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Jiaxin Qi

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3 papers

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AAAI Conference 2026 Conference Paper

Gene Incremental Learning for Single-Cell Transcriptomics

Jiaxin Qi
Yan Cui
Jianqiang Huang
Gaogang Xie

Classes, as fundamental elements of Computer Vision, have been extensively studied within incremental learning frameworks. In contrast, tokens, which play essential roles in many research fields, exhibit similar characteristics of growth, yet investigations into their incremental learning remain significantly scarce. This research gap primarily stems from the holistic nature of tokens in language, which imposes significant challenges on the design of incremental learning frameworks for them. To overcome this obstacle, in this work, we turn to a type of token, gene, for a large-scale biological dataset—single-cell transcriptomics—to formulate a pipeline for gene incremental learning and establish corresponding evaluations. We found that the forgetting problem also exists in gene incremental learning, thus we adapted existing class incremental learning methods to mitigate the forgetting of genes. Through extensive experiments, we demonstrated the soundness of our framework design and evaluations, as well as the effectiveness of the method adaptations. Finally, we provide a complete benchmark for gene incremental learning in single-cell transcriptomics.

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AAAI Conference 2025 Conference Paper

A Simple and Comprehensive Benchmark for Single-Cell Transcriptomics

Jiaxin Qi
Yan Cui
Kailei Guo
Xiaomin Zhang
Jianqiang Huang
Gaogang Xie

Single-cell transcriptomics describes complex molecular features at the individual cell level, serving various roles in biological research, such as enhancing gene expression and predicting drug responses. Due to transcriptomic data structurally resembling sequential data, many researchers have trained numerous transformers on extensive transcriptomic datasets. However, they have consistently neglected to explore the intrinsic properties of the data and the appropriateness of their chosen model architecture. In this paper, we carefully investigate the nature of transcriptomics, identifying three overlooked problems: 1) long-tailed data problem, 2) model selection problem, and 3) evaluation problem. Consequently, by applying the weighted sampling strategy, we address the long-tailed data problem and achieve consistent improvement across all settings. By adapting different model structures to transcriptomic data, we discover that transformers are not the only option. By developing three downstream tasks and fair evaluation metrics, we establish a simple and comprehensive benchmark to validate the effectiveness of models for transcriptomics. Through extensive experiments, we clarify the misunderstandings in the traditional methods and provide competitive baselines, thereby paving the way for future research in this field.

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AAAI Conference 2022 Conference Paper

Deconfounded Visual Grounding

Jianqiang Huang
Yu Qin
Jiaxin Qi
Qianru Sun
Hanwang Zhang

We focus on the confounding bias between language and location in the visual grounding pipeline, where we find that the bias is the major visual reasoning bottleneck. For example, the grounding process is usually a trivial languagelocation association without visual reasoning, e. g. , grounding any language query containing sheep to the nearly central regions, due to that most queries about sheep have groundtruth locations at the image center. First, we frame the visual grounding pipeline into a causal graph, which shows the causalities among image, query, target location and underlying confounder. Through the causal graph, we know how to break the grounding bottleneck: deconfounded visual grounding. Second, to tackle the challenge that the confounder is unobserved in general, we propose a confounder-agnostic approach called: Referring Expression Deconfounder (RED), to remove the confounding bias. Third, we implement RED as a simple language attention, which can be applied in any grounding method. On popular benchmarks, RED improves various state-of-the-art grounding methods by a significant margin. Code is available at: https: //github. com/JianqiangH/ Deconfounded VG.

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