Jiateng Liu, Zhenhailong Wang, Xiaojiang Huang, Yingjie Li, Xiang Li, Chenlei Guo, Xing Fan, Ruhi Sarikaya, Heng Ji. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

This paper proposes an effective Gaussian management framework for high-fidelity scene reconstruction of both appearance and geometry. Unlike recent Gaussian Splatting (GS) pipelines that treat all primitives uniformly during optimization, our framework explicitly manages the attribute activation, representation and pruning of Gaussian. Specifically, our framework first introduces GauSep, a novel densification strategy that selectively activates Gaussian color or normal attributes to alleviate destructive gradient conflicts arising from dual supervision. We further propose GauRep, an adaptive Gaussian representation that dynamically adjusts spherical harmonics (SHs) orders and performs task-decoupled pruning to reduce redundancy at both the individual and global levels. To provide reliable geometric supervision for above mangement process, we additionally introduce CoRe, an regularized surface reconstruction module that distills robust normal fields from an SDF branch to the Gaussian representation through a confidence mechanism. Notably, the proposed Gaussian management is compatible with various reconstruction architectures and can be seamlessly integrated to improve performance while reducing size of the model. Extensive experiments demonstrate that our approach achieves superior or comparable performance in appearance and geometry reconstruction compared with state-of-the-art methods, while using significantly fewer parameters.

Hyeonjeong Ha, Xiaomeng Jin, Jeonghwan Kim, Jiateng Liu, Zhenhailong Wang, Khanh Duy Nguyen, Ansel Blume, Nanyun Peng, Kai-Wei Chang, Heng Ji. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Propaganda plays a critical role in shaping public opinion and fueling disinformation. While existing research primarily focuses on identifying propaganda techniques, it lacks the ability to capture the broader motives and the impacts of such content. To address these challenges, we introduce PropaInsight, a conceptual framework grounded in foundational social science research, which systematically dissects propaganda into techniques, arousal appeals, and underlying intent. PropaInsight offers a more granular understanding of how propaganda operates across different contexts. Additionally, we present PropaGaze, a novel dataset that combines human-annotated data with high-quality synthetic data generated through a meticulously designed pipeline. Our experiments show that off-the-shelf LLMs struggle with propaganda analysis, but PropaGaze significantly improves performance. Fine-tuned Llama-7B-Chat achieves 203.4% higher text span IoU in technique identification and 66.2% higher BertScore in appeal analysis compared to 1-shot GPT-4-Turbo. Moreover, PropaGaze complements limited human-annotated data in data-sparse and cross-domain scenarios, demonstrating its potential for comprehensive and generalizable propaganda analysis.

Rushi Wang, Jiateng Liu, Cheng Qian, Yifan Shen, Yanzhou Pan, Zhaozhuo Xu, Ahmed Abbasi, Heng Ji, Denghui Zhang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Capturing human motion with existing monocular estimators often results in large errors when dealing with rare poses, occlusions, truncations, and frame blurring, leading to jitter and long-term drift. Although previous methods have introduced post-processing networks for pose refinement, they struggle to balance global smoothing and fine-grained correction. In this work, we propose MotionRefineNet, which leverages the synergy and complementarity between long- and short-term features in the temporal domain and high- and low-frequency features in the frequency domain to address these challenges. The temporal branch is designed as a hierarchical motion structure to learn multi-time scale features, where long-term features learn motion smoothness, and short-term features capture local rapid changes. The frequency branch employs different frequency band learning strategies based on the degrees of freedom (DoF) of body parts. For body parts with low DoF, the focus is on low-frequency features that represent overall motion trends and regular actions. For body parts with high DoF, we design a filter to adaptively extract useful information from all frequency bands, including subtle motion changes in the high-frequency bands. Extensive experiments on multiple datasets and estimators demonstrate that MotionRefineNet outperforms existing methods in refining 2D, 3D, and SMPL poses, achieving superior pose smoothing and deviation correction. Our code is available at: https://github.com/Wheels319/MotionRefineNet.

Micro-expressions (MEs) are involuntary facial expressions that reveal genuine emotions and have significant applications in fields such as psychology, security, and human-computer interaction. However, previous ME datasets are mainly collected in controlled laboratory environments, such as fixed views, single illumination and head movements, limited subjects and the lack of background. There are significant gaps between them and the real world. To handle this issue, we introduce a novel Natural Micro-Expression (NaME) dataset, a natural dataset collected under unconstrained real-world conditions. It encompasses (1) diverse subjects, multiple views and varying head movements ; (2) rich background information, providing a more realistic benchmark for the micro-expression recognition (MER) research. Furthermore, we propose a MER benchmark for natural environments, named MixFormer. MixFormer includes an efficient sparse attention mechanism to capture subtle facial motions from various factors, and a face-background mix of attention module to model the environment context to help MER. Extensive experiments are conducted to analyze our NaME dataset and benchmark. We believe that our dataset and benchmark will pave the way for future research in MER beyond controlled settings, facilitating the deployment of MER in practical applications. NaME is available at github.com/real-ljt/NAMEdataset.

Micro-expression recognition (MER) has drawn increasing attention in recent years due to its ability to reveal the true feelings people want to hide. The key challenge in MER is subtle motions, which are hard to capture but crucial for MER. Existing methods usually solve this problem by magnifying all motions in the whole face and temporal sequence. However, micro-expressions (MEs) only involve a few facial areas and several temporal snippets. The all-motion magnification in previous methods cannot precisely capture these local ME motion patterns, and can easily cause spatial as well as temporal distortions, which significantly decrease the MER accuracy. In this paper, we propose an RGB-Frequency Relation Exploration Network (RF-REN), which enhances the subtle motions in refined local ME cues by exploring spatial and temporal relations in both RGB and frequency domains. Specifically, we first decompose the ME video into RGB as well as frequency domains, and conduct temporal division according to different motion stages to cover various ME local patterns. Secondly, we construct an adaptive local-global relation exploration (LGRE) module to explore the local relation cues in the spatial appearance and temporal dynamics in both domains. Finally, we propose an RGB-Frequency routing strategy to fuse the RGB and frequency cues, aiming to aggregate spatial-temporal local-global information and enhance subtle motions for MER. Extensive experiments on three databases (CASME II, SAMM and SMIC) show that the proposed model outperforms other state-of-the-art methods.

Modern depth sensors such as LiDAR operate by sweeping laser-beams across the scene, resulting in a point cloud with notable 1 D curve-like structures. In this work, we introduce a new point cloud processing scheme and backbone, called CurveCloudNet, which takes advantage of the curve-like structure inherent to these sensors. While existing backbones discard the rich 1D traversal patterns and rely on generic 3D operations, CurveCloudNet parameterizes the point cloud as a collection of polylines (dubbed a “curve cloud”), establishing a local surface-aware ordering on the points. By reasoning along curves, CurveCloud-Net captures lightweight curve-aware priors to efficiently and accurately reason in several diverse 3D environments. We evaluate CurveCloudNet on multiple synthetic and real datasets that exhibit distinct 3D size and structure. We demonstrate that CurveCloudNet outperforms both point-based and sparse-voxel backbones in various segmentation settings, notably scaling to large scenes better than point-based alternatives while exhibiting improved single-object performance over sparse-voxel alternatives. In all, Curve-CloudNet is an efficient and accurate backbone that can handle a larger variety of 3D environments than past works.

The dynamic nature of real-world information necessitates knowledge editing (KE) in large language models (LLMs).This edited knowledge should propagate and facilitate the deduction of new information based on existing model knowledge.We define the existing related knowledge in a LLM serving as the origination of knowledge propagation as "deduction anchors".However, most of current KE approaches only operate on (subject, relation, object) triples.Both theoretically and empirically, we observe that this simplified setting often leads to uncertainty when determining the deduction anchors, causing low confidence in their responses.To mitigate this issue, we propose a novel task of event-based knowledge editing that pairs facts with event descriptions.This task manifests both as a closer simulation of real-world editing scenarios and a more logically sound setting, implicitly defining the deduction anchor and enabling LLMs to propagate knowledge confidently.We curate a new benchmark dataset EVEDIT derived from the COUNTERFACT dataset and validate its superiority in improving model confidence.Moreover, as we observe that the event-based setting is notably challenging for existing approaches, we propose a novel approach Self-Edit that showcases stronger performance, achieving 55.6% consistency improvement while maintaining the naturalness of generation. 1 Implicitly define the deduction anchor, ensuring model certainty. Previous Simple edits:Messi is a Dutch citizen.In 2024, Lionel Messi made the decision to move to Netherlands and applied for Dutch citizenship.After necessary procedures, he was granted Dutch citizenship and became a citizen of Netherlands.Q: Is Messi a citizen of Argentina in 2023?Q:Where was Messi born ?Q: Did Messi won the World Cup in 2022 ?Ignore the deduction anchor, leading to model uncertainty.

To solve complex tasks, large language models (LLMs) often require multiple rounds of interactions with the user, sometimes assisted by external tools. However, current evaluation protocols often emphasize benchmark performance with single-turn exchanges, neglecting the nuanced interactions among the user, LLMs, and external tools, while also underestimating the importance of natural language feedback from users. These oversights contribute to discrepancies between research benchmark evaluations and real-world use cases. We introduce MINT, a benchmark that evaluates LLMs' ability to solve tasks with multi-turn interactions by (1) using tools and (2) leveraging natural language feedback. To ensure reproducibility, we provide an evaluation framework where LLMs can access tools by executing Python code and receive users' natural language feedback simulated by GPT-4. We repurpose a diverse set of established evaluation datasets focusing on reasoning, coding, and decision-making and carefully curate them into a compact subset for efficient evaluation. Our analysis of 20 open- and closed-source LLMs offers intriguing findings. (a) LLMs generally benefit from tools and language feedback, with performance gains (absolute, same below) of 1--8% for each turn of tool use and 2--17% with natural language feedback. (b) Better single-turn performance does not guarantee better multi-turn performance. (c) Surprisingly, on the LLMs evaluated, supervised instruction-finetuning (SIFT) and reinforcement learning from human feedback (RLHF) generally hurt multi-turn capabilities. We expect MINT can help measure progress and incentivize research in improving LLMs' capabilities in multi-turn interactions, especially for open-source communities where multi-turn human evaluation can be less accessible compared to commercial LLMs with a larger user base.

We present MIRACLE, a system for online, interpretable visual concept and video action recognition. Through a chat interface, users query the recognition system with an uploaded image or video. For images, MIRACLE returns concept predictions from its structured knowledge base, justifying its predictions with heatmaps and natural language-based attribute detections. For videos, MIRACLE predicts an action and justifies its prediction with time varying entity-entity relations. With its ability to learn new concepts in an online, few-shot manner and its support of dynamic changes to its knowledge base, MIRACLE represents a step forward in interpretable multimodal learning systems.

Micro-Expression Recognition (MER) is challenging because the Micro-Expressions' (ME) motion is too weak to distinguish. This hurdle can be tackled by enhancing intensity for a more accurate acquisition of movements. However, existing magnification strategies tend to use the features of facial images that include not only intensity clues as intensity features, leading to the intensity representation deficient of credibility. In addition, the intensity variation over time, which is crucial for encoding movements, is also neglected. To this end, we provide a reliable scheme to extract intensity clues while considering their variation on the time scale. First, we devise an Intensity Distillation (ID) loss to acquire the intensity clues by contrasting the difference between frames, given that the difference in the same video lies only in the intensity. Then, the intensity clues are calibrated to follow the trend of the original video. Specifically, due to the lack of truth intensity annotation of the original video, we build the intensity tendency by setting each intensity vacancy an uncertain value, which guides the extracted intensity clues to converge towards this trend rather some fixed values. A Wilcoxon rank sum test (Wrst) method is enforced to implement the calibration. Experimental results on three public ME databases i.e. CASME II, SAMM, and SMIC-HS validate the superiority against state-of-the-art methods.

Micro-Expression recognition (MER) is a challenging task due to the short duration and low intensity of Micro-Expressions. A popular method to tackle this is magnifying MEs so as to enlarge the expression intensity to make recognition easier. However, the single fixed magnification strategy, widely used in existing works of MER, is not appropriate for different subjects, because each subject has specific expression intensity corresponding to different MEs. To cope with this issue, we propose a novel Attention-based Magnification-Adaptive Network (AMAN) to learn adaptive magnification levels for the ME representation. The network consists of two modules: magnification attention (MA module) to adaptively focus on appropriate magnification levels of different MEs, and frame attention (FA module) to focus on discriminative aggregated frames in a ME video. Extensive experiments on three widely used databases manifest that our method yields state-of-art results compared with other methods.

Cross-Database Micro-Expression Recognition (CD-MER) aims to develop the Micro-Expression Recognition (MER) methods with strong domain adaptability, i.e., the ability to recognize the Micro-Expressions (MEs) of different subjects captured by different imaging devices in different scenes. The development of CDMER is faced with two key problems: 1) the severe feature distribution gap between the source and target databases; 2) the feature representation bottleneck of ME such local and subtle facial expressions. To solve these problems, this paper proposes a novel Transfer Group Sparse Regression method, namely TGSR, which aims to 1) optimize the measurement and better alleviate the difference between the source and target databases, and 2) highlight the valid facial regions to enhance extracted features, by the operation of selecting the group features from the raw face feature, where each region is associated with a group of raw face feature, i.e., the salient facial region selection. Compared with previous transfer group sparse methods, our proposed TGSR has the ability to select the salient facial regions, which is effective in alleviating aforementioned problems for better performance and reducing the computational cost at the same time. We use two public ME databases, i.e., CASME II and SMIC, to evaluate our proposed TGSR method. Experimental results show that our proposed TGSR learns the discriminative and explicable regions, and outperforms most state-of-the-art subspace-learning-based domain-adaptive methods for CDMER.

Existing works for spontaneous Micro-Expression Recognition (MER) tend to encode Micro-Expression (ME) movements to get more discriminative features. However, MEs’ low intensity makes the capture for motion extremely difficult, and the widely adopted unified-magnification strategy is prone to noise and lacks flexibility. To this end, this paper provides a new insight to encode ME motion and tackle magnification noise. Specifically, we reconstruct a new sequence via magnification techniques to make subtle ME movements more distinguishable. Afterward, Sparse Self-Attention (SSA) rectifies self-attention with Locality Sensitive Hashing (LSH), cutting the space into several hush buckets of related features. Only keys in the same bucket are operated in the attention term for every query feature. The resulting sparsity in the attention matrix prevents the network from attending features stemming from less-informative magnification degrees which could be regarded as noise, while retains the sequence modelling capability of standard self-attention. Extensive experiments on three public MER databases demonstrate our superiority against the state-of-the-art methods.

Recently, facial expression recognition (FER) in the wild has gained a lot of researchers' attention because it is a valuable topic to enable the FER techniques to move from the laboratory to the real applications. In this paper, we focus on this challenging but interesting topic and make contributions from three aspects. First, we present a new large-scale 'in-the-wild' dynamic facial expression database, DFEW (Dynamic Facial Expression in the Wild), consisting of over 16,000 video clips from thousands of movies. These video clips contain various challenging interferences in practical scenarios such as extreme illumination, occlusions, and capricious pose changes. Second, we propose a novel method called Expression-Clustered Spatiotemporal Feature Learning (EC-STFL) framework to deal with dynamic FER in the wild. Third, we conduct extensive benchmark experiments on DFEW using a lot of spatiotemporal deep feature learning methods as well as our proposed EC-STFL. Experimental results show that DFEW is a well-designed and challenging database, and the proposed EC-STFL can promisingly improve the performance of existing spatiotemporal deep neural networks in coping with the problem of dynamic FER in the wild. Our DFEW database is publicly available and can be freely downloaded from https://dfew-dataset.github.io/.

The emotion recognition in the wild has been a hot research topic in the field of affective computing. Though some progresses have been achieved, the emotion recognition in the wild is still an unsolved problem due to the challenge of head movement, face deformation, illumination variation etc. To deal with these unconstrained challenges, we propose a bi-modality fusion method for video based emotion recognition in the wild. The proposed framework takes advantages of the visual information from facial expression sequences and the speech information from audio. The state-of-the-art CNN based object recognition models are employed to facilitate the facial expression recognition performance. A bi-direction long short term Memory (Bi-LSTM) is employed to capture dynamic information of the learned features. Additionally, to take full advantages of the facial expression information, the VGG16 network is trained on AffectNet dataset to learn a specialized facial expression recognition model. On the other hand, the audio based features, like low level descriptor (LLD) and deep features obtained by spectrogram image, are also developed to improve the emotion recognition performance. The best experimental result shows that the overall accuracy of our algorithm on the Test dataset of the EmotiW challenge is 62.78, which outperforms the best result of EmotiW2018 and ranks 2nd at the EmotiW2019 challenge.