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Leiting Chen

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6 papers

1 author row

AAAI Conference 2026 Conference Paper

Escaping the CAM Shadow: Uncertainty-Guided Reliable Learning for Weakly Supervised Semantic Segmentation

Luyao Chang
Leiting Chen
Chen Yang
Chuan Zhou

Weakly supervised semantic segmentation (WSSS) suffers from an inherent mismatch between coarse image-level annotations and dense pixel-level predictions. To bridge this gap, existing methods primarily focus on generating refined class activation maps (CAM) as pseudo-labels. However, we argue that this focus is insufficient as it overlooks a critical component: the segmentation decoder. The decoder is typically trained through superficial alignment of predictions with pseudo-labels in the logit space. Given the noisy nature of such labels, this naive supervision leads to error accumulation and limits performance. To address this, we propose an Uncertainty-Guided Reliable Learning (UGRL) framework that exerts dual control to reshape the learning process, achieving robust supervision that escapes the CAM shadow. The cornerstone of UGRL is a prototype-driven uncertainty modeling module that estimates the reliability of class-wise supervision. The modeled uncertainty enables two synergistic control mechanisms. First, it adaptively modulates classification and segmentation losses, encouraging the model to learn from more trustworthy signals. Second, it guides the structuring of the decoder’s feature space. Rather than relying solely on superficial alignment, UGRL enforces deeper representation alignment by applying contrastive learning on reliable pixels. This enables rich semantic transfer to fine-grained segmentation details. Extensive experiments on PASCAL VOC and MS COCO demonstrate that our method surpasses other state-of-the-art WSSS methods.

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AAAI Conference 2026 Conference Paper

Modality-Balanced Collaborative Distillation for Multi-Modal Domain Generalization

Xiaohan Wang
Zhangtao Cheng
Ting Zhong
Leiting Chen
Fan Zhou

Weight Averaging (WA) has emerged as a powerful technique for enhancing generalization by promoting convergence to a flat loss landscape, which correlates with stronger out-of-distribution performance. However, applying WA directly to multi-modal domain generalization (MMDG) is challenging: differences in optimization speed across modalities lead WA to overfit to faster-converging ones in early stages, suppressing the contribution of slower yet complementary modalities, thereby hindering effective modality fusion and skewing the loss surface toward sharper, less generalizable minima. To address this issue, we propose MBCD, a unified collaborative distillation framework that retains WA's flatness-inducing advantages while overcoming its shortcomings in multi-modal contexts. MBCD begins with adaptive modality dropout in the student model to curb early-stage bias toward dominant modalities. A gradient consistency constraint then aligns learning signals between uni-modal branches and the fused representation, encouraging coordinated and smoother optimization. Finally, a WA-based teacher conducts cross-modal distillation by transferring fused knowledge to each uni-modal branch, which strengthens cross-modal interactions and steer convergence toward flatter solutions. Extensive experiments on MMDG benchmarks show that MBCD consistently outperforms existing methods, achieving superior accuracy and robustness across diverse unseen domains.

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AAAI Conference 2026 Conference Paper

Shedding the Facades, Connecting the Domains: Detecting Shifting Multimodal Hate Video with Test-Time Adaptation

Jiao Li
Jian Lang
Xikai Tang
Wenzheng Shu
Ting Zhong
Qiang Gao
Yong Wang
Leiting Chen

Hate Video Detection (HVD) is crucial for online ecosystems. Existing methods assume identical distributions between training (source) and inference (target) data. However, hateful content often evolves into irregular and ambiguous forms to evade censorship, resulting in substantial semantic drift and rendering previously trained models ineffective. Test-Time Adaptation (TTA) offers a solution by adapting models during inference to narrow the cross-domain gap, while conventional TTA methods target mild distribution shifts and struggle with the severe semantic drift in HVD. To tackle these challenges, we propose SCANNER, the first TTA framework tailored for HVD. Motivated by the insight that, despite the evolving nature of hateful manifestations, their underlying cores remain largely invariant (i.e., targeting is still based on characteristics like gender, race, etc), we leverage these stable cores as a bridge to connect the source and target domains. Specifically, SCANNER initially reveals the stable cores from the ambiguous layout in evolving hateful content via a principled centroid-guided alignment mechanism. To alleviate the impact of outlier-like samples that are weakly correlated with centroids during the alignment process, SCANNER enhances the prior by incorporating a sample-level adaptive centroid alignment strategy, promoting more stable adaptation. Furthermore, to mitigate semantic collapse from overly uniform outputs within clusters, SCANNER introduces an intra-cluster diversity regularization that encourages the cluster-wise semantic richness. Experiments show that SCANNER outperforms all baselines, with an average gain of 4.69% in Macro-F1 over the best.

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AIIM Journal 2026 Journal Article

Towards more efficient and better multi-view and multi-modal retinopathy assisted diagnosis

Yonghao Huang
Chuan Zhou
Leiting Chen

Fundus images are widely used in early retinopathy examination to prevent visual impairment caused by retinopathy. The retinopathy examination process based on fundus images can be mainly summarized in three steps: (1) ophthalmologists obtain comprehensive fundus information by jointly analyzing multi-view fundus images; (2) ophthalmologists obtain complementary lesion information by contrastingly analyzing multi-modal fundus images; (3) ophthalmologists diagnose retinopathy categories and write specialized fundus reports. To simulate the clinical fundus image examination process, we introduce an efficient multi-view and multi-modal fundus image joint ancillary diagnosis framework that can simultaneously accept fundus images of different views and modalities for pathology classification and symptom report generation tasks. In our framework, we propose jointly employing self-attention in intra-view local and inter-view sparse global windows to extract comprehensive fundus information among different views. We propose a multi-modal fusion transformer via shunted multi-scale cross-attention to model lesions of various scales by splitting attention granularity at query and queried modalities to fuse complementary lesion information among different modalities. The experimental results of retinopathy classification and report generation tasks indicate that our proposed method is superior to other benchmarking methods, achieving a classification accuracy of 83. 96% and a report generation CIDEr of 0. 934.

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AAAI Conference 2018 Conference Paper

Multi-Scale Bidirectional FCN for Object Skeleton Extraction

Fan Yang
Xin Li
Hong Cheng
Yuxiao Guo
Leiting Chen
Jianping Li

Object skeleton detection is a challenging problem with wide application. Recently, deep Convolutional Neural Networks (CNNs) have substantially improved the performance of the state-of-the-art in this task. However, most of the existing CNN-Based methods are based on a skip-layer structure where low-level and high-level features are combined and learned so as to gather multi-level contextual information. As shallow features are too messy and lack semantic knowledge, they may cause errors and inaccuracy. Therefore, we propose a novel network architecture, Multi-Scale Bidirectional Fully Convolutional Network (MSB-FCN), to better capture and consolidate multi-scale high-level context information for object skeleton detection. Our network uses only deep features to build multi-scale feature representations, and employs a bidirectional structure to collect contextual knowledge. Hence the proposed MSB-FCN has the ability to learn the semantic-level information from different sub-regions. Furthermore, we introduce dense connections into the bidirectional structure of our MSB-FCN to ensure that the learning process at each scale can directly encode information from all other scales. Extensive experiments on various commonly used benchmarks demonstrate that the proposed MSB- FCN has achieved signiﬁcant improvements over the state-ofthe-art algorithms.

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IJCAI Conference 2016 Conference Paper

Saliency Transfer: An Example-Based Method for Salient Object Detection

Xin Li
Fan Yang
Leiting Chen
Hongbin Cai

Over the past decades, numerous theories and studies have demonstrated that salient objects in different scenes often share some properties in common that make them visually stand out from their surroundings, and thus can be processed in finer details. In this paper, we propose a novel method for salient object detection that involves the transfer of the annotations from an existing example onto an input image. Our method, which is based on the low-level saliency features of each pixel, estimates dense pixel-wise correspondences between the input image and an example image, and then integrates high-level concepts to produce an initial saliency map. Finally, a coarse-to-fine optimization framework is proposed to generate uniformly highlighted salient objects. Qualitatively and quantitatively experiments on six popular benchmark datasets validate that our approach greatly outperforms the state-of-the-art algorithms and recently published works.

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