ACM MM 2024 Papers

The growing prevalence of psychological disorders underscores the critical importance of mental health research in today's society. In psychotherapy, particularly Acceptance and Commitment Therapy (ACT), cognitive exercises employing mental imagery are used to manage negative thoughts. However, the challenge of maintaining vivid imagery diminishes their therapeutic effectiveness. Virtual reality (VR) offers untapped potential for increasing engagement and therapeutic efficacy. However, there is still a gap in exploration regarding how to effectively leverage the potential of VR to enhance traditional cognitive exercises with mental imagery. This study investigates the effective HCI design and the comparative efficacy of a VR-mediated exercise for promoting cognitive defusion to address negative thoughts grounded in ACT. Using a co-design approach with clinicians and potential users of postgraduate students, we developed a VR system that materializes negative thoughts into tangible objects. This allows users to visually modify and transpose these objects onto a surface, facilitating mental detachment from negative thoughts. In an evaluation study with 20 non-clinical participants, divided into VR and mental imagery groups, we assessed the impact of the cognitive defusion exercise on their perception of negative thoughts and psychological measures using standardized questionnaires. Results show improvement in both groups, with significant enhancements in negative thought perception and mental detachment from negative thoughts exclusively in the VR group, whereas the mental imagery group did not demonstrate significant changes. Interviews emphasize the VR's capability to present vivid visualizations of negative thoughts effortlessly, highlighting its effectiveness and engagement in psychotherapy to facilitate cognitive exercises.

Multi-view clustering has proven to be highly effective in exploring consistency information across multiple views/modalities when dealing with large-scale unlabeled data. However, in the real world, multi-view data is often distributed across multiple entities, and due to privacy concerns, federated multi-view clustering solutions have emerged. Existing federated multi-view clustering algorithms often result in misalignment in feature representations among clients, difficulty in integrating information across multiple views, and poor performance in heterogeneous scenarios. To address these challenges, we propose HFMVC, a heterogeneity-aware federated deep multi-view clustering method. Specifically, HFMVC adaptively perceives the degree of heterogeneity in the environment and employs contrastive learning to explore consistency and complementarity information across clients' multi-view data. Besides, we seek consensus among clients where local data originates from the same view, incorporating a contrastive loss between local models and the global model during local training to adjust consistency among local models. Furthermore, we elucidate the sample representation logic for local clustering in different heterogeneous environments, identifying the degree of heterogeneity by computing the within-cluster sum of squares (WCSS) and the average inter-cluster distance (AICD). Extensive experiments verify the superior performance of HFMVC across both IID and Non-IID settings.

Novel View Synthesis plays a crucial role by generating new 2D renderings from multi-view images of 3D scenes. However, capturing high-speed scenes with conventional cameras often leads to motion blur, hindering the effectiveness of 3D reconstruction. To address this challenge, high-frame-rate dense 3D reconstruction emerges as a vital technique, enabling detailed and accurate modeling of real-world objects or scenes in various fields, including Virtual Reality or embodied AI. Spike cameras, a novel type of neuromorphic sensor, continuously record scenes with an ultra-high temporal resolution, showing potential for accurate 3D reconstruction. Despite their promise, existing approaches, such as applying Neural Radiance Fields (NeRF) to spike cameras, encounter challenges due to the time-consuming rendering process. To address this issue, we make the first attempt to introduce the 3D Gaussian Splatting (3DGS) into spike cameras in high-speed capture, providing 3DGS as dense and continuous clues of views, then constructing SpikeGS. Specifically, to train SpikeGS, we establish computational equations between the rendering process of 3DGS and the processes of instantaneous imaging and exposing-like imaging of the continuous spike stream. Besides, we build a very lightweight but effective mapping process from spikes to instant images to support training. Furthermore, we introduced a new spike-based 3D rendering dataset for validation. Extensive experiments have demonstrated our method possesses the high quality of novel view rendering, proving the tremendous potential of spike cameras in modeling 3D scenes.

Point cloud analysis is challenging due to its unique characteristics of unorderness, sparsity and irregularity. Prior works attempt to capture local relationships by convolution operations or attention mechanisms, exploiting geometric information from coordinates implicitly. These methods, however, are insufficient to describe the explicit local geometry, e.g., curvature and orientation. In this paper, we propose On-the-fly Point Feature Representation (OPFR), which captures abundant geometric information explicitly through Curve Feature Generator module. This is inspired by Point Feature Histogram (PFH) from computer vision community. However, the utilization of vanilla PFH encounters great difficulties when applied to large datasets and dense point clouds, as it demands considerable time for feature generation. In contrast, we introduce the Local Reference Constructor module, which approximates the local coordinate systems based on triangle sets. Owing to this, our OPFR only requires extra 1.56ms for inference (65X faster than vanilla PFH) and 0.012M more parameters, and it can serve as a versatile plug-and-play module for various backbones, particularly MLP-based and Transformer-based backbones examined in this study. Additionally, we introduce the novel Hierarchical Sampling module aimed at enhancing the quality of triangle sets, thereby ensuring robustness of the obtained geometric features. Our proposed method improves overall accuracy (OA) on ModelNet40 from 90.7% to 94.5% (+3.8%) for classification, and OA on S3DIS Area-5 from 86.4% to 90.0% (+3.6%) for semantic segmentation, respectively, building upon PointNet++ backbone. When integrated with Point Transformer backbone, we achieve state-of-the-art results on both tasks: 94.8% OA on ModelNet40 and 91.7% OA on S3DIS Area-5.

Thanks to the powerful generative capacity of diffusion models, recent years have witnessed rapid progress in human motion generation. Existing diffusion-based methods employ disparate network architectures and training strategies. The effect of the design of each component is still unclear. In addition, the iterative denoising process consumes considerable computational overhead, which is prohibitive for real-time scenarios such as virtual characters and humanoid robots. For this reason, we first conduct a comprehensive investigation into network architectures, training strategies, and inference process. Based on the profound analysis, we tailor each component for efficient high-quality human motion generation. Despite the promising performance, the tailored model still suffers from foot skating which is an ubiquitous issue in diffusion-based solutions. To eliminate footskate, we identify foot-ground contact and correct foot motions along the denoising process. By organically combining these well-designed components together, we present StableMoFusion, a robust and efficient framework for human motion generation. Extensive experimental results show that our StableMoFusion performs favorably against current state-of-the-art methods.

The emergence of virtual reality technology has made stereoscopic omnidirectional images (SOI) easily accessible and prompted the need to evaluate their perceptual quality. At present, most stereoscopic omnidirectional image quality assessment (SOIQA) methods rely on one of the projection formats, i.e., Equirectangular Projection (ERP) or CubeMap Projection (CMP). However, while ERP provides global information and the less distorted CMP complements it by providing local structural guidance, research on leveraging both ERP and CMP in SOIQA remains limited, hindering a comprehensive understanding of both global and local visual cues. Motivated by this gap, our study introduces a novel dual-stream perception-driven network for blind quality assessment of stereoscopic omnidirectional images. By integrating both ERP and CMP, our method effectively captures both global and local information, marking the first attempt to bridge this gap in SOIQA, particularly through deep learning methodologies. We employ an inter-intra feature fusion module, which considers both the inter-complementarity between ERP and CMP and the intra-relationships within CMP images. This module dynamically and complementarily adjusts the contributions of features from both projections and effectively integrates them to achieve a more comprehensive perception. Besides, deformable convolution is employed to extract the local region of interest, simulating the orientation selectivity of the primary visual cortex. Finally, with the features of left and right views of SOI, a stereo cross attention module that simulates the binocular fusion mechanism is proposed to predict the quality score. Extensive experiments are conducted to evaluate our model and the state-of-the-art competitors, demonstrating that our model has achieved the best performance on the databases of LIVE 3D VR, SOLID, and NBU.

Video moment localization (VML) aims to identify the temporal boundary semantically matching the given query. Point-supervised VML balances localization accuracy and annotation cost but is still immature due to granularity alignment and scale perception issues. To this end, we propose a Semantic Granularity and Scale Correspondence Integration (SG-SCI) framework aimed at leveraging limited single-frame annotation for correspondence learning. It explicitly models semantic relations of different feature granularities and adaptively mines the implicit semantic scale, thereby enhancing feature representations of varying granularities and scales. SG-SCI uses granularity correspondence alignment to align semantics via latent prior knowledge and a scale correspondence learning to identify and address semantic scale differences. Extensive experiments on benchmark datasets have demonstrated the promising performance of our model over several state-of-the-art competitors.

Subject-driven image generation, aimed at customizing user-specified subjects, has experienced rapid progress. However, most of them focus on transferring the customized appearance of subjects. In this work, we consider a novel concept customization task, that is, capturing the interaction between subjects in exemplar images and transferring the learned concept of interaction to achieve customized text-to-image generation. Intrinsically, the interaction between subjects is diverse and is difficult to describe in only a few words. In addition, typical exemplar images are about the interaction between humans, which further intensifies the challenge of interaction-driven image generation with various categories of subjects. To address this task, we adopt a divide-and-conquer strategy and propose a two-stage interaction inversion framework. The framework begins by learning a pseudo-word for a single pose of each subject in the interaction. This is then employed to promote the learning of the concept for the interaction. In addition, language prior and cross-attention loss are incorporated into the optimization process to encourage the modeling of interaction. Extensive experiments demonstrate that the proposed methods are able to effectively invert the interactive pose from exemplar images and apply it to the customized generation with user-specified interaction.

Multi-modal fusion techniques, such as radar and images, enable a complementary and cost-effective perception of the surrounding environment regardless of lighting and weather conditions. However, existing fusion methods for surround-view images and radar are challenged by the inherent noise and positional ambiguity of radar, which leads to significant performance losses. To address this limitation effectively, our paper presents a robust, end-to-end fusion framework dubbed SparseInteraction. First, we introduce the Noisy Radar Filter (NRF) module to extract foreground features by creatively using queried semantic features from the image to filter out noisy radar features. Furthermore, we implement the Sparse Cross-Attention Encoder (SCAE) to effectively blend foreground radar features and image features to address positional ambiguity issues at a sparse level. Ultimately, to facilitate model convergence and performance, the foreground prior queries containing position information of the foreground radar are concatenated with predefined queries and fed into the subsequent transformer-based decoder. The experimental results demonstrate that the proposed fusion strategies markedly enhance detection performance and achieve new state-of-the-art results on the nuScenes benchmark. Source code is available at https://github.com/GG-Bonds/SparseInteraction.

Visual question answering aims to provide responses to questions given visual input. Recently, visual programmatic models (VPMs), which generate programs to answer questions through large language models (LLMs), have attracted attention. However, they often require long input prompts to provide the LLM with sufficient API usage details to generate relevant code. To address this limitation, we propose AdaCoder, an adaptive prompt compression framework for VPMs. AdaCoder operates in two phases: a compression phase and an inference phase. In the compression phase, given a preprompt that describes all API definitions with example code snippets, a set of compressed preprompts is generated, each depending on a specific question type. In the inference phase, AdaCoder predicts the question type and chooses the appropriate corresponding compressed preprompt to generate code to answer the question. In experiments, we apply AdaCoder to ViperGPT and demonstrate that it reduces token length by 71.1%, while maintaining or even improving the performance of visual question answering.

Deep steganography is a technique that imperceptibly hides secret information into image by neural networks. Existing networks consist of two components, including a hiding component for information hiding and an adversary component for countering against steganalyzers. However, these two components are two ends of the seesaw, and it is difficult to balance the tradeoff between message extraction accuracy and security performance by joint optimization. To address the issues, this paper proposes a steganographic method called AHDeS (Adversary-Hiding-Decoupled Steganography) under the Dig-and-Fill paradigm, wherein the adversary and hiding components can be decoupled into an optimization-based adversary module in the digging process and an INN-based hiding network in the filling process. Specfically in the training stage, the INN is first trained for acquiring the ability of message embedding. In the deployment stage, given the well-trained and fixed INN, the cover image is first iteratively optimized for enhancing the security performance against steganalyzers, followed by the actual message embedding by the INN. Owing to the reversibility of the INN, security performance can be enhanced without sacrificing message extraction accuracy. Experimental results show that AHDeS can achieve the state-of-the-art security performance and visual quality while maintaining satisfied message extraction accuracy.

Information diffusion prediction aims to forecast the path of information spreading in social networks by exploiting user correlations or preferences. Recent works focus on characterizing the dynamic of user preferences and propose to capture users' dynamic preferences by discretizing the diffusion process into structure snapshots. Despite their effectiveness, these works simply summarize users' dynamic preferences from partially observed structure snapshots, ignoring the continuous evolution of the preferences. Moreover, discretizing the diffusion process makes these models overlook abundant structure information across different periods, reducing their ability to discover potential participants. To address the above issues, we propose a novel Graph Neural Ordinary Differential Equation Network (GODEN) for information diffusion prediction, which incorporates neural ordinary differential equations (ODE) to model the continuous dynamics of the diffusion process. Specifically, we design two coupled ODE functions on nodes and edges to describe their co-evolution dynamics and infer users' dynamic preferences based on the solution of ODEs. To predict the future infections of the observed cascade, we represent its diffusion pattern in terms of temporal and user contexts and apply a multi-head attention module to attend to different contexts. Experimental results confirm our approach's effectiveness, with our model outperforming the state-of-the-art diffusion prediction models.

Large-scale pre-trained audio-language models excel in general multi-modal representation, facilitating their adaptation to downstream audio recognition tasks in a data-efficient manner. However, existing few-shot audio recognition methods based on audio-language models primarily focus on learning coarse-grained correlations, which are not sufficient to capture the intricate matching patterns between the multi-level information of audio and the diverse characteristics of category concepts. To address this gap, we propose multi-grained correspondence learning for bootstrapping audio-language models to improve audio recognition with few training samples. This approach leverages generative models to enrich multi-modal representation learning, mining the multi-level information of audio alongside the diverse characteristics of category concepts. Multi-grained matching patterns are then established through multi-grained key-value cache and multi-grained cross-modal contrast, enhancing the alignment between audio and category concepts. Additionally, we incorporate optimal transport to tackle temporal misalignment and semantic intersection issues in fine-grained correspondence learning, enabling flexible fine-grained matching. Our method achieves state-of-the-art results on multiple benchmark datasets for few-shot audio recognition, with comprehensive ablation experiments validating its effectiveness.

Semi-supervised medical image segmentation has gained increasing attention due to its potential to alleviate the manual annotation burden. Mainstream methods typically involve two subnets, and conduct a consistency objective to ensure them producing consistent predictions for unlabeled data. However, they often ignore that the complementarity of model predictions is equally crucial. To realize the potential of the multi-subnet architecture, we propose a novel cross-view mutual learning method with a two-branch co-training framework. Specifically, we first introduce a novel conflict-based feature learning (CFL) that encourages the two subnets to learn distinct features from the same input. These distinct features are then decoded into complementary model predictions, allowing both subnets to understand the input from different views. More importantly, we propose a cross-view mutual learning (CML) to maximize the effectiveness of CFL. This approach requires only modifications to the model inputs and supervisory signals, and implements a heterogeneous consistency objective to fully explore the complementarity of model predictions. Consequently, the aggregated predictions can effectively capture both consistency and complementarity across two subnets. Experimental results on three public datasets demonstrate the superiority of CML over previous SoTA methods. Code is available at https://github.com/SongwuJob/CML.

Online chatting has become an essential aspect of our daily interactions, with stickers emerging as a prevalent tool for conveying emotions more vividly than plain text. While conventional image emotion recognition focuses on global features, sticker emotion recognition necessitates incorporating both global and local features, along with additional modalities like text. To address this, we introduce a topic ID-guided transformer method to facilitate a more nuanced analysis of the stickers. Since each sticker will have a topic, and the same topic will have the same object, we introduce a topic ID as a flag to group images by theme. Our approach encompasses a novel topic-guided context-aware module and a topic-guided attention mechanism, enabling the extraction of comprehensive topic context features from stickers sharing the same topic ID, significantly enhancing emotion recognition accuracy. Moreover, we integrate a frequency linear attention module to leverage frequency domain information to capture better the object information of the stickers and a locally enhanced re-attention mechanism for improved local feature extraction. Extensive experiments and ablation studies on the large-scale sticker emotion dataset SER30k validate the efficacy of our method. Experimental results show that our proposed method obtains the best accuracy on both single-modal and multi-modal sticker emotion recognition.

Event extraction (EE) is a critical direction in the field of information extraction, laying an important foundation for the construction of structured knowledge bases. EE from text has received ample research and attention for years, yet there can be numerous real-world applications that require direct information acquisition from speech signals, online meeting minutes, interview summaries, press releases, etc. While EE from speech has remained under-explored, this paper fills the gap by pioneering a SpeechEE, defined as detecting the event predicates and arguments from a given audio speech. To benchmark the SpeechEE task, we first construct a large-scale high-quality dataset. Based on textual EE datasets under the sentence, document, and dialogue scenarios, we convert texts into speeches through both manual real-person narration and automatic synthesis, empowering the data with diverse scenarios, languages, domains, ambiences, and speaker styles. Further, to effectively address the key challenges in the task, we tailor an E2E SpeechEE system based on the encoder-decoder architecture, where a novel Shrinking Unit module and a retrieval-aided decoding mechanism are devised. Extensive experimental results on all SpeechEE subsets demonstrate the efficacy of the proposed model, offering a strong baseline for the task. At last, being the first work on this topic, we shed light on key directions for future research. Our codes and the benchmark datasets are open at https://SpeechEE.github.io/

Neural Radiance Fields (NeRF) achieves impressive 3D representation learning and novel view synthesis results with high-quality multi-view images as input. However, motion blur in images often occurs in low-light and high-speed motion scenes, which significantly degrades the reconstruction quality of NeRF. Previous deblurring NeRF methods struggle to estimate pose and lighting changes during the exposure time, making them unable to accurately model the motion blur. The bio-inspired event camera measuring intensity changes with high temporal resolution makes up this information deficiency. In this paper, we propose Event-driven Bundle Adjustment for Deblurring Neural Radiance Fields (EBAD-NeRF) to jointly optimize the learnable poses and NeRF parameters by leveraging the hybrid event-RGB data. An intensity-change-metric event loss and a photo-metric blur loss are introduced to strengthen the explicit modeling of camera motion blur. Experiments on both synthetic and real-captured data demonstrate that EBAD-NeRF can obtain accurate camera trajectory during the exposure time and learn a sharper 3D representations compared to prior works.

Text-driven 3D avatar customization has attracted increasing attention in recent years, where precisely editing specific local parts of avatars with only text prompts is particularly challenging. Previous editing methods usually use segmentation or cross-attention masks as constraints for local editing. Although these masks tightly cover existing objects/parts, they may limit editing methods to create drastic geometry deformations beyond the covered contents. From a different perspective, this paper presents a GPT-guided local avatar editing framework, namely GG-Editor. Specifically, GG-Editor progressively mines more reasonable candidate editing regions via harnessing multimodal large language models which already organically assimilate common-sense human knowledge. In order to improve the editing quality of the local areas, GG-Editor explicitly decouples the geometry/appearance optimization, and adopts a global-local synergy editing strategy with GPT-generated local prompts. Moreover, to preserve concepts residing in source avatars, GG-Editor proposes an orthogonal denoising score that orthogonally decomposes editing directions and introduce an explicit term for preservation. Comprehensive experiments demonstrate that GG-Editor with only textual prompts achieves realistic and high-fidelity local editing results, significantly surpassing prior works. Project page: https://xuyunqiu.github.io/GG-Editor/.