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Lang Qin, Ziming Wang, Runhao Jiang, Rui Yan, Huajin Tang

Spiking neural networks (SNNs) are widely applied in various fields due to their energy-efficient and fast-inference capabilities. Applying SNNs to reinforcement learning (RL) can significantly reduce the computational resource requirements for agents and improve the algorithm's performance under resource-constrained conditions. However, in current spiking reinforcement learning (SRL) algorithms, the simulation results of multiple time steps can only correspond to a single-step decision in RL. This is quite different from the real temporal dynamics in the brain and also fails to fully exploit the capacity of SNNs to process temporal data. In order to address this temporal mismatch issue and further take advantage of the inherent temporal dynamics of spiking neurons, we propose a novel temporal alignment paradigm (TAP) that leverages the single-step update of spiking neurons to accumulate historical state information in RL and introduces gated units to enhance the memory capacity of spiking neurons. Experimental results show that our method can solve partially observable Markov decision processes (POMDPs) and multi-agent cooperation problems with similar performance as recurrent neural networks (RNNs) but with about 50\% power consumption.

Philippe Pasquier, Jeff Ens, Nathan Fradet, Paul Triana, Davide Rizzotti, Jean-Baptiste Rolland, Maryam Safi

We present and release MIDI-GPT, a generative system based on the Transformer architecture that is designed for computer-assisted music composition workflows. MIDI-GPT supports the infilling of musical material at the track and bar level, and can condition generation on attributes including: instrument type, musical style, note density, polyphony level, and note duration. In order to integrate these features, we employ an alternative representation for musical material, creating a time-ordered sequence of musical events for each track and concatenating several tracks into a single sequence, rather than using a single time-ordered sequence where the musical events corresponding to different tracks are interleaved. We also propose a variation of our representation allowing for expressiveness. We present experimental results that demonstrate that MIDI-GPT is able to consistently avoid duplicating the musical material it was trained on, generate music that is stylistically similar to the training dataset, and that attribute controls allow enforcing various constraints on the generated material. We also outline several real-world applications of MIDI-GPT, including collaborations with industry partners that explore the integration and evaluation of MIDI-GPT into commercial products, as well as several artistic works produced using it.

Wei Miao, Jiangrong Shen, Qi Xu, Timo Hamalainen, Yi Xu, Fengyu Cong

In recent years, with the advancements in brain science, spiking neural networks (SNNs) have garnered significant attention. SNNs can generate spikes that mimic the function of neurons transmission in humans brain, thereby significantly reducing computational costs by the event-driven nature during training. While deep SNNs have shown impressive performance on classification tasks, they still face challenges in more complex tasks such as object detection. In this paper, we propose SpikingYOLOX, extending the structure of the original YOLOX by introducing signed spiking neurons and fast Fourier convolution (FFC). The designed ternary signed spiking neurons could generate three kinds of spikes to obtain more robust features in the deep layer of the backbone. Meanwhile, we integrate FFC with SNN modules to enhance object detection performance, because its global receptive field is beneficial to the object detection task. Extensive experiments demonstrate that the proposed SpikingYOLOX achieves state-of-the-art performance among other SNN-based object detection methods.

Ryo Masumura, Shota Orihashi, Mana Ihori, Tomohiro Tanaka, Naoki Makishima, Satoshi Suzuki, Saki Mizuno, Nobukatsu Hojo

This paper presents a novel method for automatically recognizing people's apparent personality traits as perceived by others. In previous studies, apparent personality trait recognition from multimodal human behavior is often modeled to directly estimate personality trait scores, i.e., the ``Big Five'' scores. In the model training phase, ground-truth personality trait scores were often determined from personality test results scored by many other people using fine-grained questionnaires, however, rich information in the personality test results have not been leveraged for anything other than determining the ground-truth Big Five scores. The scores assigned to each questionnaire item are thought to include more meta-level differences in personality characteristics. Therefore, we propose joint modeling methods that can estimate not only the Big Five scores but also questionnaire item-level scores. This enables us to improve awareness of multimodal human behavior. In addition, we present a newly created self-introduction video dataset with 50-item Big Five questionnaire results since previous apparent personality trait recognition datasets do not provide such personality test results. Experiments using the created dataset demonstrate that our proposed joint modeling methods with a multimodal transformer backbone can improve to estimate Big Five scores and effectively estimate questionnaire item-level scores. We also verify that the estimation performance reached human evaluation performance.

Haifeng Lu, Jiuyi Chen, Feng Liang, Mingkui Tan, Runhao Zeng, Xiping Hu

Emotion recognition based on body movements is vital in human-computer interaction. However, existing emotion recognition methods predominantly focus on enhancing classification accuracy, often neglecting the provision of textual explanations to justify their classifications. In this paper, we propose an Emotion-Action Interpreter powered by LargeLanguage Model (EAI-LLM), which not only recognizes emotions but also generates textual explanations by treating 3D body movement data as unique input tokens within large language models (LLMs). Specifically, we propose a multi-granularity skeleton tokenizer designed for LLMs, which separately extracts spatio-temporal tokens and semantic tokens from the skeleton data. This approach allows LLMs to generate more nuanced classification descriptions while maintaining robust classification performance. Furthermore, we treat the skeleton sequence as a specific language and propose a unified skeleton token module. This module leverages the extensive background knowledge and language processing capabilities of LLMs to address the challenges of joint training on heterogeneous datasets, thereby significantly enhancing recognition accuracy on individual datasets. Experimental results demonstrate that our model achieves recognition accuracy comparable to existing methods. More importantly, with the support of background knowledge from LLMs, our model can generate detailed emotion descriptions based on classification results, even when trained on a limited amount of labeled skeleton data.

Yan-Kai Liu, Jinyu Cai, Bao-Liang Lu, Wei-Long Zheng

Multimodal emotion recognition is a crucial research area in the field of affective brain-computer interfaces. However, in practical applications, it is often challenging to obtain all modalities simultaneously. To deal with this problem, researchers focus on using cross-modal methods to learn multimodal representations with fewer modalities. However, due to the significant differences in the distribution of different modalities, it is challenging to enable any modality to fully learn multimodal features. To address this limitation, we propose a Multi-to-Single (M2S) emotion recognition model, leveraging contrastive learning and incorporating two innovative modules: 1) a spatial and temporal-sparse (STS) attention mechanism that enhances the encoders' ability to extract features from data; 2) a novel Multi-to-Multi Contrastive Predictive Coding (M2M CPC) that learns and fuses features across different modalities. In the final testing, we only use a single modality for emotion recognition, reducing the dependence on multimodal data. Extensive experiments on five public multimodal emotion datasets demonstrate that our model achieves the state-of-the-art performance in the cross-modal tasks and maintains multimodal performance using only a single modality.

Xiaochuan Liu, Xin Cheng, Yuchong Sun, Xiaoxue Wu, Ruihua Song, Hao Sun, Denghao Zhang

Imitating how humans move their gaze in a visual scene is a vital research problem for both visual understanding and psychology, kindling crucial applications such as building alive virtual characters. Previous studies aim to predict gaze trajectories when humans are free-viewing an image, searching for required targets, or looking for clues to answer questions in an image. While these tasks focus on visual-centric scenarios, humans move their gaze also along with audio signal inputs in more common scenarios. To fill this gap, we introduce a new task that predicts human gaze trajectories in a visual scene with synchronized audio inputs and provide a new dataset containing 20k gaze points from 8 subjects. To effectively integrate audio information and simulate the dynamic process of human gaze motion, we propose a novel learning framework called EyEar (Eye moving while Ear listening) based on physics-informed dynamics, which considers three key factors to predict gazes: eye inherent motion tendency, vision salient attraction, and audio semantic attraction. We also propose a probability density score to overcome the high individual variability of gaze trajectories, thereby improving the stabilization of optimization and the reliability of the evaluation. Experimental results show that EyEar outperforms all the baselines in the context of all evaluation metrics, thanks to the proposed components in the learning model.

Wei Liu, Li Yang, Mingxuan Zhao, Dengfeng Xue, Shuxun Wang, Boyu Cai, Jin Gao, Wenjuan Li, Bing Li, Weiming Hu

Spiking Neural Networks (SNNs) are biologically inspired models that process visual inputs over multiple time steps. However, they often struggle with limited feature discrimination along the temporal dimension due to inherent spatiotemporal invariance. This limitation arises from the redundant activation of certain regions and shared supervision for multiple time steps, constraining the network’s ability to adapt and learn diverse features. To address this challenge, we propose a novel Temporal-Self-Erasing (TSE) supervision method that dynamically adapts the learning regions of interest for different time steps. The TSE method operates by identifying highly activated regions from predictions across multiple time steps and adaptively suppressing them during model training, thereby encouraging the network to focus on less activated yet potentially informative regions. This approach not only enhances the feature discrimination capability of SNNs but also facilitates more effective multi-time-step inference by exploiting more semantic information. Experimental results on benchmark datasets demonstrate that our TSE method significantly improves the classification accuracy and robustness of SNNs.

Jinhao Lin, Yifei Wang, Yanwu Xu, Qi Liu

Despite multimodal sentiment analysis being a fertile research ground that merits further investigation, current approaches take up high annotation cost and suffer from label ambiguity, non-amicable to high-quality labeled data acquisition. Furthermore, choosing the right interactions is essential because the significance of intra- or inter-modal interactions can differ among various samples. To this end, we propose Semi-IIN, a Semi-supervised Intra-inter modal Interaction learning Network for multimodal sentiment analysis. Semi-IIN integrates masked attention and gating mechanisms, enabling effective dynamic selection after independently capturing intra- and inter-modal interactive information. Combined with the self-training approach, Semi-IIN fully utilizes the knowledge learned from unlabeled data. Experimental results on two public datasets, MOSI and MOSEI, demonstrate the effectiveness of Semi-IIN, establishing a new state-of-the-art on several metrics.

Yu Liang, Wenjie Wei, Ammar Belatreche, Honglin Cao, Zijian Zhou, Shuai Wang, Malu Zhang, Yang Yang

Binary Spiking Neural Networks (BSNNs) inherit the event-driven paradigm of SNNs, while also adopting the reduced storage burden of binarization techniques. These distinct advantages grant BSNNs lightweight and energy-efficient characteristics, rendering them ideal for deployment on resource-constrained edge devices. However, due to the binary synaptic weights and non-differentiable spike function, effectively training BSNNs remains an open question. In this paper, we conduct an in-depth analysis of the challenge for BSNN learning, namely the frequent weight sign flipping problem. To mitigate this issue, we propose an Adaptive Gradient Modulation Mechanism (AGMM), which is designed to reduce the frequency of weight sign flipping by adaptively adjusting the gradients during the learning process. The proposed AGMM can enable BSNNs to achieve faster convergence speed and higher accuracy, effectively narrowing the gap between BSNNs and their full-precision equivalents. We validate AGMM on both static and neuromorphic datasets, and results indicate that it achieves state-of-the-art results among BSNNs. This work substantially reduces storage demands and enhances SNNs' inherent energy efficiency, making them highly feasible for resource-constrained environments.

Yinan Li, Jun Long, Zhan Yang

Hashing has been widely applied in large-scale multimodal retrieval by mapping heterogeneous modalities data into binary codes. However, most cross-modal hashing methods cannot make the most of semantic information to construct the association relations of sample pairs, resulting in unsatisfactory retrieval accuracy. Concept lattice is a powerful tool for data mining and information retrieval, and for all we know, this is the first time to combine formal concept analysis and hash learning to improve cross-modal hashing retrieval performance. In this paper, we propose a novel framework for Asymmetric Cross-modal Hashing based on Formal Concept Analysis, denoted as ACHFCA. Initially, a flash-projection three-layer semantic enhancement descriptor is designed to extract latent representations from heterogeneous modalities. Subsequently, an asymmetric hash learning framework is established to enhance the semantics of different layers based on the fine-grained similarity values reconstructed from concept lattice to reinforce the discriminative competence of the model. Finally, an effective discrete optimization algorithm is proposed, which can directly learn compact hash codes. Comprehensive experiments on MIRFlickr, NUS-WIDE and IAPR-TC12 datasets demonstrate the superior performance of ACHFCA to state-of-the-art hashing approaches.

Jingmeng Li, Lukang Fu, Surun Yang, Hui Wei

Completely automated public Turing test to tell humans apart (CAPTCHA) is an effective mechanism to protect websites and online applications from malicious bots programs. Image-based CAPTCHA is one of the most widely used schemes. However, deep learning techniques have significantly weakened the security of some image-based CAPTCHA schemes. Mooney images (MIs) are important research materials in the field of cognitive science. Compared to natural images, MI exhibits fewer visual cues, fragmented content, and greater ambiguity, leading to the perception of MI relying more on the iterative process between feedforward and feedback mechanisms. In this paper, we raise an intriguing question: can MIs be used to enhance the security of CAPTCHA? Before this study, we first propose a novel framework HiMI that generates the high-quality MIs from natural images and also allows flexible adjustment of the perceived difficulty. Based on MI, we design two MI-CAPTCHA schemes related to object detection and instance segmentation tasks, respectively. We experimentally demonstrate that HiMI performs better than other baseline methods in terms of both image quality and application potential in two MI-CAPTCHA schemes. Additionally, we conduct experiments to explore the solving performance of humans and CAPTCHA solvers under different parameter settings of schemes, providing valuable reference for the practical application.

Chengtai Li, Yee Yang Tan, Yuting He, Jianfeng Ren, Ruibin Bai, Yitian Zhao, Heng Yu, Xudong Jiang

Abstract visual reasoning (AVR) is a critical ability of humans, and it has been widely studied, but arithmetic visual reasoning, a unique task in AVR to reason over number sense, is less studied in the literature. To facilitate this research, we construct a Machine Number Reasoning (MNR) dataset to assess the model's ability in arithmetic visual reasoning over number sense and spatial layouts. To solve the MNR tasks, we propose a Dual-branch Arithmetic Regression Reasoning (DARR) framework, which includes an Intra-Image Arithmetic Regression Reasoning (IIARR) module and a Cross-Image Arithmetic Regression Reasoning (CIARR) module. The IIARR includes a set of Intra-Image Regression Blocks to identify the correct number orders and the underlying arithmetic rules within individual images, and an Order Gate to determine the correct number order. The CIARR establishes the arithmetic relations across different images through a `3-to-1' regressor and a set of `2-to-1' regressors, with a Selection Gate to select the most suitable `2-to-1' regressor and a gated fusion to combine the two kinds of regressors. Experiments on the MNR dataset show that the DARR outperforms state-of-the-art models for arithmetic visual reasoning.

Zhenxin Lei, Man Yao, Jiakui Hu, Xinhao Luo, Yanye Lu, Bo Xu, Guoqi Li

Spiking Neural Networks (SNNs) have a low-power advantage but perform poorly in image segmentation tasks. The reason is that directly converting neural networks with complex architectural designs for segmentation tasks into spiking versions leads to performance degradation and non-convergence. To address this challenge, we first identify the modules in the architecture design that lead to the severe reduction in spike firing, make targeted improvements, and propose Spike2Former architecture. Second, we propose normalized integer spiking neurons to solve the training stability problem of SNNs with complex architectures. We set a new state-of-the-art for SNNs in various semantic segmentation datasets, with a significant improvement of +12.7% mIoU and 5.0x efficiency on ADE20K, +14.3% mIoU and 5.2x efficiency on VOC2012, and +9.1% mIoU and 6.6x efficiency on CityScapes.

Lucio La Cava, Andrea Tagarelli

The emergence of unveiling human-like behaviors in Large Language Models (LLMs) has led to a closer connection between NLP and human psychology. However, research on the personalities exhibited by LLMs has largely been confined to limited investigations using individual psychological tests, primarily focusing on a small number of commercially licensed LLMs. This approach overlooks the extensive use and significant advancements observed in open-source LLMs. This work aims to address both the above limitations by conducting an in-depth investigation of a significant body of 12 LLM Agents based on the most representative Open models, through the two most well-known psychological assessment tests, namely Myers-Briggs Type Indicator (MBTI) and Big Five Inventory (BFI). Our approach involves evaluating the intrinsic personality traits of LLM agents and determining the extent to which these agents can mimic human personalities when conditioned by specific personalities and roles. Our findings unveil that (i) each LLM agent showcases distinct human personalities; (ii) personality-conditioned prompting produces varying effects on the agents, with only few successfully mirroring the imposed personality, while most of them being ``closed-minded'' (i.e., they retain their intrinsic traits); and (iii) combining role and personality conditioning can enhance the agents' ability to mimic human personalities. Our work represents a step up in understanding the dense relationship between NLP and human psychology through the lens of LLMs.

Md Rysul Kabir, James Mochizuki-Freeman, Zoran Tiganj

The ability to estimate temporal relationships is critical for both animals and artificial agents. Cognitive science and neuroscience provide remarkable insights into behavioral and neural aspects of temporal credit assignment. In particular, scale invariance of learning dynamics, observed in behavior and supported by neural data, is one of the key principles that governs animal perception: proportional rescaling of temporal relationships does not alter the overall learning efficiency. Here we integrate a computational neuroscience model of scale invariant memory into deep reinforcement learning (RL) agents. We first provide a theoretical analysis and then demonstrate through experiments that such agents can learn robustly across a wide range of temporal scales, unlike agents built with commonly used recurrent memory architectures such as LSTM. This result illustrates that incorporating computational principles from neuroscience and cognitive science into deep neural networks can enhance adaptability to complex temporal dynamics, mirroring some of the core properties of human learning.

Zihan Ji, Xuetao Tian, Ye Liu

The scarcity of high-quality large-scale labeled datasets poses a huge challenge for employing deep learning models in video deception detection. To address this issue, inspired by the psychological theory on the relation between deception and expressions, we propose a novel method called AFFAKT in this paper, which enhances the classification performance by transferring useful and correlated knowledge from a large facial expression dataset. Two key challenges in knowledge transfer arise: 1) how much knowledge of facial expression data should be transferred and 2) how to effectively leverage transferred knowledge for the deception classification model during inference. Specifically, the optimal relation mapping between facial expression classes and deception samples is firstly quantified using proposed H-OTKT module and then transfers knowledge from the facial expression dataset to deception samples. Moreover, a correlation prototype within another proposed module SRKB is well designed to retain the invariant correlations between facial expression classes and deception classes through momentum updating. During inference, the transferred knowledge is fine-tuned with the correlation prototype using a sample-specific re-weighting strategy. Experimental results on two deception detection datasets demonstrate the superior performance of our proposed method. The interpretability study reveals high associations between deception and negative affections, which coincides with the theory in psychology.

Zhejing Hu, Yan Liu, Gong Chen, Bruce X.B. Yu

The field of music generation has seen a surge of interest from both academia and industry, with innovative platforms such as Suno, Udio, and SkyMusic earning widespread recognition. However, the challenge of music infilling—modifying specific music segments without reconstructing the entire piece—remains a significant hurdle for both audio-based and symbolic-based models, limiting their adaptability and practicality. In this paper, we address symbolic music infilling by introducing the Collaborative Music Inpainter (CMI), an advanced human-in-the-loop (HITL) model for music infilling. The CMI features the Joint Embedding Predictive Autoregressive Generative Architecture (JEP-AGA), which learns the high-level predictive representations of the masked part that needs to be infilled during the autoregressive generative process, akin to how humans perceive and interpret music. The newly developed Dynamic Interaction Learner (DIL) achieves HITL by iteratively refining the infilled output based on user interactions alone, significantly reducing the interaction cost without requiring further input. Experimental results confirm CMI’s superior performance in music infilling, demonstrating its efficiency in producing high-quality music.

Jinbing Hou, Youpeng Zhao, Jian Zhao

Humans excel at understanding and reasoning about novel, compositionally structured knowledge, largely due to their capacity for compositional generalization—a cognitive skill that has recently been validated in structured neural networks. However, most existing research has focused primarily on semantic translation within canonical language environments, often neglecting the explicit connection to compositional generalization behavior. In contrast, humans typically demonstrate this ability through interaction with their environments rather than solely through internal reasoning. To address this gap, we propose CraftFactory, a benchmark designed for evaluating compositional generalization in an interactive control environment. This benchmark introduces a new challenge for testing compositional generalization in a more realistic and comprehensive manner. CraftFactory stands out due to three key features: (1) it offers an open-ended interactive control environment with thousands of items and flexible actions; (2) it requires advanced compositional inference through various combinations and complex permutations of instructions; and (3) it evaluates compositional generalization intuitively through interactive behavior. By leveraging CraftFactory, we aim to promote the development of more advanced compositional generalization methods, thereby contributing to the broader field of general AI.

Xilin He, Haijian Liang, Boyi Peng, Weicheng Xie, Muhammad Haris Khan, Siyang Song, Zitong Yu

Multimodal sentiment analysis, which learns a model to process multiple modalities simultaneously and predict a sentiment value, is an important area of affective computing. Modeling sequential intra-modal information and enhancing cross-modal interactions are crucial to multimodal sentiment analysis. In this paper, we propose MSAmba, a novel hybrid Mamba-based architecture for multimodal sentiment analysis, consisting of two core blocks: Intra-Modal Sequential Mamba (ISM) block and Cross-Modal Hybrid Mamba (CHM) block, to comprehensively address the above-mentioned challenges with hybrid state space models. Firstly, the ISM block models the sequential information within each modality in a bi-directional manner with the assistance of global information. Subsequently, the CHM blocks explicitly model centralized cross-modal interaction with a hybrid combination of Mamba and attention mechanism to facilitate information fusion across modalities. Finally, joint learning of the intra-modal tokens and cross-modal tokens is utilized to predict the sentiment values. This paper serves as one of the pioneering works to unravel the outstanding performances and great research potential of Mamba-based methods in the task of multimodal sentiment analysis. Experiments on CMU-MOSI, CMU-MOSEI and CH-SIMS demonstrate the superior performance of the proposed MSAmba over prior Transformer-based and CNN-based methods.