Author name cluster

Zihang He

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

ICML Conference 2025 Conference Paper

S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking

Tao Tang
Lijun Zhou
Pengkun Hao
Zihang He
Kalok Ho
Shuo Gu
Zhihui Hao
Haiyang Sun

3D multiple object tracking (MOT) plays a crucial role in autonomous driving perception. Recent end-to-end query-based trackers simultaneously detect and track objects, which have shown promising potential for the 3D MOT task. However, existing methods are still in the early stages of development and lack systematic improvements, failing to track objects in certain complex scenarios, like occlusions and the small size of target object’s situations. In this paper, we first summarize the current end-to-end 3D MOT framework by decomposing it into three constituent parts: query initialization, query propagation, and query matching. Then we propose corresponding improvements, which lead to a strong yet simple tracker: S2-Track. Specifically, for query initialization, we present 2D-Prompted Query Initialization, which leverages predicted 2D object and depth information to prompt an initial estimate of the object’s 3D location. For query propagation, we introduce an Uncertainty-aware Probabilistic Decoder to capture the uncertainty of complex environment in object prediction with probabilistic attention. For query matching, we propose a Hierarchical Query Denoising strategy to enhance training robustness and convergence. As a result, our S2-Track achieves state-of-the-art performance on nuScenes benchmark, i. e. , 66. 3% AMOTA on test split, surpassing the previous best end-to-end solution by a significant margin of 8. 9% AMOTA. We achieve 1st place on the nuScenes tracking task leaderboard.

Details

AAAI Conference 2024 System Paper

RecWizard: A Toolkit for Conversational Recommendation with Modular, Portable Models and Interactive User Interface

Zeyuan Zhang
Tanmay Laud
Zihang He
Xiaojie Chen
Xinshuang Liu
Zhouhang Xie
Julian McAuley
Zhankui He

We present a new Python toolkit called RecWizard for Conversational Recommender Systems (CRS). RecWizard offers support for development of models and interactive user interface, drawing from the best practices of the Huggingface ecosystems. CRS with RecWizard are modular, portable, interactive and Large Language Models (LLMs)-friendly, to streamline the learning process and reduce the additional effort for CRS research. For more comprehensive information about RecWizard, please check our GitHub https://github.com/McAuley-Lab/RecWizard.

PDF Details DOI

ICLR Conference 2023 Conference Paper

Revisit Finetuning strategy for Few-Shot Learning to Transfer the Emdeddings

Heng Wang
Tan Yue
Xiang Ye
Zihang He
Bohan Li 0013
Yong Li 0025

Few-Shot Learning (FSL) aims to learn a simple and effective bias on limited novel samples. Recently, many methods have been focused on re-training a randomly initialized linear classifier to adapt it to the novel features extracted by the pre-trained feature extractor (called Linear-Probing-based methods). These methods typically assumed the pre-trained feature extractor was robust enough, i.e., finetuning was not needed, and hence the pre-trained feature extractor does not change on the novel samples. However, the unchanged pre-trained feature extractor will distort the features of novel samples because the robustness assumption may not hold, especially on the out-of-distribution samples. To extract the undistorted features, we designed Linear-Probing-Finetuning with Firth-Bias (LP-FT-FB) to yield an accurate bias on the limited samples for better finetuning the pre-trained feature extractor, providing stronger transferring ability. In LP-FT-FB, we further proposed inverse Firth Bias Reduction (i-FBR) to regularize the over-parameterized feature extractor on which FBR does not work well. The proposed i-FBR effectively alleviates the over-fitting problem of the feature extractor in the process of finetuning and helps extract undistorted novel features. To show the effectiveness of the designed LP-FT-FB, we conducted a lot of experiments on the commonly used FSL datasets under different backbones, including in-domain and cross-domain FSL tasks. The experimental results show that the proposed FT-LP-FB outperforms the SOTA FSL methods. The code is available at https://github.com/whzyf951620/LinearProbingFinetuningFirthBias.

Details

ICML Conference 2018 Conference Paper

SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions

Chandrajit L. Bajaj
Tingran Gao
Zihang He
Qi-Xing Huang
Zhenxiao Liang

We introduce a principled approach for simultaneous mapping and clustering (SMAC) for establishing consistent maps across heterogeneous object collections (e. g. , 2D images or 3D shapes). Our approach takes as input a heterogeneous object collection and a set of maps computed between some pairs of objects, and outputs a homogeneous object clustering together with a new set of maps possessing optimal intra- and inter-cluster consistency. Our approach is based on the spectral decomposition of a data matrix storing all pairwise maps in its blocks. We additionally provide tight theoretical guarantees on the exactness of SMAC under established noise models. We also demonstrate the usefulness of the approach on synthetic and real datasets.

Details