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Scene-aware motion synthesis has been widely researched recently due to its numerous applications. Prevailing methods rely heavily on paired motion-scene data, while it is difficult to generalize to diverse scenes when trained only on a few specific ones. Thus, we propose a unified framework, termed Diffusion Implicit Policy (DIP), for scene-aware motion synthesis, where paired motion-scene data are no longer necessary. In this paper, we disentangle human-scene interaction from motion synthesis during training, and then introduce an interaction-based implicit policy into motion diffusion during inference. Synthesized motion can be derived through iterative diffusion denoising and implicit policy optimization, thus motion naturalness and interaction plausibility can be maintained simultaneously. For long-term motion synthesis, we introduce motion blending in joint rotation power space. The proposed method is evaluated on synthesized scenes with ShapeNet furniture, and real scenes from PROX and Replica. Results show that our framework presents better motion naturalness and interaction plausibility than cutting-edge methods. This also indicates the feasibility of utilizing the DIP for motion synthesis in more general tasks and versatile scenes.

AAAI 2026

Diffusion Implicit Policy for Unpaired Scene-aware Motion Synthesis

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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.

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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.

Label Distribution Learning (LDL) is a groundbreaking paradigm for addressing the task with label ambiguity. Subjectivity in annotating label description degrees often leads to imbalanced label distribution. Existing approaches either adopt representation alignment or decoupling strategies to solve the imbalanced label distribution learning (ILDL). However, representation alignment-based methods overlook the issue of gradient vanishing for non-dominant branches within imbalanced label distributions, while decoupling-based approaches fail to achieve adaptive weight optimization. To address these issues, we propose Adaptive Momentum and Exponential Moving Average weighted modeling (AMEMA). AMEMA combines EMA-based loss weighting with momentum allocation to mitigate gradient attenuation in non-dominant label learning and adaptively balance the optimization signals between dominant and non-dominant branches. Specifically, AMEMA decouples label distributions into two branches, dominant and non-dominant. It computes and updates Kullback-Leibler divergence losses for each branch using EMA. Different initial momenta are applied to each branch to facilitate branch-specific optimization dynamics. Dynamic weighting coefficients, derived from EMA-smoothed losses, allow the model to adjust its learning direction adaptively and improve the learning of non-dominant labels. Extensive experiments on benchmark datasets show that AMEMA consistently outperforms state-of-the-art ILDL methods across various evaluation metrics.

Adaptive Momentum and EMA-weighted Modeling for Imbalanced Label Distribution Learning

Diffusion Transformers (DiTs) have demonstrated exceptional performance in high-fidelity image and video generation. To reduce their substantial computational costs, feature caching techniques have been proposed to accelerate inference by reusing hidden representations from previous timesteps. However, current methods often struggle to maintain generation quality at high acceleration ratios, where prediction errors increase sharply due to the inherent instability of long-step forecasting. In this work, we adopt an ordinary differential equation (ODE) perspective on the hidden-feature sequence, modeling layer representations along the trajectory as a feature-ODE. We attribute the degradation of existing caching strategies to their inability to robustly integrate historical features under large skipping intervals. To address this, we propose \textbf{FoCa} (Forecast-then-Calibrate), which treats feature caching as a feature-ODE solving problem. Extensive experiments across image synthesis, video generation, and super-resolution tasks demonstrate the effectiveness of FoCa, particularly under aggressive acceleration. Without additional training, FoCa achieves near-lossless speedups of 5.50$\times$ on FLUX, 6.45$\times$ on HunyuanVideo, 3.17$\times$ on Inf-DiT, and maintains high quality with a 4.53$\times$ speedup on DiT. Our code will be released upon acceptance.

Forecast Then Calibrate: Feature Caching as ODE for Efficient Diffusion Transformers

Parallel corpora, as the foundation of machine translation, remain crucial even in the era of large language models (LLMs) for pre-training and fine-tuning.
However, annotating parallel corpora is extremely costly, as it requires annotators to be proficient in multiple languages.
To reduce this cost, prior work has explored image-pivoted corpus synthesis, generating multilingual captions for the same image as pseudo-parallel data.
Unfortunately, these pseudo corpora suffer from the serious issue of multilingual focus divergence, i.e., the model attending to distinct aspects of the image when generating captions in different languages.
To address this problem, we propose a method called PRISMS (Parallel Refracting ImageS into Multilingual descriptions with Structured visual guidance), which leverages semantic graphs as structured visual guidance to unify the focus of multilingual captions. 
To ensure adherence to this guidance, we introduce two key techniques: supervised fine-tuning using self-generated instructional data, and reinforcement learning with a reward signal based on semantic graph consistency. 
Experimental results on five languages show that our PRISMS significantly improves the image-pivot parallel corpora synthesis, enabling LLMs to achieve translation performance comparable to that of models trained on manually annotated corpora.

The Visual Prism: Refracting Images into Parallel Multilingual Descriptions with Structured Visual Guidance

Multi-person eyeblink detection in untrimmed in-the-wild videos is an emerging and challenging task. Due to its significant spatio-temporal fine-grained characteristics compared to general actions, we empirically find that general action detectors, though effective in broader domains, struggle with this task (i.e.,Blink-AP$ < $2\%). Specialized eyeblink methods alleviate it through fine-grained spatio-temporal operations. SOTA method proposes a unified model combining instance-aware face localization and eyeblink detection through joint multi-task learning and feature sharing. While effectiveness, it exhibits two critical limitations that may contribute to its unsatisfactory performance (i.e.,Blink-AP$=$10.11\%): (1) Face localization and eyeblink detection require distinct spatio-temporal feature granularities, making joint modeling in a unified feature space suboptimal. (2) Eyeblink task training could be largely affected by unstable face-eye feature learning under the joint training paradigm. 
To address this, we propose DeFB, a decomposed feature learning paradigm with favorable effectiveness and efficiency: (1) We design to model face and eye in feature spaces of different granularities, which greatly enhances fine-grained perception while reducing computational costs compared with unified feature space;
(2) To address the instability in face-eye feature learning, an asynchronous learning mechanism for the face and eye feature spaces is adopted, with eye feature learning serving as a refinement process based on well-trained coarse face features, which also maintains efficient feature sharing as in the existing unified model.
Compared with SOTA method, DeFB doubles the performance (Blink-AP: 24.65\% v.s. 10.11\%) while boosting efficiency by nearly 35\%. DeFB can also be integrated as a plugin to substantially augment the eyeblink detection capabilities of general action detectors. Code will be released to facilitate relevant fields.

DeFB: Decomposed Feature Learning for Real-Time Multi-Person Eyeblink Detection in Untrimmed In-the-Wild Videos

Urban air pollution is a major health crisis causing millions of premature deaths annually, underscoring the urgent need for accurate and scalable monitoring of air quality (AQ). 
While low-cost sensors (LCS) offer a scalable alternative to expensive reference-grade stations, their readings are affected by drift, calibration errors, and environmental interference. 
To address these challenges, we introduce Veli (Reference-free Variational Estimation via Latent Inference), an unsupervised Bayesian model that leverages variational inference to correct LCS readings without requiring co-location with reference stations, eliminating a major deployment barrier.
Specifically, Veli constructs a disentangled representation of the LCS readings, effectively separating the true pollutant reading from the sensor noise. To build our model and address the lack of standardized benchmarks in AQ monitoring, we also introduce the Air Quality Sensor Data Repository (AQ-SDR).
AQ-SDR is the largest AQ sensor benchmark to date, with readings from 23,737 LCS and reference stations across multiple regions. Veli demonstrates strong generalization across both in-distribution and out-of-distribution settings, effectively handling sensor drift and erratic sensor behavior. We will publicly release the model code and the dataset.

Veli: Unsupervised Method and Unified Benchmark for Low-Cost Air Quality Sensor Correction

We propose unsupervised multi-scenario (UMS) person re-identification (ReID) as a new task that expands ReID across diverse scenarios (cross-resolution, clothing change, etc.) within a single coherent framework. To tackle UMS-ReID, we introduce image-text knowledge modeling (ITKM) -- a three-stage framework that effectively exploits the representational power of vision-language models. We start with a pre-trained CLIP model with an image encoder and a text encoder. In Stage I, we introduce a scenario embedding in the image encoder and fine-tune the encoder to adaptively leverage knowledge from multiple scenarios. In Stage II, we optimize a set of learned text embeddings to associate with pseudo-labels from Stage I and introduce a multi-scenario separation loss to increase the divergence between inter-scenario text representations. In Stage III, we first introduce cluster-level and instance-level heterogeneous matching modules to obtain reliable heterogeneous positive pairs (e.g., a visible image and an infrared image of the same person) within each scenario. Next, we propose a dynamic text representation update strategy to maintain consistency between text and image supervision signals. Experimental results across multiple scenarios demonstrate the superiority and generalizability of ITKM; it not only outperforms existing scenario-specific methods but also enhances overall performance by integrating knowledge from multiple scenarios.

Image-Text Knowledge Modeling for Unsupervised Multi-Scenario Person Re-Identification

Building a unified target representation that simultaneously achieves short-term adaptability and long-term stability is crucial for robust visual tracking. 
However, existing trackers typically face an inherent trade-off. Methods primarily relying on short-term appearance and motion cues achieve rapid adaptation, but they often struggle with long-term identity consistency. Conversely, trackers that emphasize extensive temporal context provide strong robustness, yet this approach can compromise their short-term adaptability.
To bridge this gap, we propose a novel tracker, MUTrack, which comprehensively integrates both long-term and short-term memories into a unified target representation for more robust tracking. 
Specifically, we design a unified memory bank that stores and manages long-term memory for maintaining long-term identity consistency, and short-term memory for adapting to instantaneous appearance changes. 
To fully leverage the complementary nature of both long-term and short-term temporal information, we introduce a perception interaction module that dynamically fuses these memory types through deep and bidirectional interactions, enabling mutual refinement where one guides the other.
This ultimately generates a highly adaptive target representation, which effectively balances adaptability to instantaneous changes with robustness against long-term identity drift.
Extensive experiments on GOT10k, TrackingNet, LaSOT, LaSOT_ext, NfS, and OTB100 consistently demonstrate that MUTrack achieves SOTA performance.

MUTrack: A Memory-Aware Unified Representation Framework for Visual Tracking

Despite the rapid progress of Vision-Language Models (VLMs), their capabilities are inadequately assessed by existing benchmarks, which are predominantly English-centric, feature simplistic layouts, and support limited tasks. Consequently, they fail to evaluate model performance for Visually Rich Document Understanding (VRDU), a critical challenge involving complex layouts and dense text. To address this, we introduce DocWeaver, a novel multi-agent pipeline that leverages Large Language Models to automatically generate a new benchmark. The result is MosaicDoc, a large-scale, bilingual (Chinese and English) resource designed to push the boundaries of VRDU. Sourced from newspapers and magazines, MosaicDoc features diverse and complex layouts (including multi-column and non-Manhattan), rich stylistic variety from 196 publishers, and comprehensive multi-task annotations (OCR, VQA, reading order, and localization). With 72K images and over 600K QA pairs, MosaicDoc serves as a definitive benchmark for the field. Our extensive evaluation of state-of-the-art models on this benchmark reveals their current limitations in handling real-world document complexity and charts a clear path for future research.

MosaicDoc: A Large-Scale Bilingual Benchmark for Visually Rich Document Understanding

Retrieval-augmented generation (RAG) is widely adopted for knowledge-intensive tasks, but unverified external knowledge can pose risks such as data injection and retrieval pollution, leading to unexpected generation. Existing defenses rely on patch-based fixes, which limit generalization and increase system latency. To address these issues, we propose **RAG2RAG**, the first **framework-level** security solution designed specifically for RAG. Inspired by human intuition to reason about "what can and cannot be said" during RAG phase, RAG2RAG augments the main RAG module with a lightweight security expert module composed of two components: (1) a Detective that dynamically retrieves supporting evidence, and (2) a Judge that makes final decisions based on retrieved context. The main and expert modules operate in parallel without causing noticeable delays. Experiments across two languages, 6 domains, and 7 types of poisoning attacks demonstrate that RAG2RAG consistently achieves higher accuracy and lower attack success rates than 7 mainstream baselines. Furthermore, it integrates seamlessly with various RAG architectures, offering generalizable and efficient protection across diverse threat scenarios.

Safe RAG by RAG: Untying the Bell That RAG Rang with the RAG Hand

Out-of-distribution (OOD) detection plays a critical role in ensuring the robustness of machine learning models in open-world settings. While extensive efforts have been made in vision, language, and graph domains, the challenge of OOD detection in hypergraph-structured data remains unexplored. In this work, we formalize the problem of hypergraph out-of-distribution (HOOD) detection, which aims to identify nodes or hyperedges whose high-order relational contexts differ significantly from those seen during training. We propose HyperGOOD, a unified energy-based detection framework that integrates multi-scale spectral decomposition with structure-aware uncertainty propagation. By preserving both low- and high-frequency signals and diffusing uncertainty across the hypergraph, HyperGOOD effectively captures subtle and relationally entangled anomalies. Experimental results on nine hypergraph datasets demonstrate the effectiveness of our approach, establishing a new foundation for robust hypergraph learning under distributional shifts.

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Adaptive Momentum and EMA-weighted Modeling for Imbalanced Label Distribution Learning

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