monocular 3D Object Detection.
CoRR, 2023

APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding.
CoRR, 2023

One-shot Implicit Animatable Avatars with Model-based Priors.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Representation in AI Evaluations.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

GD-MAE: Generative Decoder for MAE Pre-Training on LiDAR Point Clouds.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Exploring the Relationship Between Architectural Design and Adversarially Robust Generalization.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Towards In-Distribution Compatible Out-of-Distribution Detection.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Improving alignment of dialogue agents via targeted human judgements.
CoRR, 2022

Towards In-distribution Compatibility in Out-of-distribution Detection.
CoRR, 2022

WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Towards Efficient Adversarial Training on Vision Transformers.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Attacking Adversarial Attacks as A Defense.
CoRR, 2021

Do Wider Neural Networks Really Help Adversarial Robustness?
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Accelerate Your CNN from Three Dimensions: A Comprehensive Pruning Framework.
CoRR, 2020

Does Network Width Really Help Adversarial Robustness?
CoRR, 2020

2019
Correlation Maximized Structural Similarity Loss for Semantic Segmentation.
CoRR, 2019

Improving Semantic Segmentation via Dilated Affinity.
CoRR, 2019

2006
TCP Performance Improvement through Inter-layer Enhancement with Mobile IPv6.
Proceedings of the Global Telecommunications Conference, 2006. GLOBECOM '06, San Francisco, CA, USA, 27 November, 2006

2003
Mobile IPv6 in WLAN mobile networks and its implementation.
Proceedings of the IEEE 14th International Symposium on Personal, 2003

1999
The animal tests of chaotic signal therapy for epilepsy (CSTE).
Proceedings of the International Joint Conference Neural Networks, 1999

Pattern grouping strategy makes BP algorithm less sensitive to learning rate.
Proceedings of the International Joint Conference Neural Networks, 1999


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