Portrait de Yang Wang

Yang Wang

Membre académique associé
yang.wang@concordia.ca
Professeur agrégé, Concordia University, Département d'informatique et de génie logiciel
Sujets de recherche
Apprentissage automatique appliqué
Apprentissage profond
Vision par ordinateur
Site web
Google Scholar

Étudiants actuels

Publications

FACT-GS: Frequency-Aligned Complexity-Aware Texture Reparameterization for 2D Gaussian Splatting
Tianhao Xie
Linlian Jiang
Xinxin Zuo
Yang Wang
Tiberiu Popa
Realistic scene appearance modeling has advanced rapidly with Gaussian Splatting, which enables real-time, high-quality rendering. Recent ad… (voir plus)vances introduced per-primitive textures that incorporate spatial color variations within each Gaussian, improving their expressiveness. However, texture-based Gaussians parameterize appearance with a uniform per-Gaussian sampling grid, allocating equal sampling density regardless of local visual complexity, which leads to inefficient texture space utilization. We introduce FACT-GS, a Frequency-Aligned Complexity-Aware Texture Gaussian Splatting framework that allocates texture sampling density according to local visual frequency. Grounded in adaptive sampling theory, FACT-GS reformulates texture parameterization as a differentiable sampling-density allocation problem, replacing the uniform textures with a learnable frequency-aware allocation strategy implemented via a deformation field whose Jacobian modulates local sampling density. Built on 2D Gaussian Splatting, FACT-GS performs non-uniform sampling on fixed-resolution texture grids, preserving real-time performance while recovering sharper high-frequency details under the same parameter budget.
2026-09-20
IEEE/CVF Conference on Computer Vision and Pattern Recognition (publié)
doi.org
openreview.net
Learning Through Creation: A Hash-Free Framework for On-the-Fly Category Discovery
Bohan Zhang
Weidong Tang
Zhixiang Chi
Yi Jin
Zhenbo Li
Yang Wang
Yanan Wu
On-the-Fly Category Discovery (OCD) aims to recognize known classes while simultaneously discovering emerging novel categories during infere… (voir plus)nce, using supervision only from known classes during offline training. Existing approaches rely either on fixed label supervision or on diffusion-based augmentations to enhance the backbone, yet none of them explicitly train the model to perform the discovery task required at test time. It is fundamentally unreasonable to expect a model optimized on limited labeled data to carry out a qualitatively different discovery objective during inference. This mismatch creates a clear optimization misalignment between the offline learning stage and the online discovery stage. In addition, prior methods often depend on hash-based encodings or severe feature compression, which further limits representational capacity. To address these issues, we propose Learning through Creation (LTC), a fully feature-based and hash-free framework that injects novel-category awareness directly into offline learning. At its core is a lightweight, online pseudo-unknown generator driven by kernel-energy minimization and entropy maximization (MKEE). Unlike previous methods that generate synthetic samples once before training, our generator evolves jointly with the model's dynamics and synthesizes pseudo-novel instances on-the-fly at negligible cost. These samples are incorporated through a dual max-margin objective with adaptive thresholding, strengthening the model's ability to delineate and detect unknown regions through explicit creation. Extensive experiments across seven benchmarks show that LTC consistently outperforms prior work, achieving improvements ranging from 1.5% to 13.1% in all-class accuracy.
2026-09-20
IEEE/CVF Conference on Computer Vision and Pattern Recognition (publié)
openreview.net
openreview.net
Bridging the Modality Gap in Compositional Zero-Shot Learning via Sparse Alignment and Unimodal Memory Bank
Yang Zhang
Zhixiang Chi
Xudong Yan
Yang Wang
Songhe Feng
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object compositions with learned primitives (attribute and object… (voir plus)) knowledge from seen compositions. While previous approaches gain their notable performance through the powerful cross-modal alignment of CLIP, they often overlook the modality gap, an inherent constraint stemming from information-imbalanced training data. In this work, we propose SAM, a novel
2026-09-16
IEEE/CVF Conference on Computer Vision and Pattern Recognition (publié)
openreview.net
openreview.net
RDFace: A Benchmark Dataset for Rare Disease Facial Image Analysis under Extreme Data Scarcity and Phenotype-Aware Synthetic Generation
Ganlin Feng
Yuxi Long
Hafsa Ali
Erin Lou
Fahad Butt
Qian Liu
Yang Wang
Pingzhao Hu
Rare diseases often manifest with distinctive facial phenotypes in children, offering valuable diagnostic cues for clinicians and AI-assiste… (voir plus)d screening systems. However, progress in this field is severely limited by the scarcity of curated, ethically sourced facial data and the high similarity among phenotypes across different conditions. To address these challenges, we introduce RDFace, a curated benchmark dataset comprising 456 pediatric facial images spanning 103 rare genetic conditions (average 4.4 samples per condition). Each ethically verified image is paired with standardized metadata. RDFace enables the development and evaluation of data-efficient AI models for rare disease diagnosis under real-world low-data constraints. We benchmark multiple pretrained vision backbones using cross-validation and explore synthetic augmentation with DreamBooth and FastGAN. Generated images are filtered via facial landmark similarity to maintain phenotype fidelity and merged with real data, improving diagnostic accuracy by up to 13.7% in ultra-low-data regimes. To assess semantic validity, phenotype descriptions generated by a vision–language model from real and synthetic images achieve a report similarity score of 0.84. RDFace establishes a transparent, benchmark-ready dataset for equitable rare disease AI research and presents a scalable framework for evaluating both diagnostic performance and the integrity of synthetic medical imagery.
2026-09-16
IEEE/CVF Conference on Computer Vision and Pattern Recognition (publié)
doi.org
openreview.net
TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery
Yanan Wu
Yuhan Yan
Tailai Chen
Zhixiang Chi
ZiZhang Wu
Yi Jin
Yang Wang
Zhenbo Li
On-the-fly category discovery (OCD) aims to recognize known categories while simultaneously discovering novel ones from an unlabeled online … (voir plus)stream, using a model trained only on labeled data. Existing approaches freeze the feature extractor trained offline and employ a hash-based framework that quantizes features into binary codes as class prototypes. However, discovering novel categories with a fixed knowledge base is counterintuitive, as the learning potential of incoming data is entirely neglected. In addition, feature quantization introduces information loss, diminishes representational expressiveness, and amplifies intra-class variance. It often results in category explosion, where a single class is fragmented into multiple pseudo-classes. To overcome these limitations, we propose a test-time adaptation framework that enables learning through discovery. It incorporates two complementary strategies: a semantic-aware prototype update and a stable test-time encoder update. The former dynamically refines class prototypes to enhance classification, whereas the latter integrates new information directly into the parameter space. Together, these components allow the model to continuously expand its knowledge base with newly encountered samples. Furthermore, we introduce a margin-aware logit calibration in the offline stage to enlarge inter-class margins and improve intra-class compactness, thereby reserving embedding space for future class discovery. Experiments on standard OCD benchmarks demonstrate that our method substantially outperforms existing hash-based state-of-the-art approaches, yielding notable improvements in novel-class accuracy and effectively mitigating category explosion. The code is publicly available at https://github.com/ynanwu/TALON.
2026-09-16
IEEE/CVF Conference on Computer Vision and Pattern Recognition (publié)
doi.org
openreview.net
Widget2Code: From Visual Widgets to UI Code via Multimodal LLMs
Houston H. Zhang
Tao Zhang
Baoze Lin
Yuanqi Xue
Yincheng Zhu
Huan Liu
Li Gu
Linfeng Ye
Ziqiang Wang
Xinxin Zuo
Yang Wang
Yuanhao Yu
Zhixiang Chi
User interface to code (UI2Code) aims to generate executable code that can faithfully reconstruct a given input UI. Prior work focuses large… (voir plus)ly on web pages and mobile screens, leaving app widgets underexplored. Unlike web or mobile UIs with rich hierarchical context, widgets are compact, context-free micro-interfaces that summarize key information through dense layouts and iconography under strict spatial constraints. Moreover, while (image, code) pairs are widely available for web or mobile UIs, widget designs are proprietary and lack accessible markup. We formalize this setting as the Widget-to-Code (Widget2Code) and introduce an image-only widget benchmark with fine-grained, multi-dimensional evaluation metrics. Benchmarking shows that although generalized multimodal large language models (MLLMs) outperform specialized UI2Code methods, they still produce unreliable and visually inconsistent code. To address these limitations, we develop a baseline that jointly advances perceptual understanding and structured code generation. At the perceptual level, we follow widget design principles to assemble atomic components into complete layouts, equipped with icon retrieval and UI composition modules. At the system level, we design an end-to-end infrastructure, WidgetFactory, which includes a framework-agnostic widget-tailored domain-specific language (WidgetDSL) and a compiler that translates it into multiple front-end implementations (e.g., React, HTML). An adaptive rendering module further refines spatial dimensions to satisfy compactness constraints. Together, these contributions substantially enhance visual fidelity, establishing a strong baseline and unified infrastructure for future Widget2Code research.
2026-09-16
IEEE/CVF Conference on Computer Vision and Pattern Recognition (publié)
doi.org
openreview.net
From Visual Widgets to UI Code: Efficient Tool-Grounded Generation
Houston H. Zhang
Tao Zhang
Li Gu
Linfeng Ye
Yuanhao Yu
Xinxin Zuo
Yang Wang
Zhixiang Chi
Existing screenshot-to-code systems face a trade-off between flexibility and controllability. Direct multimodal generation can hallucinate v… (voir plus)isible details, whereas structured pipelines reduce such errors through component-wise decomposition, predefined templates, and customized intermediate representations. These structures, however, introduce additional generative orchestration and restrict outputs to designs covered by the representation. We investigate whether selective tool grounding can improve the fidelity--efficiency trade-off of direct widget-to-code generation. We introduce \textbf{WidgetGen}, a lightweight tool-grounded framework that extracts observable text and color evidence, performs high-level layout and optional chart reasoning, and directly generates executable JavaScript XML (\emph{JSX}). This design reduces reliance on component-wise generation while avoiding a fixed UI schema. Across six multimodal models and \(1{,}000\) held-out widgets, WidgetGen outperforms direct prompting and the structured Widget2Code pipeline on most visual reconstruction metrics, with consistent gains in area, legibility, and style. Finally, reconstruction-derived image-code pairs improve six Qwen-family open-weight models across every reported metric through supervised fine-tuning. These results establish WidgetGen as a strong lightweight baseline and show that selective evidence grounding offers an effective alternative to extensive representation constraints.
2026-09-03
MUCG @ European Conference on Computer Vision (publié)
doi.org
openreview.net
MSpecTmol: A Multi-Modal Spectroscopic Learning Framework for Automated Molecular Structure Elucidation
Wenjie Du
Xiaohan Qin
Ye Wei
Jun Xia
Yang Wang
Spectroscopic techniques are indispensable for the elucidation of molecular structures, particularly for novel molecules with unknown config… (voir plus)urations. However, a fundamental limitation of any single spectroscopic modality is that it provides an inherently circumscribed and fragmented view, capturing only specific facets of the complete molecular structure, which is often insufficient for unequivocal and robust characterization. Consequently, the integration of data from multiple spectroscopic sources is imperative to overcome these intrinsic limitations and achieve a comprehensive and accurate structural characterization. In this work, we introduce \textbf{MSpecTmol}, a novel \textbf{M}ulti-modal \textbf{Spec}trum information fusion learning framework for automated \textbf{Mol}ecular structure elucidation. By extending information bottleneck theory, our framework provides a principled and adaptive approach to fusing spectra. It designates a primary modality to extract core molecular features while leveraging auxiliary inputs to enrich the representation. To validate the end-to-end effectiveness of our framework, we design a two-fold evaluation: molecular substructure classification to probe its discriminative power in identifying substructures, and extends this knowledge to reconstruct plausible 3D structures. Our results not only demonstrate state-of-the-art performance in molecular substructure classification but also achieve near-experimental accuracy (\textasciitilde 0.68\AA) in molecular conformation reconstruction. These findings underscore the model’s capacity to learn interpretable features aligned with chemical intuition, thereby paving the way for future advances in automated and reliable spectroscopic analysis. Our code can be found at \href{https://anonymous.4open.science/r/MspecTmol-6B4D}{https://anonymous.4open.science.}
2026-07-21
Transactions on Machine Learning Research (accepté)
openreview.net
openreview.net
DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation
Siobhan Reid
Zhixiang Chi
Li Gu
Omid Reza Heidari
Ziqiang Wang
Yang Wang
Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples. This… (voir plus) setting is more realistic than typical domain adaptation setups, which assume access to target data during source training. However, prior FSTT-DA approaches fail to effectively leverage source domain-specific knowledge, relying on shallow batch normalization updates, prompt-based methods that treat the model as a black box, or ensembling strategies that do not capture cross-domain relationships. To address these limitations, we introduce a new FSTT-DA framework that integrates LoRA fine-tuning with model merging. In our approach, separate LoRA modules are fine-tuned on CLIP's vision encoder for each source domain. Since LoRA modifies only a small fraction of the model's parameters, it retains the base model's generalized knowledge while internally learning domain-specific features. To adapt the learned knowledge to a specific target domain, we propose a hypernetwork trained via meta-learning that generates per-column merging factors to combine LoRA modules. Given a small batch of target images, the hypernetwork produces merging weights that fuse source LoRA modules into a single adapted representation. Our results demonstrate state-of-the-art performance across various domain adaptation datasets. Our code is publicly available at https://github.com/nahbois4321/DA-MergeLoRA.
2026-07-19
arXiv (prépublication)
doi.org
arxiv.org
No Adaptation Without Observation: Observability-Constrained Test-Time Prompt Tuning for LiDAR Semantic Segmentation
Linlian Jiang
Wentao Ju
Sadman Rakib Pinon
Jianwei Xian
Zhixiang Chi
Xinxin Zuo
Yang Wang
LiDAR semantic segmentation often degrades under real-world deployment due to evolving sensing conditions, while collecting new annotations … (voir plus)for retraining is impractical. Test-time adaptation (TTA) updates model parameters online using pseudo-label supervision, but directly applying standard TTA strategies to LiDAR data is challenging. Because pseudo-label reliability is spatially heteroscedastic under range-dependent sparsity and occlusion, uniform updates on globally shared parameters can inject unstable gradients and destabilize adaptation. We propose a geometry-constrained test-time prompt tuning framework for LiDAR semantic segmentation. Our method estimates per-location sensing reliability from depth-consistent beam terminations and neighborhood support, and uses it to reweight spatial supervision. Adaptation is confined to lightweight prompt adapters inserted into a frozen backbone, with spatial gating to prevent unreliable regions from perturbing globally shared representations. A temporally smoothed prototype alignment strategy further stabilizes online updates by accumulating reliable semantic evidence over time. Experiments on standard LiDAR benchmarks demonstrate improved adaptation stability and segmentation performance under deployment variations without additional annotations.
2026-06-28
arXiv (prépublication)
doi.org
arxiv.org
A four-amino-acid YRIN insertion in ftsI reduced ceftiofur susceptibility in a canine Escherichia coli isolate
Xukun Dang
Zeshi Chen
Rina Bai
Junyao Jiang
Weishuai Zhai
Shizhen Ma
Lu Liu
Dejun Liu
Zhaofei Xia
Zhangqi Shen
Yang Wang
Siyu Chen
Abstract Ceftiofur resistance in canine clinical Escherichia coli is usually associated with extended-spectrum β-lactamases (ESBLs) or AmpC… (voir plus) β-lactamases. However, some isolates display elevated ceftiofur minimum inhibitory concentrations (MICs) without carrying these known resistance genes. In this study, we identified a phenotype-genotype discordant canine clinical E. coli isolate named 231255, with a ceftiofur MIC of 8 μg/mL. Routine resistance gene screening detected only bla TEM-1 and a chromosomal bla EC variant, bla EC-1149 , which could not adequately explain the elevated ceftiofur MIC. To investigate the underlying mechanism, a genomic library was constructed from genomic DNA of isolate 231255 and screened on ceftiofur-containing plates. Positive clones revealed one candidate determinant: an altered ftsI fragment encoding penicillin-binding protein 3 (PBP3) with a four-amino-acid YRIN insertion downstream of residue P333, previously identified in human E. coli . Previous studies have shown that this type of YRIN/YRIK insertion alone can reduce susceptibility to PBP3-targeting β-lactams, particularly aztreonam and ceftazidime. Functional validation showed that recombinant plasmids carrying the altered ftsI consistently increased the ceftiofur MIC to 4 μg/mL in different recipient backgrounds. These findings provide experimental evidence that ftsI /PBP3 alteration can elevate ceftiofur MIC in canine E. coli . Notably, the isolate belonged to ST410, and phylogenetic analysis indicated that it was not confined to a dog-associated background but instead clustered within a broader lineage shared across multiple sources, highlighting the need for potential dissemination of this mechanism and its associated resistant lineages at the human-companion animal interface.
2026-06-22
One Health Advances (publié)
doi.org
Beyond the GUI Paradigm: Do Mobile Agents Need the Phone Screen?
Li Gu
Zihuan Jiang
Linqiang Guo
Zhixiang Chi
Ziqiang Wang
Huan Liu
Yuanhao Yu
Tse-Hsun Chen
Yang Wang
Recent advances in mobile agents are dominated by the GUI paradigm, in which agents perceive UI information and emit screen interactions. Ho… (voir plus)wever, mobile platforms also expose a command-line interface (CLI) that provides direct access to device services and data. We argue CLI deserves first-class consideration alongside GUI. We evaluate three coding agents (Claude Code, Terminus-2, mini-swe-agent) across four model APIs on AndroidWorld and MobileWorld without any mobile-specific post-training, comparing against three reproducible GUI baselines (GUI-Owl-1.5-32B, MAI-UI, Qwen3-VL-32B). Claude Code (Opus 4.7) reaches 71.8\% and 51.9\%, outperforming every reproducible GUI baseline (69.3/68.1/57.8\% on AndroidWorld; 43.2/26.3/13.3\% on MobileWorld), while every other CLI configuration remains competitive. To establish the paradigm's ceiling, we provide oracle CLI solutions that reach 88.8\% on AndroidWorld (103/116 tasks CLI-solvable) and 86.3\% on MobileWorld (101/117 tasks CLI-solvable), indicating substantial room for future improvement. To cover everyday user intents beyond the GUI scope, we introduce the \textbf{CLI-Advantage Task Suite}, comprising 45 templates across five categories: bulk operations, multi-condition filtering, aggregation, cross-app workflows, and hidden device state. Every CLI agent outperforms every GUI baseline in all five categories, with substantially fewer steps per task (10.7 vs.\ 18.6). To support future research on mobile CLI agents, we will open-source agent implementations, oracle solutions, the CLI-Advantage suite, and evaluation infrastructure.
2026-06-15
arXiv (prépublication)
doi.org
arxiv.org