Liming Wang

NeurIPS Conference 2025 Conference Paper

Can Diffusion Models Disentangle? A Theoretical Perspective

• Liming Wang
• Muhammad Jehanzeb Mirza
• Yishu Gong
• Yuan Gong
• Jiaqi Zhang
• Brian Tracey
• Katerina Placek
• Marco Vilela

This paper presents a novel theoretical framework for understanding how diffusion models can learn disentangled representations with commonly used weak supervision such as partial labels and multiple views. Within this framework, we establish identifiability conditions for diffusion models to disentangle latent variable models with \emph{stochastic}, \emph{non-invertible} mixing processes. We also prove \emph{finite-sample global convergence} for diffusion models to disentangle independent subspace models. To validate our theory, we conduct extensive disentanglement experiments on subspace recovery in latent subspace Gaussian mixture models, image colorization, denoising, and voice conversion for speech classification. Our experiments show that training strategies inspired by our theory, such as style guidance regularization, consistently enhance disentanglement performance.

EAAI Journal 2025 Journal Article

Physics-informed dual guidance method using physical envelope harmonic distribution and transfer learning for few-shot gear fault classification

• Kun Yue
• Liming Wang
• Xiaoxi Ding
• Wennian Yu
• Zaigang Chen
• Wenbin Huang

AAAI Conference 2021 Conference Paper

Empowering Adaptive Early-Exit Inference with Latency Awareness

• Xinrui Tan
• Hongjia Li
• Liming Wang
• Xueqing Huang
• Zhen Xu

With the capability of trading accuracy for latency on-the-fly, the technique of adaptive early-exit inference has emerged as a promising line of research to accelerate the deep learning inference. However, studies in this line of research commonly use a group of thresholds to control the accuracy-latency trade-off, where a thorough and general methodology on how to determine these thresholds has not been conducted yet, especially with regard to the common requirements of average inference latency. To address this issue and enable latencyaware adaptive early-exit inference, in the present paper, we approximately formulate the threshold determination problem of finding the accuracy-maximum threshold setting that meets a given average latency requirement, and then propose a threshold determination method to tackle our formulated non-convex problem. Theoretically, we prove that, for certain parameter settings, our method finds an approximate stationary point of the formulated problem. Empirically, on top of various models across multiple datasets (CIFAR-10, CIFAR- 100, ImageNet and two time-series datasets), we show that our method can well handle the average latency requirements, and consistently finds good threshold settings in negligible time.

ICRA Conference 2018 Conference Paper

Planar Object Tracking in the Wild: A Benchmark

• Pengpeng Liang
• Yifan Wu
• Hu Lu
• Liming Wang
• Chunyuan Liao
• Haibin Ling

Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the-art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking.

NeurIPS Conference 2013 Conference Paper

Designed Measurements for Vector Count Data

• Liming Wang
• David Carlson
• Miguel Rodrigues
• David Wilcox
• Robert Calderbank
• Lawrence Carin

We consider design of linear projection measurements for a vector Poisson signal model. The projections are performed on the vector Poisson rate, $X\in\mathbb{R}_+^n$, and the observed data are a vector of counts, $Y\in\mathbb{Z}_+^m$. The projection matrix is designed by maximizing mutual information between $Y$ and $X$, $I(Y; X)$. When there is a latent class label $C\in\{1, \dots, L\}$ associated with $X$, we consider the mutual information with respect to $Y$ and $C$, $I(Y; C)$. New analytic expressions for the gradient of $I(Y; X)$ and $I(Y; C)$ are presented, with gradient performed with respect to the measurement matrix. Connections are made to the more widely studied Gaussian measurement model. Example results are presented for compressive topic modeling of a document corpora (word counting), and hyperspectral compressive sensing for chemical classification (photon counting).

NeurIPS Conference 2008 Conference Paper

Grouping Contours Via a Related Image

• Praveen Srinivasan
• Liming Wang
• Jianbo Shi

Contours have been established in the biological and computer vision literatures as a compact yet descriptive representation of object shape. While individual contours provide structure, they lack the large spatial support of region segments (which lack internal structure). We present a method for further grouping of contours in an image using their relationship to the contours of a second, related image. Stereo, motion, and similarity all provide cues that can aid this task; contours that have similar transformations relating them to their matching contours in the second image likely belong to a single group. To find matches for contours, we rely only on shape, which applies directly to all three modalities without modification, in constrant to the specialized approaches developed for each independently. Visually salient contours are extracted in each image, along with a set of candidate transformations for aligning subsets of them. For each transformation, groups of contours with matching shape across the two images are identified to provide a context for evaluating matches of individual contour points across the images. The resulting contexts of contours are used to perform a final grouping on contours in the original image while simultaneously finding matches in the related image, again by shape matching. We demonstrate grouping results on image pairs consisting of stereo, motion, and similar images. Our method also produces qualitatively better results against a baseline method that does not use the inferred contexts.