Author name cluster

Qi Tang

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6 papers

2 author rows

ICLR Conference 2025 Conference Paper

A Theoretically-Principled Sparse, Connected, and Rigid Graph Representation of Molecules

Shih-Hsin Wang
Yuhao Huang
Justin M. Baker
Yuan-En Sun
Qi Tang
Bao Wang 0001

Graph neural networks (GNNs) -- learn graph representations by exploiting the graph's sparsity, connectivity, and symmetries -- have become indispensable for learning geometric data like molecules. However, the most used graphs (e.g., radial cutoff graphs) in molecular modeling lack theoretical guarantees for achieving connectivity and sparsity simultaneously, which are essential for the performance and scalability of GNNs. Furthermore, existing widely used graph construction methods for molecules lack rigidity, limiting GNNs' ability to exploit graph nodes' spatial arrangement. In this paper, we introduce a new hyperparameter-free graph construction of molecules and beyond with sparsity, connectivity, and rigidity guarantees. Remarkably, our method consistently generates connected and sparse graphs with the edge-to-node ratio being bounded above by 3. Our graphs' rigidity guarantees that edge distances and dihedral angles are sufficient to uniquely determine the general spatial arrangements of atoms. We substantiate the effectiveness and efficiency of our proposed graphs in various molecular modeling benchmarks. Code is available at https://github.com/shihhsinwang0214/SCHull.

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EAAI Journal 2025 Journal Article

Intelligent assessment of habitat quality based on multiple machine learning fusion methods

Kui Yang
Dongge Cui
Chengrui Wang
Qi Tang
Linguang Miao

Evaluating habitat quality can help balance the relationship between economic development and biodiversity conservation, and it serves as a foundation for constructing an ecological security pattern. However, research on the intelligent construction of habitat quality is limited. This study develops a comprehensive framework to assess habitat quality based on optimized machine learning methods. The findings of the research are as follows: (1) From the perspective of human-machine interactive interpretation, ensemble learning is used to enhance the performance of basic classifiers, resulting in a classification map with high precision and recall. (2) The particle swarm optimization (PSO) algorithm can improve the goodness of fit of the Extreme Gradient Boosting (XGBoost) inversion model by 4–5 %. (3) The habitat quality inversion method based on XGBoost-PSO has high credibility and application value, with its texture structure being the result of both expert experience and image information interaction. (4) The model demonstrates certain application potential in downscaling; under the seven-band perspective, the blue and near-infrared bands are the most important, while in the four-band perspective, green and near-infrared bands take precedence.

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EAAI Journal 2025 Journal Article

Non-contact weight intelligent estimation based on yak skeleton localization

Fei Wang
Xinghua Zou
Zhijiang Chen
Qi Tang
Tianshuo Li
Shuiying Wang
Lijun Yang
Dongming Tang

Weight estimation is a vital method for monitoring the growth and health of yaks, however, traditional techniques-such as relying on herders’ experience or using weighbridge-are labor-intensive, time-consuming and pose safety risks. Currently, many studies have shown that yak body size can be an effective indicator of weight. With the advancement of computer vision, non-contact weight estimation has become increasingly feasible for livestock. Yet, studies focusing on yak weight estimation, particularly in high-altitude plateau regions, remain limited. Herein, we propose a novel weight estimation approach based on deep learning and binocular vision technology to address this gap. The method involves four main steps: (1) yak image acquisition, (2) skeletal key point localization, (3) body size calculation (4) weight estimation using Gaussian process regression. To enhance practicality and mobility, we also developed two edge-intelligent devices: an intelligent inspection vehicle and a handheld detection unit, enabling convenient and non-invasive weight estimation. Our models are trained and tested on a yak dataset collected by our team on the Tibetan Plateau. Experimental results demonstrate the effectiveness of our approach, achieving an Mean Absolute Percentage Error (MAPE) of 0. 12 percent, a Mean Absolute Error (MAE) of 25. 4 kilograms (kg) and a Coefficient of determination ( R 2 ) value of 0. 72. It not only provides a new technical solution for the yak industry but also provides innovative insights for advancing intelligent animal husbandry. The code and data can be accessed at https: //github. com/FeiWang-swun/YakWeight.

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NeurIPS Conference 2024 Conference Paper

SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution

Qi Tang
Yao Zhao
Meiqin Liu
Chao Yao

Diffusion-based Video Super-Resolution (VSR) is renowned for generating perceptually realistic videos, yet it grapples with maintaining detail consistency across frames due to stochastic fluctuations. The traditional approach of pixel-level alignment is ineffective for diffusion-processed frames because of iterative disruptions. To overcome this, we introduce SeeClear--a novel VSR framework leveraging conditional video generation, orchestrated by instance-centric and channel-wise semantic controls. This framework integrates a Semantic Distiller and a Pixel Condenser, which synergize to extract and upscale semantic details from low-resolution frames. The Instance-Centric Alignment Module (InCAM) utilizes video-clip-wise tokens to dynamically relate pixels within and across frames, enhancing coherency. Additionally, the Channel-wise Texture Aggregation Memory (CaTeGory) infuses extrinsic knowledge, capitalizing on long-standing semantic textures. Our method also innovates the blurring diffusion process with the ResShift mechanism, finely balancing between sharpness and diffusion effects. Comprehensive experiments confirm our framework's advantage over state-of-the-art diffusion-based VSR techniques.

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AAAI Conference 2024 Conference Paper

Semantic Lens: Instance-Centric Semantic Alignment for Video Super-resolution

Qi Tang
Yao Zhao
Meiqin Liu
Jian Jin
Chao Yao

As a critical clue of video super-resolution (VSR), inter-frame alignment significantly impacts overall performance. However, accurate pixel-level alignment is a challenging task due to the intricate motion interweaving in the video. In response to this issue, we introduce a novel paradigm for VSR named Semantic Lens, predicated on semantic priors drawn from degraded videos. Specifically, video is modeled as instances, events, and scenes via a Semantic Extractor. Those semantics assist the Pixel Enhancer in understanding the recovered contents and generating more realistic visual results. The distilled global semantics embody the scene information of each frame, while the instance-specific semantics assemble the spatial-temporal contexts related to each instance. Furthermore, we devise a Semantics-Powered Attention Cross-Embedding (SPACE) block to bridge the pixel-level features with semantic knowledge, composed of a Global Perspective Shifter (GPS) and an Instance-Specific Semantic Embedding Encoder (ISEE). Concretely, the GPS module generates pairs of affine transformation parameters for pixel-level feature modulation conditioned on global semantics. After that the ISEE module harnesses the attention mechanism to align the adjacent frames in the instance-centric semantic space. In addition, we incorporate a simple yet effective pre-alignment module to alleviate the difficulty of model training. Extensive experiments demonstrate the superiority of our model over existing state-of-the-art VSR methods.

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IROS Conference 2020 Conference Paper

On a videoing control system based on object detection and tracking

Yanhao Ren
Yi Wang
Qi Tang
Haijun Jiang
Wenlian Lu

In this paper, we propose a camera control system towards occasionally videoing preassigned objects. Based on the technique of real-time visual detection and tracking, using the Kalman filter and re-identification (ReID), we propose continuous composition of lens, based on the atomic rules of shots, and give the trajectory planning of the camera, to generate the PID controller to the pan-tilt. By both simulation and emulation by frame-wise cropping of video clips, we illustrate the efficiency of this method. Based on this model, we design and produce an AI automatic camera for lively photography and clip videoing.

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