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Shuo Feng

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5 papers

1 author row

AAAI Conference 2026 Conference Paper

VPN: Visual Prompt Navigation

Shuo Feng
Zihan Wang
Yuchen Li
Rui Kong
Hengyi Cai
Shuaiqiang Wang
Gim Hee Lee
Piji Li

While natural language is commonly used to guide embodied agents, the inherent ambiguity and verbosity of language often hinder the effectiveness of language-guided navigation in complex environments. To this end, we propose Visual Prompt Navigation (VPN), a novel paradigm that guides agents to navigate using only user-provided visual prompts within 2D top-view maps. This visual prompt primarily focuses on marking the visual navigation trajectory on a top-down view of a scene, offering intuitive and spatially grounded guidance without relying on language instructions. It is more friendly for non-expert users and reduces interpretive ambiguity. We build VPN tasks in both discrete and continuous navigation settings, constructing two new datasets, R2R-VP and R2R-CE-VP, by extending existing R2R and R2R-CE episodes with corresponding visual prompts. Furthermore, we introduce VPNet, a dedicated baseline network to handle the VPN tasks, with two data augmentation strategies: view-level augmentation (altering initial headings and prompt orientations) and trajectory-level augmentation (incorporating diverse trajectories from large-scale 3D scenes), to enhance navigation performance. Extensive experiments evaluate how visual prompt forms, top-view map formats, and data augmentation strategies affect the performance of visual prompt navigation.

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EAAI Journal 2025 Journal Article

Context-aware emotion recognition through agent-scene interactions

Yu-Xiang Chen
Hong-Mei Sun
Cheng-Yue Che
Shuo Feng
Rui-Sheng Jia

In real-world scenarios, context-aware emotion recognition (CAER) is a key problem in affective computing with broad application prospects. Most current CAER methods primarily rely on image-level contextual features. However, the interactive relationships between the agent and other objects within the scene are often overlooked or only partially modeled, which limits emotion recognition accuracy. To address this, we proposed a spatial interactive context-aware emotion network (ICENet) that consists of an agent feature extraction branch and a scene-context interaction branch. Specifically, the agent feature extraction branch aims to extract facial and posture features from the target agent and fuse them. In the facial feature extraction network named FaceNet, pure Convolutional Neural Network (ConvNeXt) is used as the backbone to extract global features, and a self-attention-based fine-grained feature extraction (FGFE) module is designed to capture more discriminative local features. In the posture feature extraction network, semantic segmentation is used to extract human silhouettes, which are then processed by Vision Transformer to obtain posture-related features. Meanwhile, the scene-context interaction branch named ObjNet integrates agent’s gaze angle and global depth maps to construct target agent-objects relationship in three-dimensional (TAR3D). Subsequently, a Graph Convolutional Network is employed to model the TAR3D and extract scene-context interaction features. Subsequently, a multiplicative fusion strategy is adopted to integrate agent features with scene-context interaction features, and emotion classification is performed based on the fused representation. Finally, experiments on EMOTIC and CAER-S datasets show that our approach outperforms current state-of-the-art methods in classification accuracy. The code is available at https: //github. com/Cyx336/ICENet. git.

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EAAI Journal 2024 Journal Article

A hierarchical deep model integrating economic facts for stock movement prediction

Jiahao Yang
Ming Zhang
Shuo Feng
Xuejun Zhang
Xing Bai

Accurate stock movement prediction is essential to profit from the stock market. However, this task is challenging due to the complexity and non-stationary nature of the market. Deep learning methods have obtained more attention and success in mining price movement patterns. However, some limitations affect their performances. In general, the stock market is ever-changing, and many factors affect stock movement, so capturing the stock movement patterns is hard without enough prior information. To tackle it, we consider employing economic facts to help improve the deep learning method. In this paper, we propose a novel Hierarchical Deep learning Model that fuses Economic Facts (HDMEF) to predict stock movement from the micro to the macro tiers: the individual, industry, and whole market tiers. Specifically, we present three well-designed modules to separately model them based on the Capital Asset Pricing Model (CAPM), the herding effects, and the holiday effects in the stock market. Experiments on the A-share CSI300 and CSI500 indexes demonstrate that our proposed method performs best on all test phases compared with previous competitive baselines, even an absolute improvement of 2%–3% on some test phases where all the baselines act poor, proving our method is more efficient and robust in different market conditions. In addition, we do an ablation study to analyze the role of various economic effects used in our model, and the results prove that each module is helpful for prediction.

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IJCAI Conference 2023 Conference Paper

A New ANN-SNN Conversion Method with High Accuracy, Low Latency and Good Robustness

Bingsen Wang
Jian Cao
Jue Chen
Shuo Feng
Yuan Wang

Due to the advantages of low energy consumption, high robustness and fast inference speed, Spiking Neural Networks (SNNs), with good biological interpretability and the potential to be applied on neuromorphic hardware, are regarded as the third generation of Artificial Neural Networks (ANNs). Despite having so many advantages, the biggest challenge encountered by spiking neural networks is training difficulty caused by the non-differentiability of spike signals. ANN-SNN conversion is an effective method that solves the training difficulty by converting parameters in ANNs to those in SNNs through a specific algorithm. However, the ANN-SNN conversion method also suffers from accuracy degradation and long inference time. In this paper, we reanalyzed the relationship between Integrate-and-Fire (IF) neuron model and ReLU activation function, proposed a StepReLU activation function more suitable for SNNs under membrane potential encoding, and used it to train ANNs. Then we converted the ANNs to SNNs with extremely small conversion error and introduced leakage mechanism to the SNNs and get the final models, which have high accuracy, low latency and good robustness, and have achieved the state-of-the-art performance on various datasets such as CIFAR and ImageNet.

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JBHI Journal 2023 Journal Article

Exploring Contextual Relationships for Cervical Abnormal Cell Detection

Yixiong Liang
Shuo Feng
Qing Liu
Hulin Kuang
Jianfeng Liu
Liyan Liao
Yun Du
Jianxin Wang

Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection. Specifically, both contextual relationships between cells and cell-to-global images are exploited to enhance features of each region of interest (RoI) proposal. Accordingly, two modules, dubbed as RoI-relationship attention module (RRAM) and global RoI attention module (GRAM), are developed and their combination strategies are also investigated. We establish a strong baseline by using Double-Head Faster R-CNN with a feature pyramid network (FPN) and integrate our RRAM and GRAM into it to validate the effectiveness of the proposed modules. Experiments conducted on a large cervical cell detection dataset reveal that the introduction of RRAM and GRAM both achieves better average precision (AP) than the baseline methods. Moreover, when cascading RRAM and GRAM, our method outperforms the state-of-the-art (SOTA) methods. Furthermore, we show that the proposed feature-enhancing scheme can facilitate image- and smear-level classification.

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