no code implementations • 1 Sep 2024 • Yunxiao Shi, Min Xu, Haimin Zhang, Xing Zi, Qiang Wu
This paper proposes a novel AI Search Engine framework called the Agent Collaboration Network (ACN).
1 code implementation • 16 Aug 2024 • Yunxiao Shi, Wujiang Xu, Haimin Zhang, Qiang Wu, Yongfeng Zhang, Min Xu
Modeling high-order feature interactions is critical for click-through rate (CTR) prediction, yet traditional approaches often face challenges in balancing predictive accuracy and computational efficiency.
no code implementations • 16 Jul 2024 • Pierre-David Letourneau, Manish Kumar Singh, Hsin-Pai Cheng, Shizhong Han, Yunxiao Shi, Dalton Jones, Matthew Harper Langston, Hong Cai, Fatih Porikli
We present Polynomial Attention Drop-in Replacement (PADRe), a novel and unifying framework designed to replace the conventional self-attention mechanism in transformer models.
1 code implementation • 15 Jul 2024 • Yunxiao Shi, Xing Zi, Zijing Shi, Haimin Zhang, Qiang Wu, Min Xu
These four RAG modules synergistically improve the response quality and efficiency of the RAG system.
1 code implementation • 28 May 2024 • Wujiang Xu, Qitian Wu, Zujie Liang, Jiaojiao Han, Xuying Ning, Yunxiao Shi, Wenfang Lin, Yongfeng Zhang
Motivated by this insight, we empower small language models for SR, namely SLMRec, which adopt a simple yet effective knowledge distillation method.
1 code implementation • 6 May 2024 • Yunxiao Shi, Xing Zi, Zijing Shi, Haimin Zhang, Qiang Wu, Min Xu
The efficiency and personalization characteristics of ERAGent are supported by the Experiential Learner module which makes the AI assistant being capable of expanding its knowledge and modeling user profile incrementally.
no code implementations • 11 Apr 2024 • Zeng Yu, Yunxiao Shi
Importantly, in the alignment process of SAS and AAL, all the parameters are immediately optimized with optimization principles rather than training the whole network, which yields a better parameter training manner.
no code implementations • 19 Mar 2024 • Rajeev Yasarla, Manish Kumar Singh, Hong Cai, Yunxiao Shi, Jisoo Jeong, Yinhao Zhu, Shizhong Han, Risheek Garrepalli, Fatih Porikli
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training.
Ranked #3 on
Monocular Depth Estimation
on KITTI Eigen split
no code implementations • CVPR 2024 • Yunxiao Shi, Manish Kumar Singh, Hong Cai, Fatih Porikli
Leveraging the initial depths and features from this network, we uplift the 2D features to form a 3D point cloud and construct a 3D point transformer to process it, allowing the model to explicitly learn and exploit 3D geometric features.
no code implementations • 6 Mar 2024 • Li Wang, Min Xu, Quangui Zhang, Yunxiao Shi, Qiang Wu
Building upon this insight, we propose a disentangled encoder that focuses on disentangling user and item embeddings into interest and social influence embeddings.
no code implementations • IEEE/CVF International Conference on Computer Vision (ICCV) 2023 • Rajeev Yasarla, Hong Cai, Jisoo Jeong, Yunxiao Shi, Risheek Garrepalli, Fatih Porikli
We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation.
Ranked #14 on
Monocular Depth Estimation
on KITTI Eigen split
no code implementations • 6 Apr 2023 • Yunxiao Shi, Hong Cai, Amin Ansari, Fatih Porikli
the number of views and frames.
no code implementations • 28 Sep 2019 • Yunxiao Shi, Jing Zhu, Yi Fang, Kuochin Lien, Junli Gu
Learning to predict scene depth and camera motion from RGB inputs only is a challenging task.
no code implementations • 10 Sep 2019 • Jing Zhu, Yunxiao Shi, Mengwei Ren, Yi Fang, Kuo-Chin Lien, Junli Gu
To this end, we introduce a new Structure-Oriented Memory (SOM) module to learn and memorize the structure-specific information between RGB image domain and the depth domain.
Ranked #55 on
Monocular Depth Estimation
on KITTI Eigen split