Singapore

Multimodal sentiment analysis (MSA) aims to infer emotional states by effectively integrating textual, acoustic, and visual modalities. Despite notable progress, existing multimodal fusion methods often neglect modality-specific structural dependencies and semantic misalignment, limiting their quality, interpretability, and robustness. To address these challenges, we propose a novel framework called the Structural-Semantic Unifier (SSU), which systematically integrates modality-specific structural information and cross-modal semantic grounding for enhanced multimodal representations. Specifically, SSU dynamically constructs modality-specific graphs by leveraging linguistic syntax for text and a lightweight, text-guided attention mechanism for acoustic and visual modalities, thus capturing detailed intra-modal relationships and semantic interactions. We further introduce a semantic anchor, derived from global textual semantics, that serves as a cross-modal alignment hub, effectively harmonizing heterogeneous semantic spaces across modalities. Additionally, we develop a multi-view contrastive learning objective that promotes discriminability, semantic consistency, and structural coherence across intra- and inter-modal views. Extensive evaluations on two widely-used benchmark datasets, CMU-MOSI and CMU-MOSEI, demonstrate that SSU consistently achieves state-of-the-art performance while significantly reducing computational overhead compared to prior methods. Comprehensive qualitative analyses further validate SSU’s interpretability and its ability to capture nuanced emotional patterns through semantically-grounded interactions.

AAAI 2026

Structures Meet Semantics: Multimodal Fusion via Graph Contrastive Learning

graph-based machine learning

affective computing

multimodal learning

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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-26 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.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

poster

Image cropping is crucial for enhancing the visual appeal and narrative impact of photographs, yet existing rule-based and data-driven approaches often lack diversity or require annotated training data. We introduce ProCrop, a retrieval-based method that leverages professional photography to guide cropping decisions. By fusing features from professional photographs with those of the query image, ProCrop learns from professional compositions, significantly boosting performance. Additionally, we present a large-scale dataset of 242K weakly-annotated images, generated by out-painting professional images and iteratively refining diverse crop proposals. This composition-aware dataset generation offers diverse high-quality crop proposals guided by aesthetic principles and becomes the largest publicly available dataset for image cropping. Extensive experiments show that ProCrop significantly outperforms existing methods in both supervised and weakly-supervised settings. Notably, when trained on the new dataset, our ProCrop surpasses previous weakly-supervised methods and even matches fully supervised approaches. Both the code and dataset will be made publicly available to advance research in image aesthetics and composition analysis.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions

3D scene graph generation is a pivotal task in scene understanding. Its performance is easy to be constrained by the limited availability of annotated data. Currently, the existing solutions on point cloud pre-training usually emphasize on object-centric representations while neglecting the predicate feature learning. This limitation significantly hinders their relational reasoning capabilities, as inter-object relationships are fundamentally governed by predicate features. To enhance 3D Scene Graphs Pre-training, this paper proposes a task-specific Multi-view Invariance Learning framework with Collaborative Cross-modal Regularization. 
In detail, the inherent horizontal-rotation invariance of 3D objects and their semantic relationships are leveraged to construct a self-supervised paradigm for triplet feature learning. Moreover, our framework harnesses the cross-modal prior knowledge from the vision-language model to regularize model optimization. It could further achieve the semantic discrimination via unsupervised deep clustering. To resolve the knowledge discrepancies arising from the pre-trained model in fine-tuning, a predicate adapter equipped with knowledge filtering gate is devised to selectively aggregate the predicate features of pre-trained model. Extensive experiments demonstrate that our framework is effective in boosting 3D scene graph generation performance, surpassing state-of-the-art ones.

Multi-view Invariance Learning for 3D Scene Graph Pre-training via Collaborative Cross-Modal Regularization

Effective coordination in Multi-Agent Reinforcement Learning (MARL) is particularly challenging under partial observability, where agents must reason about potential collaborators using only local information. Existing methods fall into two categories: communication-based approaches that enable message exchange but often fix or misidentify who the collaborators are, and role-based approaches that encourage specialization based on behavioral similarity. However, both lines of work overlook the task‑induced cooperative dependencies that decide which agents should collaborate, leading to miscommunication or role misassignment under partial observability. We introduce GRDC (Graph‑driven Role Discovery and Communication), a unified framework that approximates these dependencies by dynamically constructing local interaction graphs from trajectory embeddings, then uses these graphs to infer roles via prototype matching and to restrict communication to intra‑role agents with attention-based aggregation. Beyond role inference and communication, GRDC maximizes role entropy, decorrelates prototypes, and dynamically prunes redundant ones to obtain structured yet compact role specialization. Experimental results on Predator Prey, Cooperative Navigation, and SMACv2 demonstrate that GRDC consistently outperforms state-of-the-art communication- and role-based baselines, improving coordination efficiency and training stability across tasks.

GRDC: A Unified Graph-Driven Framework for Role Discovery and Communication in Multi-Agent Reinforcement Learning

Despite recent advances in the reasoning capabilities of Large Language Models (LLMs), improving the reasoning ability of Small Language Models (SLMs, e.g., up to 1.5B parameters) remains challenging. A key obstacle lies in the complexity and variability of natural language: essentially equivalent problems often appear in diverse surface forms, often obscured by redundant or distracting details. This imposes a dual burden on SLMs: they must first extract the core problem from complex linguistic input, and then perform reasoning based on that understanding. The resulting vast and noisy problem space hinders optimization, particularly for models with limited capacity. To address this, we propose a new framework that decouples understanding from reasoning by mapping natural language problems into a canonical problem space-a semantically simplified yet expressive domain. This enables SLMs to focus on reasoning over standardized inputs, free from linguistic variability. Within this framework, we introduce DURIT (Decoupled Understanding from Reasoning via Iterative Training), a three-step algorithm that iteratively: (1) mapping natural language problems via reinforcement learning, (2) aligns reasoning trajectories through self-distillation, and (3) trains reasoning policies in the problem space. The mapper and reasoner are co-trained in an alternating loop throughout this process. Experiments show that DURIT substantially improves SLMs' performance on both in-domain and out-of-domain mathematical and logical reasoning tasks. Beyond improving reasoning capabilities, DURIT also improves the robustness of reasoning, validating decoupling understanding from reasoning as an effective strategy for strengthening SLMs.

Decoupling Understanding from Reasoning via Problem Space Mapping for Small-Scale Model Reasoning

Generative reasoning with large language models (LLMs) often involves long decoding sequences, leading to substantial memory and latency overheads from accumulating key-value (KV) caches. While existing KV compression methods primarily focus on reducing prefill memory from long input sequences, they fall short in addressing the dynamic and layer-sensitive nature of long-form generation, which is central to reasoning tasks. We propose Lethe, a dynamic KV cache management framework that introduces adaptivity along both the spatial and temporal dimensions of decoding. Along the spatial dimension, Lethe performs layerwise sparsity-aware allocation, assigning token pruning budgets to each transformer layer based on estimated attention redundancy. Along the temporal dimension, Lethe conducts multi-round token pruning during generation, driven by a Recency-Aware Selective Retention (RASR) mechanism. RASR extends traditional recency-based heuristics by also considering token relevance derived from evolving attention patterns, enabling informed decisions about which tokens to retain or evict. Empirical results demonstrate that Lethe achieves a favorable balance between efficiency and generation quality across diverse models and tasks, increases throughput by up to 2.56×.

Lethe: Layer- and Time-Adaptive KV Cache Pruning for Reasoning-Intensive LLM Serving

Change captioning aims to describe changes between a pair of images. However, existing works rely on visual features alone, which often fail to capture subtle but meaningful changes because they lack the ability to represent explicitly structured information such as object relationships and compositional semantics. To alleviate this, we present CORTEX (COmpositional Reasoning-aware TEXt-guided), a novel framework that integrates complementary textual cues to enhance change understanding. In addition to capturing cues from pixel-level differences, CORTEX utilizes scene-level textual knowledge provided by Vision Language Models (VLMs) to extract richer image text signals that reveal underlying compositional reasoning. CORTEX consists of three key modules: (i) an Image-level Change Detector that identifies low-level visual differences between paired images, (ii) a Reasoning-aware Text Extraction (RTE) module that use VLMs to generate compositional reasoning descriptions implicit in visual features, and (iii) an Image-Text Dual Alignment (ITDA) module that aligns visual and textual features for fine-grained relational reasoning. This enables CORTEX to reason over visual and textual features and capture changes that are otherwise ambiguous in visual features alone.

Leveraging Textual Compositional Reasoning for Robust Change Captioning

The explosive growth of artificial intelligence is exponentially escalating computational demand, inflating data-centre energy use and carbon emissions, and spurring rapid deployment of green data centres to relieve resource and environmental stress. Achieving sub-minute orchestration of renewables, smart grids, storage, and thermo-fluid loads, while minimising PUE and lifecycle carbon intensity, hinges on accurate load forecasting. Existing rule-based or thermodynamic methods are subjective and costly. Although typical deep time-series networks perform well on large-scale datasets, fail in cold-start, fragmented, or drifting environments typical of nascent green data centres. We introduce HyperLoad, a cross-modal framework that exploits large-scale pre-trained language models (LLMs) to overcome data scarcity. In the alignment stage, textual priors and time-series attributes are mapped to a common latent space, maximizing prior-knowledge utility. In the modeling stage, domain-aligned priors are injected through adaptive prefix tuning, enabling rapid scenario adaptation, while a global interactive self-attention module captures cross-device temporal dependencies. The public GreenData dataset is released for benchmarking. Under both data-sufficient and data-scarce regimes, HyperLoad consistently surpasses state-of-the-art baselines, demonstrating its practicality for sustainable data-centre management.

HyperLoad: A Cross-Modality Enhanced Large Language Model-Based Framework for Green Data Center Cooling Load Prediction

Text-to-visible \& infrared person retrieval aims to retrieve the corresponding visible (RGB) and thermal infrared (TIR) images given the text descriptions. Existing methods perform semantic decoupling by aligning RGB and TIR features separately to different attributes, thereby facilitating the alignment between the fused multimodal representation and the text. However, insufficient TIR representation ability and cross-view representation capabilities of RGB and TIR modalities limit the retrieval accuracy and robustness. To address these issues, we propose a novel Dual-teacher Interactive Knowledge Distillation Network called DIKDNet, which performs the interactive knowledge distillation between two modality-specific teachers with rich cross-view representation capabilities to enhance TIR representations and the collaborative knowledge distillation from both teachers to the corresponding students to enhance the cross-modal cross-view representations, for robust text-to-visible \& infrared person retrieval. Specifically, to enhance the representation ability of the TIR backbone network while preserving modality-specific characteristics, we design an Interactive Knowledge Distillation Module (IKDM), which introduces a boundary-constrained distillation strategy between RGB and TIR backbones, to transfer the semantic features of RGB backbone to TIR one. To enhance the cross-modal cross-view representation capability, we design a Collaborative Knowledge Distillation Module (CKDM) to transfer the cross-modal similarity relations and the cross-view multimodal representations from teacher networks to student ones. Experimental results demonstrate that our method consistently achieves significant performance gains on both the RGBT-PEDES and RGBNT201-PEDES datasets. The code will be released upon the acceptance.

Dual-Teacher Interactive Knowledge Distillation Network for Text-to-Visible & Infrared Person Retrieval

Current 3D visual grounding tasks only process sentence-level detection or segmentation, which critically fails to leverage the rich compositional contextual reasonings within natural language expressions. To address this challenge, we introduce Detailed 3D Referring Expression Segmentation (3D-DRES), a new task that provides a phrase to 3D instance mapping, aiming at enhancing fine-grained 3D vision-language understanding. To support 3D-DRES, we present DetailRefer, a new dataset comprising 55,432 descriptions spanning 11,054 distinct objects. Unlike previous datasets, DetailRefer implements a pioneering phrase-instance annotation paradigm where each referenced noun phrase is explicitly mapped to its corresponding 3D elements. Additionally, we introduce DetailBase, a purposefully streamlined yet effective baseline architecture that supports dual-mode segmentation at both sentence and phrase levels. Our experimental results demonstrate that models trained on DetailRefer not only excel at phrase-level segmentation but also show surprising improvements on traditional 3D-RES benchmarks.

3D-DRES: Detailed 3D Referring Expression Segmentation

Multimodal Large Language Models (MLLMs) have achieved remarkable progress in multimodal reasoning. However, they often excessively rely on textual information during the later stages of inference, neglecting the crucial integration of visual input. Current methods typically address this by explicitly injecting visual information to guide the reasoning process. In this work, through an analysis of MLLM attention patterns, we made an intriguing observation: with appropriate guidance, MLLMs can spontaneously re-focus their attention on visual inputs during the later stages of reasoning, even without explicit visual injection. This spontaneous shift in focus suggests that MLLMs are intrinsically capable of performing visual fusion reasoning. Building on this insight, we introduce **Look-Back**, an implicit approach designed to guide MLLMs to "look back" at visual information in a self-directed manner during reasoning. Look-Back empowers the model to autonomously determine when, where, and how to re-focus on visual inputs, eliminating the need for explicit model-structure constraints or additional input. We demonstrate that Look-Back significantly enhances the model's reasoning and perception capabilities, as evidenced by extensive empirical evaluations on multiple multimodal benchmarks.

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