Multimodal sentiment analysis (MSA) is a research field that recognizes human sentiments by combining textual, visual, and audio modalities. The main challenge lies in integrating sentiment-related information from different modalities, which typically arises during the unimodal feature extraction phase and the multimodal feature fusion phase. Existing methods extract only shallow information from unimodal features during the extraction phase, neglecting sentimental differences across different personalities. During the fusion phase, they directly merge the feature information from each modality without considering differences at the feature level. This ultimately affects the model's recognition performance. To address this problem, we propose a personality-sentiment aligned multi-level fusion framework. We introduce personality traits during the feature extraction phase and propose a novel personality-sentiment alignment method to obtain personalized sentiment embeddings from the textual modality for the first time. In the fusion phase, we introduce a novel multi-level fusion method. This method gradually integrates sentimental information from textual, visual, and audio modalities through multimodal pre-fusion and a multi-level enhanced fusion strategy. Our method has been evaluated through multiple experiments on two commonly used datasets, achieving state-of-the-art results.

MetaDesigner introduces a transformative framework for artistic typography synthesis, powered by Large Language Models (LLMs) and grounded in a user-centric design paradigm. Its foundation is a multi-agent system comprising the Pipeline, Glyph, and Texture agents, which collectively orchestrate the creation of customizable WordArt, ranging from semantic enhancements to intricate textural elements. A central feedback mechanism leverages insights from both multimodal models and user evaluations, enabling iterative refinement of design parameters. Through this iterative process, MetaDesigner dynamically adjusts hyperparameters to align with user-defined stylistic and thematic preferences, consistently delivering WordArt that excels in visual quality and contextual resonance. Empirical evaluations underscore the system's versatility and effectiveness across diverse WordArt applications, yielding outputs that are both aesthetically compelling and context-sensitive.

Deception detection has garnered increasing attention in recent years due to the significant growth of digital media and heightened ethical and security concerns. It has been extensively studied using multimodal methods, including video, audio, and text. In addition, individual differences in deception production and detection are believed to play a crucial role. Although some studies have utilized individual information such as personality traits to enhance the performance of deception detection, current systems remain limited, partly due to a lack of sufficient datasets for evaluating performance. To address this issue, we introduce a multimodal deception dataset MDPE. Besides deception features, this dataset also includes individual differences information in personality and emotional expression characteristics. It can explore the impact of individual differences on deception behavior. It comprises over 104 hours of deception and emotional videos from 193 subjects. Furthermore, we conducted numerous experiments to provide valuable insights for future deception detection research. MDPE not only supports deception detection, but also provides conditions for tasks such as personality recognition and emotion recognition, and can even study the relationships between them. We believe that MDPE will become a valuable resource for promoting research in the field of affective computing.

Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored. We introduce OmniBench, a novel benchmark designed to evaluate models’ ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define language models capable of such tri-modal processing as omni-language models (OLMs). OmniBench features high-quality human annotations that require integrated understanding across all modalities. Our evaluation reveals that: i) open-source OLMs show significant limitations in instruction-following and reasoning in tri-modal contexts; and ii) most baseline models perform poorly (below 50% accuracy) even with textual alternatives to image/audio inputs. To address these limitations, we develop OmniInstruct, an 96K-sample instruction tuning dataset for training OLMs. We advocate for developing more robust tri-modal integration techniques and training strategies to enhance OLM performance. Codes and data could be found at https://m-a-p.ai/OmniBench/.

Recently, multimodal fusion efforts have achieved remarkable success in Multimodal Sentiment Analysis (MSA). However, most of the existing methods are based on model-level fusion, and the challenge of heterogeneity between modalities is not well resolved. Heterogeneity lies in the different feature distributions and distinct representation spaces among different modalities. To mitigate this problem, we propose that fusion is a progressive process, and we introduce a novel multi-level contrastive learning and multi-layer convolution fusion (MCL-MCF) method for MSA. Due to the relationships among multimodal data, the fusion process that involves single-modal to single-modal, single-modal to bimodal or trimodal, and higher-level fused modality semantic consistency is divided into three levels. The first-level contrast learning alleviates heterogeneity between unimodal modalities at the early level of multimodal feature fusion. The second-level contrast learning mitigates heterogeneity between unimodal and fused modalities. At the third level, we introduce a tensor convolution fusion (TCF) module that extracts high-level semantic features from the fused modalities and mitigates heterogeneity at the higher feature level through contrastive learning. To simulate fusion as a progressive process, MCF is proposed to fuse shallow and deep features to model complex relationships among modalities. Experiments on three public datasets show our approach's state-of-the-art performance.

Outstanding performance in sentiment analysis not only relies on the design of sophisticated fusion methods but also on the crucial step of designing excellent modal interaction methods. To the best of our knowledge, there are few methods addressing the capture of multimodal spatial features. Majority of feature interactions have been primarily focused on temporal aspects, with less attention given to the combined spatiotemporal feature interaction (SFI). In this paper, we design a dual-view multimodal interaction method, named DVMI, primarily consisting of two parts. In the first part, a triangular convolutional module is proposed for ample temporal interaction between modalities, implicit local and global SFI, and capturing global spatial representations. Building upon the foundation laid in the first part, the second part employs an attention mechanism for explicit global SFI. To demonstrate the effectiveness of the DVMI framework,we conduct extensive experiments on three datasets, achieving state-of-the-art experimental results.

Isolating the orbital error from the interferometric synthetic aperture radar (InSAR) observations is a great challenge, especially in the presence of tectonic deformation due to their similar spatial patterns. The influence of orbital error is systematic, which can reduce the reliability of deformation monitoring. In this letter, we propose a method to isolate the orbital error from the multitemporal InSAR (MTInSAR) derived tectonic deformation based on the wavelet multiresolution analysis and independent component analysis (ICA). Starting from the sequential interferometric phase of unwrapping, the tectonic deformation and orbital error are firstly extracted from the interferometric phase by wavelet analysis based on their longwavelength spatial patterns, and ICA is then used to isolate the orbital error from the tectonic deformation according to the different temporal characteristics of the two types of signals. In the simulation experiment, the root-mean-square error (RMSE) of the isolated orbital error is 2.6 mm. Experiments with real data in Southern California show that the proposed method can successfully separate the orbital error from the tectonic deformation, and the InSAR deformation rates are in good agreement with the GPS observations.

In this paper, we describe how scene depth can be extracted using a hyperspectral light field capture (H-LF) system. Our H-LF system consists of a 5 ×6 array of cameras, with each camera sampling a different narrow band in the visible spectrum. There are two parts to extracting scene depth. The first part is our novel cross-spectral pairwise matching technique, which involves a new spectral-invariant feature descriptor and its companion matching metric we call bidirectional weighted normalized cross correlation (BWNCC). The second part, namely, H-LF stereo matching, uses a combination of spectral-dependent correspondence and defocus cues. These two new cost terms are integrated into a Markov Random Field (MRF) for disparity estimation. Experiments on synthetic and real H-LF data show that our approach can produce high-quality disparity maps. We also show that these results can be used to produce the complete plenoptic cube in addition to synthesizing all-focus and defocused color images under different sensor spectral responses.

Abstract Headsets for virtual reality such as head‐mounted displays have become ubiquitous and bring immersive experiences to individual users. People who stand outside the virtual world may want to share the same scenes that are shown on the screen of the headset. It is therefore of great importance to merge real and virtual worlds into the same environment, where physical and virtual objects exist simultaneously and interact in real time. We propose shared augmented virtual environment (SAVE), a mixed reality (MR) system that overlays the virtual world with real objects captured by a Kinect depth camera. We refine the depth map and exploit a Graphics Processing Unit (GPU) based natural image matting method to obtain the real objects from cluttered scenes. In the synthetic MR world, we can render real and virtual objects in real time and handle the depth from both worlds properly. The advantage of our system is that we connect the virtual and real worlds with a bridge controller mounted on the Kinect and need to calibrate the whole system only once before use. Our results demonstrate that the proposed SAVE system is able to create high‐quality 1080p live MR footage, enabling realistic virtual experiences to be shared among a number of people in potential applications such as education, design, and entertainment.

The complete plenoptic function records radiance of rays from every location, at every angle, for every wavelength and at every time. The signal is multi-dimensional and has long relied on multi-modal sensing such as hybrid light field camera arrays. In this paper, we present a single camera hyperspectral light field imaging solution that we call Snapshot Plenoptic Imager (SPI). SPI uses spectral coded catadioptric mirror arrays for simultaneously acquiring the spatial, angular and spectral dimensions. We further apply a learning-based approach to improve the spectral resolution from very few measurements. Specifically, we demonstrate and then employ a new spectral sparsity prior that allows the hyperspectral profiles to be sparsely represented under a pre-trained dictionary. Comprehensive experiments on synthetic and real data show that our technique is effective, reliable, and accurate. In particular, we are able to produce the first wide FoV multi-spectral light field database.

Energy is one of the most crucial issues in Wireless Sensor Networks (WSNs). Hierarchical routing protocols are best known for energy efficiency. In this paper, we propose an energy-efficient multi-hop hierarchical routing protocol which balances the load of network and reduces the cluster heads energy dissipation. According to the density of sensor nodes in monitoring area, the protocol can dynamically determine the size of the cluster. Furthermore, we propose a weight function to dynamically select the next hop appropriately and transmit data by multi-hop manner. Simulation results show that the new protocol can reduce energy consumption and obviously improve the network lifetime.

In this paper, we design a QoS Guaranteed Dynamic Bandwidth Allocation (QGDBA) scheme for a unique OFDMA-PON architecture. In the scheme, we apply statistical method to dynamically allocate bandwidth, iteration algorithm to fully utilize the frames and United-Priority to guarantee QoS of differentiated services. From the simulation results, we find the scheme can provide efficient bandwidth allocation for the next-generation OFDMA-PON.