Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity. However, existing benchmarks either lack an interactive environment or are limited to environments specific to certain applications or domains, failing to reflect the diverse and complex nature of real-world computer use, thereby limiting the scope of tasks and agent scalability. To address this issue, we introduce OSWorld, the first-of-its-kind scalable, real computer environment for multimodal agents, supporting task setup, execution-based evaluation, and interactive learning across various operating systems such as Ubuntu, Windows, and macOS. OSWorld can serve as a unified, integrated computer environment for assessing open-ended computer tasks that involve arbitrary applications. Building upon OSWorld, we create a benchmark of 369 computer tasks involving real web and desktop apps in open domains, OS file I/O, and workflows spanning multiple applications. Each task example is derived from real-world computer use cases and includes a detailed initial state setup configuration and a custom execution-based evaluation script for reliable, reproducible evaluation. Extensive evaluation of state-of-the-art LLM/VLM-based agents on OSWorld reveals significant deficiencies in their ability to serve as computer assistants. While humans can accomplish over 72.36% of the tasks, the best model achieves only 12.24% success, primarily struggling with GUI grounding and operational knowledge. Comprehensive analysis using OSWorld provides valuable insights for developing multimodal generalist agents that were not possible with previous benchmarks. Our code, environment, baseline models, and data are publicly available at [this https URL](https://os-world.github.io/).
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Neuroscience, Cognitive Science
Decoding non-invasive brain recordings is pivotal for advancing our understanding of human cognition but faces challenges due to individual differences and complex neural signal representations. Traditional methods often require customized models and extensive trials, lacking interpretability in visual reconstruction tasks. Our framework integrates 3D brain structures with visual semantics using a *Vision Transformer 3D*. This unified feature extractor efficiently aligns fMRI features with multiple levels of visual embeddings, eliminating the need for subject-specific models and allowing extraction from single-trial data. The extractor consolidates multi-level visual features into one network, simplifying integration with Large Language Models (LLMs). Additionally, we have enhanced the fMRI dataset with diverse fMRI-image-related textual data to support multimodal large model development. Integrating with LLMs enhances decoding capabilities, enabling tasks such as brain captioning, complex reasoning, concept localization, and visual reconstruction. Our approach demonstrates superior performance across these tasks, precisely identifying language-based concepts within brain signals, enhancing interpretability, and providing deeper insights into neural processes. These advances significantly broaden the applicability of non-invasive brain decoding in neuroscience and human-computer interaction, setting the stage for advanced brain-computer interfaces and cognitive models.
Human-computer interaction
Graphical User Interface (GUI) automation holds significant promise for enhancing human productivity by assisting with computer tasks. Existing task formulations primarily focus on simple tasks that can be specified by a single, language-only instruction, such as “Insert a new slide.” In this work, we introduce VideoGUI, a novel multi-modal benchmark designed to evaluate GUI assistants on visual-centric GUI tasks. Sourced from high-quality web instructional videos, our benchmark focuses on tasks involving professional and novel software (e.g., Adobe Pho- toshop or Stable Diffusion WebUI) and complex activities (e.g., video editing). VideoGUI evaluates GUI assistants through a hierarchical process, allowing for identification of the specific levels at which they may fail: (i) high-level planning: reconstruct procedural subtasks from visual conditions without language descrip- tions; (ii) middle-level planning: generate sequences of precise action narrations based on visual state (i.e., screenshot) and goals; (iii) atomic action execution: perform specific actions such as accurately clicking designated elements. For each level, we design evaluation metrics across individual dimensions to provide clear signals, such as individual performance in clicking, dragging, typing, and scrolling for atomic action execution. Our evaluation on VideoGUI reveals that even the SoTA large multimodal model GPT4o performs poorly on visual-centric GUI tasks, especially for high-level planning. The data and code are available at https://github.com/showlab/videogui.
Human-computer interaction
Large Language Model Multi-Agent Systems (LLM-MAS) have greatly progressed in solving complex tasks. It communicates among agents within the system to collaboratively solve tasks, under the premise of shared information. However, when agents' collaborations are leveraged to perform multi-person tasks, a new challenge arises due to information asymmetry, since each agent can only access the information of its human user. Previous MAS struggle to complete tasks under this condition. To address this, we propose a new MAS paradigm termed iAgents, which denotes Informative Multi-Agent Systems. In iAgents, the human social network is mirrored in the agent network, where agents proactively exchange human information necessary for task resolution, thereby overcoming information asymmetry. iAgents employs a novel agent reasoning mechanism, InfoNav, to navigate agents' communication towards effective information exchange. Together with InfoNav, iAgents organizes human information in a mixed memory to provide agents with accurate and comprehensive information for exchange. Additionally, we introduce InformativeBench, the first benchmark tailored for evaluating LLM agents' task-solving ability under information asymmetry. Experimental results show that iAgents can collaborate within a social network of 140 individuals and 588 relationships, autonomously communicate over 30 turns, and retrieve information from nearly 70,000 messages to complete tasks within 3 minutes.
Characterizing Similarities and Divergences in Conversational Tones in Humans and LLMs by Sampling with People
PDF ↗Conversational tones — the manners and attitudes in which speakers communicate — are essential to effective communication. As Large Language Models (LLMs) become increasingly popular, it is necessary to characterize the divergences in their conversational tones relative to humans. Prior research relied on pre-existing taxonomies or text corpora, which suffer from experimenter bias and may not be representative of real-world distributions. Inspired by methods from cognitive science, we propose an iterative method for simultaneously eliciting conversational tones and sentences, where participants alternate between two tasks: (1) one participant identifies the tone of a given sentence and (2) a different participant generates a sentence based on that tone. We run 50 iterations of this process with both human participants and GPT-4 and obtain a dataset of sentences and frequent conversational tones. In an additional experiment, humans and GPT-4 annotated all sentences with all tones. With data from 1,339 participants, 33,370 human judgments, and 29,900 GPT-4 queries, we show how our approach can be used to create an interpretable geometric representation of relations between tones in humans and GPT-4. This work showcases how combining ideas from machine learning and cognitive science can address challenges in human-computer interactions.
With the remarkable advancements of large language models (LLMs), LLM-based agents have become a research hotspot in human-computer interaction.However, there is a scarcity of benchmarks available for LLM-based mobile agents.Benchmarking these agents generally faces three main challenges:(1) The inefficiency of UI-only operations imposes limitations to task evaluation.(2) Specific instructions within a singular application lack adequacy for assessing the multi-dimensional reasoning and decision-making capacities of LLM mobile agents.(3) Current evaluation metrics are insufficient to accurately assess the process of sequential actions. To this end, we propose Mobile-Bench, a novel benchmark for evaluating the capabilities of LLM-based mobile agents.First, we expand conventional UI operations by incorporating 103 collected APIs to accelerate the efficiency of task completion.Subsequently, we collect evaluation data by combining real user queries with augmentation from LLMs.To better evaluate different levels of planning capabilities for mobile agents, our data is categorized into three distinct groups: SAST, SAMT, and MAMT, reflecting varying levels of task complexity. Mobile-Bench comprises 832 data entries, with more than 200 tasks specifically designed to evaluate multi-APP collaboration scenarios.Furthermore, we introduce a more accurate evaluation metric, named CheckPoint, to assess whether LLM-based mobile agents reach essential points during their planning and reasoning steps. Dataset and platform will be released in the future.
This paper introduces Instruction-oriented Object Detection (IOD), a new task that enhances human-computer interaction by enabling object detectors to understand user instructions and locate relevant objects. Unlike traditional open-vocabulary object detection tasks that rely on users providing a list of required category names, IOD requires models to comprehend natural-language instructions, contextual reasoning, and output the name and location of the desired categories. This poses fresh challenges for modern object detection systems. To develop an IOD system, we create a dataset called IOD-Bench, which consists of instruction-guided detections, along with specialized evaluation metrics. We leverage large-scale language models (LLMs) to generate a diverse set of instructions (8k+) based on existing public object detection datasets, covering a wide range of real-world scenarios. As an initial approach to the IOD task, we propose a model called Ins-DetCLIP. It harnesses the extensive knowledge within LLMs to empower the detector with instruction-following capabilities. Specifically, our Ins-DetCLIP employs a visual encoder (i.e., DetCLIP, an open-vocabulary detector) to extract object-level features. These features are then aligned with the input instructions using a cross-modal fusion module integrated into a pre-trained LLM. Experimental results conducted on IOD-Bench demonstrate that our model consistently outperforms baseline methods that directly combine LLMs with detection models. This research aims to pave the way for a more adaptable and versatile interaction paradigm in modern object detection systems, making a significant contribution to the field.
Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis. Typically, massive and expansive pixel-level annotations are spent to train deep models by object-oriented interactions with manually labeled object masks. In this work, we reveal that informative interactions can be made by simulation with semantic-consistent yet diverse region exploration in an unsupervised paradigm. Concretely, we introduce a Multi-granularity Interaction Simulation (MIS) approach to open up a promising direction for unsupervised interactive segmentation. Drawing on the high-quality dense features produced by recent self-supervised models, we propose to gradually merge patches or regions with similar features to form more extensive regions and thus, every merged region serves as a semantic-meaningful multi-granularity proposal. By randomly sampling these proposals and simulating possible interactions based on them, we provide meaningful interaction at multiple granularities to teach the model to understand interactions. Our MIS significantly outperforms non-deep learning unsupervised methods and is even comparable with some previous deep-supervised methods without any annotation.
3D hand pose estimation is a critical task in various human-computer interaction applications. Numerous deep learning based estimation models in this domain have been actively explored. However, the existing models follows a non-recurrent scheme and thus require complex architectures or redundant parameters in order to achieve acceptable model capacity. To tackle this limitation, this paper proposes HandR2N2, a compact neural network that iteratively regresses the hand pose using a novel residual recurrent unit. The recurrent design allows recursive exploitation of partial layers to gradually optimize previously estimated joint locations. In addition, we exploit graph reasoning to capture kinematic dependencies between joints for better performance. Experimental results show that the proposed model significantly outperforms the existing methods on three hand pose benchmark datasets in terms of both accuracy and efficiency. Codes and pre-trained models are publicly available at https://github.com/cwc1260/HandR2N2.
Multimodal emotion recognition has become an important research topic due to its wide applications in human-computer interaction. Over the last few decades, the technology has made remarkable progress with the development of deep learning. However, existing technologies are hard to meet the demand for practical applications. To this end, we organize this workshop to bring together researchers in this field to further discuss recent research and future directions.
Real-time engagement estimation holds significant potential across various research areas, particularly in the realm of human-computer interaction. It empowers artificial agents to dynamically adjust their responses based on user engagement levels, fostering more intuitive and immersive interactions. Despite the strides in automating real-time engagement estimation, the task remains challenging in real-world settings, especially when handling multi-modal human social signals. Capitalizing on human body and audio signals, this paper explores the appropriate feature representations of different modalities and effective modelling of dual conversations. This results in a novel and efficient multi-modal engagement detection model.We thoroughly evaluated our method in the MultiMediate'23 grand challenge. It performs consistently, with a notable improvement over the baseline model. Specifically, while the baseline achieves a concordance correlation coefficient (CCC) of 0.59, our approach yields a CCC of 0.70, suggesting its promising efficacy in real-life engagement detection.
The task of interval localization of macro- and micro-expression in long videos has a wide range of applications in the field of human-computer interaction. Compared with macro-expression, micro-expression has shorter duration, lower intensity, and smaller number of samples, which make them more difficult to spot accurately in long videos. In this paper, we propose a pre-trained model combined with the optical flow method to improve the accuracy and robustness of macro- and micro-expression spotting. Firstly, self-supervised pre-training is performed on rich unlabeled data based on VideoMAE. Then, multiple models are trained on the datasets SAMM-LV and CAS(ME)³ for macro- and micro-expression with different fine-grains. Finally, different lengths of slices are generated based on the models with different fine-grains, and the optimal matching method through the combination of model fine-grainedness and slice lengths is explored. At the same time, macro- and micro-expression generating regions were spotted using the optical flow method, fused with the model outputs to supplement the spatio-temporal information not captured by the model and to exclude the interference of non-interested regions. We evaluated the performance of our method on the MEGC2023 testset (consisting of 10 long videos from SAMM and 20 long videos from CAS(ME)3) and won first place in the MEGC2023 Challenge. The results demonstrate the effectiveness of the method.
Offline Multiple Appropriate Facial Reaction Generation (OMAFRG) aims to predict the reaction of different listeners given a speaker, which is useful in the senario of human-computer interaction and social media analysis. In recent years, the Offline Facial Reactions Generation (OFRG) task has been explored in different ways. However, most studies only focus on the deterministic reaction of the listeners. The research of the non-deterministic (i.e. OMAFRG) always lacks of sufficient attention and the results are far from satisfactory. Compared with the deterministic OFRG tasks, the OMAFRG task is closer to the true circumstance but corresponds to higher difficulty for its requirement of modeling stochasticity and context. In this paper, we propose a new model named FRDiff to tackle this issue. Our model is developed based on the diffusion model architecture with some modification to enhance its ability of aggregating the context features. And the inherent property of stochasticity in diffusion model enables our model to generate multiple reactions. We conduct experiments on the datasets provided by the ACM Multimedia REACT2023 and obtain the second place on the board, which demonstrates the effectiveness of our method.
Automatic emotion recognition has a wide range of applications in human-computer interaction. In this paper, we present our work in the Multimodel Emotion Recognition (MER) 2023, which contains three sub-challenges: MER-MULTI, MER-NOISE, and MER-SEMI. We first use a vanilla semi-supervised method to mine high quality samples from the MER-SEMI unlabeled dataset to expand the training set. Specifically, we ensemble three models trained with the official training set by a majority voting method, which is used to select samples with high prediction consistency. The selected samples together with the original training set are further augmented by adding noise. Then, the features of different modalities of expanded dataset are extracted from several pre-trained or fine-tuned models, and they are subsequently used to create different feature combinations to capture more effective emotion representations. Besides, we employ early fusion of different modal features and late fusion of different recognition models to obtain the final prediction. Experimental results show that our proposed method improves the performance over the official baselines by 30.4%, 55.3% and 1.57% for the three sub-challenges and ranks 4, 3, and 5, respectively. The present work sheds light on high-quality data mining and model ensemble by majority voting for multimodal emotion recognition.
Understanding and elucidating human behavior across diverse scenarios represents a pivotal research challenge in pursuing seamless human-computer interaction. However, previous research on multi-participant dialogues has mostly relied on proprietary datasets, which are not standardized and openly accessible. To propel advancements in this domain, the MultiMediate'23 Challenge presents two sub-challenges: Eye contact detection and Next speaker prediction, aiming to foster a comprehensive understanding of multi-participant behavior. To tackle these challenges, we propose a multi-scale conformer fusion network (MSCFN) for enhancing the perception of multi-participant group behaviors. The conformer block combines the strengths of transformers and convolution networks to facilitate the establishment of global and local contextual relationships between sequences. Then the output features from all Conformer blocks are concatenated to fusion multi-scale representations. Our proposed method was evaluated using the officially provided dataset, and it achieves the best and second best performance in next speaker prediction and gaze detection tasks of MultiMediate'23, respectively.
Layout generation is important in the field of graphic design and has attracted intensive research attention recently. To further prompt human-computer interactions, we construct the ALDA to assist users throughout the design process, which achieves adaptive and diverse content expansions upon only one element for beginners and generates high-quality posters subsequently. Specifically, to obtain diverse contents, we propose aesthetic-aware design graphs (AGs) for effective poster representation and propose a self-constrained blending strategy upon related examples. In addition, we build a novel layout generator to better arrange elements conditioned on our AGs. Finally, we implement ALDA as an online tool with a set of controllable factors to enhance its practicality.
The traditional controllable face generation refers to the controllability of coarse-grained ranges such as facial features, expression postures, or viewing angles, but specific application scenarios require finer-grained control. This paper proposes a fine-grained and controllable face generation technology, CFTF. CFTF allows users to participate deeply in the face generation process through multiple rounds of language feedback. It not only enables control over coarse-grained features such as gender and viewing angle, but also provides flexible control over details such as hair color, accessories, and iris color. We apply CFTF to the suspect portrait scene, and perform multiple rounds of human-computer interaction based on the eyewitness's painting sketch of the suspect and descriptions of their facial features, realizing the "Human-in-the-loop" collaborative portrait drawing.
Evaluating speaker emotion in conversations is crucial for various applications requiring human-computer interaction. However, co-occurrences of multiple emotional states (e.g. 'anger' and 'frustration' may occur together or one may influence the occurrence of the other) and their dynamic evolution may vary dramatically due to the speaker's internal (e.g., influence of their personalized socio-cultural-educational and demographic backgrounds) and external contexts. Thus far, the previous focus has been on evaluating only the dominant emotion observed in a speaker at a given time, which is susceptible to producing misleading classification decisions for difficult multi-labels during testing. In this work, we present Self-supervised Multi- Label Peer Collaborative Distillation (SeMuL-PCD) Learning via an efficient Multimodal Transformer Network, in which complementary feedback from multiple mode-specific peer networks (e.g.transcript, audio, visual) are distilled into a single mode-ensembled fusion network for estimating multiple emotions simultaneously. The proposed Multimodal Distillation Loss calibrates the fusion network by minimizing the Kullback-Leibler divergence with the peer networks. Additionally, each peer network is conditioned using a self-supervised contrastive objective to improve the generalization across diverse socio-demographic speaker backgrounds. By enabling peer collaborative learning that allows each network to independently learn their mode-specific discriminative patterns,SeMUL-PCD is effective across different conversation environments. In particular, the model not only outperforms the current state-of-the-art models on several large-scale public datasets (e.g., MOSEI, EmoReact and ElderReact), but with around 17% improved weighted F1-score in the cross-dataset experimental settings. The model also demonstrates an impressive generalization ability across age and demography-diverse populations.
Facial expression recognition (FER) plays a critical role in human-computer interaction and affective computing. Traditional FER methods typically rely on comparing the difference between an examined facial expression and a neutral face of the same person to extract the motion of facial features and filter out expression-irrelevant information. With the extensive use of deep learning, the performance of FER has been further improved. However, existing deep learning-based methods rarely utilize neutral faces. To address this gap, we propose a novel deep learning-based FER method called Generative Neutral Features-Disentangled Learning (GNDL), which draws inspiration from the facial feature manifold. Our approach integrates a neutral feature generator (NFG) that generates neutral features in scenarios where the neutral face of the same subject is not available. The NFG uses fine-grained features from examined images as input and produces corresponding neutral features with the same identity. We train the NFG using a neutral feature reconstruction loss to ensure that the generative neutral features are consistent with the actual neutral features. We then disentangle the generative neutral features from the examined features to remove disturbance features and generate an expression deviation embedding for classification. Extensitive experimental results on three popular databases (CK+, Oulu-CASIA, and MMI) demonstrate that our proposed GNDL method outperforms state-of-the-art FER methods.
Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition
PDF ↗With the recent surge in the use of touchscreen devices, free-hand sketching has emerged as a promising modality for human-computer interaction. While previous research has focused on tasks such as recognition, retrieval, and generation of familiar everyday objects, this study aims to create a Sketch Input Method Editor (SketchIME) specifically designed for a professional Command, Control, Communications, Computer, and Intelligence (C4I) system. Within this system, sketches are utilized as low-fidelity prototypes for recommending standardized symbols in the creation of comprehensive situation maps. This paper also presents a systematic dataset comprising 374 specialized sketch types, and proposes a simultaneous recognition and segmentation architecture with multilevel supervision between recognition and segmentation to improve performance and enhance interpretability. By incorporating few-shot domain adaptation and class-incremental learning, the network's ability to adapt to new users and extend to new task-specific classes is significantly enhanced. Results from experiments conducted on both the proposed dataset and the SPG dataset illustrate the superior performance of the proposed architecture. Our dataset and code are publicly available at https://github.com/GuangmingZhu/SketchIME.