United States

Supervised multimodal classification has been proven to outperform unimodal classification in the image-text domain. However, this task highly relies on abundant labeled data. To perform multimodal classification in data-insufficient scenarios, in this study, we explore semi-supervised multimodal classification (SSMC) that only requires a small amount of labeled data and plenty of unlabeled data. 
Specifically, we first design baseline SSMC models by combining known semi-supervised pseudo-labeling methods with the two most commonly used modal fusion strategies, i.e. feature-level fusion and label-level aggregation. Based on our investigation and empirical study of the baselines, we discover two complementarities that may benefit SSMC if properly exploited: the predictions from different modalities (modal complementarity) and modal fusion strategies for pseudo-labeling (strategic complementarity). Therefore, we propose a learning from Modal and Strategic Complementarity (MSC) framework for SSMC. Concretely, to exploit modal complementarity, we propose to learn reliability weights to weigh the predictions from different modalities and refine the fusion scores. To learn from strategic complementarity, we introduce a dual KL divergence loss to guide the balance of quantity and quality of pseudo-labeled data selection. Extensive empirical studies demonstrate the effectiveness of the proposed framework.

AAAI 2025

Semi-Supervised Multimodal Classification Through Learning from Modal and Strategic Complementarities

semi-supervised learning

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Oriented object detection is crucial for complex scenes such as aerial images and industrial inspection, providing precise delineation by minimizing background interference. Recently, the weakly-supervised detector paradigm H2RBox has demonstrated promise in learning rotated bounding box (RBox) from the more readily available horizontal bounding box (HBox), alleviating the scarcity and high cost of RBox annotations. However, these H2RBox-based methods have primarily focused on the gap in orientation information between HBox-supervised and RBox-supervised approaches, overlooking the gap in training sample selection. In response, we propose the Adaptive Fine-grained Sample Mining (AFSM) strategy, which improves the selection of fine-grained training samples in HBox-supervised methods. AFSM assigns the best-matching prediction RBox to each ground truth (GT) HBox and selects positive samples based on these paired boxes. Furthermore, to effectively filter the best-matching prediction RBox for AFSM, we introduce the Prediction Rbox Assignment (PRA) scheme, employing Kullback-Leibler Divergence (KLD) as a localization quality metric. Additionally, we introduce an improved self-supervised branch loss ($L_{ss^*}$) to address the symmetry of weakly-supervised branch prediction boxes. Incorporating these core components (AFSM, PRA, and $L_{ss^*}$), we develop an end-to-end network architecture (BGHR) to further bridge the gap between HBox- and RBox-supervised oriented object detection. Extensive experiments on DOTA-v1.0 and DIOR-R demonstrate that BGHR achieves state-of-the-art performance compared to HBox-supervised methods without additional overhead. Even when benchmarked against fully supervised FCOS, our method still exhibits a slight performance advantage.

BGHR: Bridging the Gap Between HBox-Supervised and RBox-Supervised Oriented Object Detection via Adaptive Fine-Grained Sample Mining

Text-driven Human-Object Interaction (Text-to-HOI) generation is an emerging field with applications in animation, video games, virtual reality, and robotics. A key challenge in HOI generation is maintaining interaction consistency in long sequences. Existing Text-to-Motion-based approaches, such as discrete motion tokenization, cannot be directly applied to HOI generation due to limited data in this domain and the complexity of the modality. To address the problem of interaction consistency in long sequences, we propose an autoregressive diffusion model (ARDHOI) that predicts the next continuous token. Specifically, we introduce a Contrastive Variational Autoencoder (cVAE) to learn a physically plausible space of continuous HOI tokens, thereby ensuring that generated human-object motions are realistic and natural. For generating sequences autoregressively, we develop a Mamba-based context encoder to capture and maintain consistent sequential actions. Additionally, we implement an MLP-based denoiser to generate the subsequent token conditioned on the encoded context. Our model has been evaluated on the OMOMO and BEHAVE datasets, where it outperforms existing state-of-the-art methods in terms of both performance and inference speed. This makes ARDHOI a robust and efficient solution for text-driven HOI tasks. We will make the model weights and source code publicly available.

Auto-Regressive Diffusion for Generating 3D Human-Object Interactions

The application of solutions to time-varying problems is crucial for predicting changes in human body shape over time. While recurrent neural networks have made significant strides in this area, their reliance on centralized processing leads to challenges such as model silos and data isolation. Consequently, distributed artificial intelligence systems, such as federated learning, have emerged to facilitate dynamic collaboration among different models; however, they still depend on a central server, posing significant risks to system security and efficiency. Furthermore, traditional FL primarily supports homogeneous models and lacks effective solutions for interactions among heterogeneous models. To address these limitations, we propose a novel method called the Dynamic Collaboration of Heterogeneous Models (DCHM), which leverages a consortium blockchain network to enhance the credibility of the overall model and facilitate effective coordination among diverse models. Additionally, we introduce a Distributed Hierarchical Aggregation (DHA) algorithm that enables permissioned nodes within each group to aggregate local model results and share these with all permissionless nodes for standardized processing. Following several iterative cycles, permissioned nodes conduct a secondary integration of local results to produce a global outcome. Experimental results demonstrate that DCHM excels in efficiency when analyzing the time-varying nature of body shape changes.

DCHM: Dynamic Collaboration of Heterogeneous Models for Analyzing Time-Varying Body Shape Changes Using a Blockchain-Based Framework

Scene-level point cloud registration is very challenging when considering dynamic foregrounds. Existing indoor datasets mostly assume rigid motions, so the trained models cannot robustly handle scenes with non-rigid motions. On the other hand, non-rigid datasets are mainly object-level, so the trained models cannot generalize well to complex scenes. This paper presents HybridReg, a new approach to 3D point cloud registration, learning uncertainty mask to account for hybrid motions: rigid for backgrounds and non-rigid/rigid for instance-level foregrounds. First, we build a scene-level 3D registration dataset, namely HybridMatch, designed specifically with strategies to arrange diverse deforming foregrounds in a controllable manner. Second, we account for different motion types and formulate a mask-learning module to alleviate the interference of deforming outliers. Third, we exploit a simple yet effective negative log-likelihood loss to adopt uncertainty to guide the feature extraction and correlation computation. To our best knowledge, HybridReg is the first work that exploits hybrid motions for robust point cloud registration. Extensive experiments show HybridReg's strengths, leading it to achieve state-of-the-art performance on both widely-used indoor and outdoor datasets. Code and dataset will be released to facilitate future research.

HybridReg: Robust 3D Point Cloud Registration with Hybrid Motions

In vision transformers, position embedding (PE) plays a crucial role in capturing the order of tokens. However, in vision transformer structures, there is a limitation in the expressiveness of PE due to the structure where position embedding is simply added to the token embedding. A layer-wise method that delivers PE to each layer and applies independent LNs for token embedding and PE has been adopted to overcome this limitation. In this paper, we identify the conflicting result that occurs in a layer-wise structure when using the global average pooling (GAP) method instead of the class token. To overcome this problem, we propose MPVG, which maximizes the effectiveness of PE in a layer-wise structure with GAP. Specifically, we identify that PE counterbalances token embedding values at each layer in a layer-wise structure. Furthermore, we recognize that the counterbalancing role of PE is insufficient in the layer-wise structure, and we address this by maximizing the effectiveness of PE through MPVG. Through experiments, we demonstrate that PE performs a counterbalancing role and that maintaining this counterbalancing directionality significantly impacts vision transformers. As a result, the experimental results show that MPVG outperforms existing methods across vision transformers on various tasks.

Maximizing the Position Embedding for Vision Transformers with Global Average Pooling

Despite the impressive progress of multimodal generative models, generating sound solely from text poses challenges in ensuring comprehensive scene depiction and temporal alignment.
Meanwhile, video-to-audio generation limits the flexibility to prioritize sound synthesis for specific objects within the scene.
To tackle these challenges, we propose a novel video-and-text-to-audio generation method, called ReWaS, where video serves as a conditional control for a text-to-audio generation model.
Especially, our method estimates the structural information of sound (namely, energy) from the video while receiving key content cues from a user prompt.
We employ a well-performing text-to-audio model to consolidate the video control, which is much more efficient for training multimodal diffusion models with massive triplet-paired (audio-video-text) data.
In addition, by separating the generative components of audio, it becomes a more flexible system that allows users to freely adjust the energy, surrounding environment, and primary sound source according to their preferences.
Experimental results demonstrate that our method shows superiority in terms of quality, controllability, and training efficiency.
Our demo is available at https://rewas-tv2a.github.io/.

Read, Watch and Scream! Sound Generation from Text and Video

Benefiting from their powerful generative capabilities, pretrained diffusion models have garnered significant attention for real-world image super-resolution (Real-SR). Existing diffusion-based SR approaches typically utilize semantic information from degraded images and restoration prompts to activate prior for producing realistic high-resolution images. However, general-purpose pretrained diffusion models, not designed for restoration tasks, often have suboptimal prior, and manually defined prompts may fail to fully exploit the generated potential. To address these limitations, we introduce RAP-SR, a novel restoration prior enhancement approach in pretrained diffusion models for Real-SR. First, we develop the High-Fidelity Aesthetic Image Dataset (HFAID), curated through a Quality-Driven Aesthetic Image Selection Pipeline (QDAISP). Our dataset not only surpasses existing ones in fidelity but also excels in aesthetic quality. Second, we propose the Restoration Priors Enhancement Framework, which includes Restoration Priors Refinement (RPR) and Restoration-Oriented Prompt Optimization (ROPO) modules. RPR refines the restoration prior using the HFAID, while ROPO optimizes the unique restoration identifier, improving the quality of the resulting images. RAP-SR effectively bridges the gap between general-purpose models and the demands of Real-SR by enhancing restoration prior. Leveraging the plug-and-play nature of RAP-SR, our approach can be seamlessly integrated into existing diffusion-based SR methods, boosting their performance. Extensive experiments demonstrate its broad applicability and state-of-the-art results.

RAP-SR: RestorAtion Prior Enhancement in Diffusion Models for Realistic Image Super-Resolution

We introduce a biologically plausible RL framework for solving tasks in partially observable Markov decision processes (POMDPs). 
The proposed algorithm combines three integral parts: (1) A Meta-RL architecture, resembling the mammalian basal ganglia; (2) A biologically plausible reinforcement learning algorithm, exploiting temporal difference learning and eligibility traces to train the policy and the value-function; (3) An online automatic differentiation algorithm for computing the gradients with respect to parameters of a shared recurrent network backbone. Our experimental results show that the method is capable of solving a diverse set of partially observable reinforcement learning tasks. The algorithm we call real-time recurrent reinforcement learning (RTRRL) serves as a model of learning in biological neural networks, mimicking reward pathways in the basal ganglia.

Real-Time Recurrent Reinforcement Learning

We present VQTalker, a Vector Quantization-based framework for multilingual talking head generation that addresses the challenges of lip synchronization and natural motion across diverse languages. Our approach is grounded in the phonetic principle that human speech comprises a finite set of distinct sound units (phonemes) and corresponding visual articulations (visemes), which often share commonalities across languages. We introduce a facial motion tokenizer based on Group Residual Finite Scalar Quantization (GRFSQ), which creates a discretized representation of facial features. This method enables comprehensive capture of facial movements while improving generalization to multiple languages, even with limited training data. Building on this quantized representation, we implement a coarse-to-fine motion generation process that progressively refines facial animations. Extensive experiments demonstrate that VQTalker achieves state-of-the-art performance in both video-driven and speech-driven scenarios, particularly in multilingual settings. Notably, our method achieves high-quality results at a resolution of $512 \times 512$  pixels while maintaining a lower bitrate of approximately 11 kbps. Our work opens new possibilities for cross-lingual talking face generation.

VQTalker: Towards Multilingual Talking Avatars through Facial Motion Tokenization

Deciphering visual content from fMRI sheds light on the human vision system, but data scarcity and noise limit brain decoding model performance. Traditional approaches rely on subject-specific models, which are sensitive to training sample size. In this paper, we address data scarcity by proposing shallow subject-specific adapters to map cross-subject fMRI data into unified representations. A shared deep decoding model then decodes these features into the target feature space. We use both visual and textual supervision for multi-modal brain decoding and integrate high-level perception decoding with pixel-wise reconstruction guided by high-level perceptions. Our extensive experiments reveal several interesting insights: 1) Training with cross-subject fMRI benefits both high-level and low-level decoding models; 2) Merging high-level and low-level information improves reconstruction performance at both levels; 3) Transfer learning is effective for new subjects with limited training data by training new adapters; 4) Decoders trained on visually-elicited brain activity can generalize to decode imagery-induced activity, though with reduced performance.

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Semi-Supervised Multimodal Classification Through Learning from Modal and Strategic Complementarities

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Semi-Supervised Multimodal Classification Through Learning from Modal and Strategic Complementarities

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BGHR: Bridging the Gap Between HBox-Supervised and RBox-Supervised Oriented Object Detection via Adaptive Fine-Grained Sample Mining

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