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Jiaqi Lin

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6 papers

1 author row

TMLR Journal 2025 Journal Article

Activate and Adapt: A Two-Stage Framework for Open-Set Model Adaptation

Xiasi Wang
Jiaqi Lin
Chaoqi Chen
Luyao Tang
Yi Huang
Chengsen Wang
Lei YE
Yuan Yao

The ability of generalizing to new environments is critical for deep neural networks. Most existing works presume that the training and test data share an identical label set, overlooking the potential presence of new classes in test data. In this paper, we tackle a practical and challenging problem: Open-Set Model Adaptation (OSMA). OSMA aims to train a model on the source domain, which contains only known class data, and then adapt the trained model to the distribution-shifted target domain to classify known class data while identifying new class data. In this context, we face two challenges: (1) enabling the model to recognize new classes using only the known class data from the source domain during training, and (2) adapting the source-trained model to the target domain that contains new class data. To address these challenges, we propose a novel and universal two-stage framework named Activate and Adapt (ADA). In the training stage, we extract potential new class information hidden within the rich semantics of the source domain data to enable the model to identify new class data. Additionally, to retain source domain information while preserving data privacy, we condense the source domain data into a small dataset, facilitating the subsequent adaptation phase. In the test stage, we adaptively adjust the source-trained model to the target domain with new classes by infusing the style of target data into the condensed dataset, and decoupling domain alignment for known and new classes. Experiments across three standard benchmarks demonstrate that ADA surpasses previous methods in both online and offline settings.

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AAAI Conference 2025 Conference Paper

Decoupling Appearance Variations with 3D Consistent Features in Gaussian Splatting

Jiaqi Lin
Zhihao Li
Binxiao Huang
Xiao Tang
Jianzhuang Liu
Shiyong Liu
Xiaofei Wu
Fenglong Song

Gaussian Splatting has emerged as a prominent 3D representation in novel view synthesis, but it still suffers from appearance variations, which are caused by various factors, such as modern camera ISPs, different time of day, weather conditions, and local light changes. These variations can lead to floaters and color distortions in the rendered images/videos. Recent appearance modeling approaches in Gaussian Splatting are either tightly coupled with the rendering process, hindering real-time rendering, or they only account for mild global variations, performing poorly in scenes with local light changes. In this paper, we propose DAVIGS, a method that decouples appearance variations in a plug-and-play and efficient manner. By transforming the rendering results at the image level instead of the Gaussian level, our approach can model appearance variations with minimal optimization time and memory overhead. Furthermore, our method gathers appearance-related information in 3D space to transform the rendered images, thus building 3D consistency across views implicitly. We validate our method on several appearance-variant scenes, and demonstrate that it achieves state-of-the-art rendering quality with minimal training time and memory usage, without compromising rendering speeds. Additionally, it provides performance improvements for different Gaussian Splatting baselines in a plug-and-play manner.

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IJCAI Conference 2025 Conference Paper

Hybrid Mesh-Gaussian Representation for Efficient Indoor Scene Reconstruction

Binxiao Huang
Zhihao Li
Shiyong Liu
Xiao Tang
Jiajun Tang
Jiaqi Lin
Yuxin Cheng
Zhenyu Chen

3D Gaussian splatting (3DGS) has demonstrated exceptional performance in image-based 3D reconstruction and real-time rendering. However, regions with complex textures require numerous Gaussians to capture significant color variations accurately, leading to inefficiencies in rendering speed. To address this challenge, we introduce a hybrid representation for indoor scenes that combines 3DGS with textured meshes. Our approach uses textured meshes to handle texture-rich flat areas, while retaining Gaussians to model intricate geometries. The proposed method begins by pruning and refining the extracted mesh to eliminate geometrically complex regions. We then employ a joint optimization for 3DGS and mesh, incorporating a warm-up strategy and transmittance-aware supervision to balance their contributions seamlessly. Extensive experiments demonstrate that the hybrid representation maintains comparable rendering quality and achieves superior frames per second FPS with fewer Gaussian primitives.

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NeurIPS Conference 2025 Conference Paper

Quantifying and Alleviating Co-Adaptation in Sparse-View 3D Gaussian Splatting

Kangjie Chen
Yingji Zhong
Zhihao Li
Jiaqi Lin
Youyu Chen
Minghan Qin
Haoqian Wang

3D Gaussian Splatting (3DGS) has demonstrated impressive performance in novel view synthesis under dense-view settings. However, in sparse-view scenarios, despite the realistic renderings in training views, 3DGS occasionally manifests appearance artifacts in novel views. This paper investigates the appearance artifacts in sparse-view 3DGS and uncovers a core limitation of current approaches: the optimized Gaussians are overly-entangled with one another to aggressively fit the training views, which leads to a neglect of the real appearance distribution of the underlying scene and results in appearance artifacts in novel views. The analysis is based on a proposed metric, termed Co-Adaptation Score (CA), which quantifies the entanglement among Gaussians, i. e. , co-adaptation, by computing the pixel-wise variance across multiple renderings of the same viewpoint, with different random subsets of Gaussians. The analysis reveals that the degree of co-adaptation is naturally alleviated as the number of training views increases. Based on the analysis, we propose two lightweight strategies to explicitly mitigate the co-adaptation in sparse-view 3DGS: (1) random gaussian dropout; (2) multiplicative noise injection to the opacity. Both strategies are designed to be plug-and-play, and their effectiveness is validated across various methods and benchmarks. We hope that our insights into the co-adaptation effect will inspire the community to achieve a more comprehensive understanding of sparse-view 3DGS.

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AAAI Conference 2025 Conference Paper

SOVGaussian: Sparse-View 3D Gaussian Splatting for Open-Vocabulary Scene Understanding

Peng Ling
Tiao Tan
Jiaqi Lin
Wenming Yang

Modeling 3D open-vocabulary language fields is challenging yet highly anticipated. Despite great progress, existing approaches heavily rely on a large number of training views to construct language-embedded 3D scenes, which is unfortunately impractical in real-world scenarios. This paper introduces SOVGaussian, the first method for few-shot novel view open-vocabulary language querying. We introduce a depth-constrained neural language field to mitigate the geometry degradation caused by overfitting training views. Rather than straightforwardly using dense depth maps for loosely accurate supervision, Language-Aware Depth Distillation (LAD) based on open-vocabulary object masks is proposed, ensuring intra-object geometric accuracy within the language field. To further refine the language-geometry consistency of the language field, we propose a novel Language-Guided Outlier Pruning (LOP) strategy, which identifies floating 3D Gaussian primitives overfitting training views based on their language-grouped densities. Our comprehensive experiments demonstrate that SOVGaussian is able to reconstruct a superior scene representation from few-shot images, outperforming existing state-of-the-art methods and achieving significantly better performance on novel view language querying and synthesis.

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NeurIPS Conference 2022 Conference Paper

Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm

Aidan Good
Jiaqi Lin
Xin Yu
Hannah Sieg
Mikey Fergurson
Shandian Zhe
Jerzy Wieczorek
Thiago Serra

Pruning techniques have been successfully used in neural networks to trade accuracy for sparsity. However, the impact of network pruning is not uniform: prior work has shown that the recall for underrepresented classes in a dataset may be more negatively affected. In this work, we study such relative distortions in recall by hypothesizing an intensification effect that is inherent to the model. Namely, that pruning makes recall relatively worse for a class with recall below accuracy and, conversely, that it makes recall relatively better for a class with recall above accuracy. In addition, we propose a new pruning algorithm aimed at attenuating such effect. Through statistical analysis, we have observed that intensification is less severe with our algorithm but nevertheless more pronounced with relatively more difficult tasks, less complex models, and higher pruning ratios. More surprisingly, we conversely observe a de-intensification effect with lower pruning ratios.

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