Jinghan Yu, Junhao Xiao, Chenyu Zhu, Jiaming Li, Jia Li, HanMing Deng, Xirui Wang, Guoli Jia, Jianjun Li, Xiang Bai, Bowen Zhou, Zhiyuan Ma. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Diffusion models pre-trained on large-scale paired image-text data achieve significant success in image editing. To convey more fine-grained visual details, subject-driven editing integrates subjects in user-provided reference images into existing scenes. However, it is challenging to obtain photorealistic results, which simulate contextual interactions, such as reflections, illumination, and shadows, induced by merging the target object into the source image. To address this issue, we propose PS-Diffusion, which ensures realistic and consistent object-scene blending while maintaining the invariance of subject appearance during editing. To be specific, we first divide the contextual interactions into those occurring in the foreground and the background areas. The effect of the former is estimated through intrinsic image decomposition, and the region of the latter is predicted in an additional background effect control branch. Moreover, we propose an effect attention module to disentangle the learning processes of interaction and subject, alleviating confusion between them. Additionally, we introduce a synthesized dataset, Replace-5K, consisting of 5,000 image pairs with invariant subject and contextual interactions via 3D rendering. Extensive quantitative and qualitative experiments on our dataset and two real-world datasets demonstrate that our method achieves state-of-the-art performance. The code is available in the https://github.com/wei-cheng777/PS-Diffusion.

Multimodal sentiment analysis has attracted extensive research attention as increasing numbers of users share images and texts to express their emotions and opinions on social media. Collecting large amounts of labeled sentiment data is an expensive and challenging task due to the high cost of labeling and unavoidable label ambiguity. Semi-supervised learning (SSL) is explored to utilize the extensive unlabeled data to alleviate the demand for annotation. However, unlike typical multimodal tasks, sentiment inconsistency between image and text degrades the performance of SSL algorithms. To address the issue, we propose SCRD, the first semi-supervised framework for image-text sentiment recognition. To better utilize the discriminative features of each modality, we decouple features into common and private parts. We then use the private features to train unimodal classifiers for enhanced modality-specific sentiment representation. Considering the complex relationships between modalities, we devise a modal selection-based attention module that adaptively identifies the dominant sentiment modality at the sample level to guide multimodal fusion. Furthermore, to prevent model predictions from over-relying on common features under the guidance of multimodal labels, we design a pseudo-label filtering strategy based on the matching degree of prediction and dominant modality. Extensive experiments and comparisons on five publicly available datasets demonstrate that SCRD outperforms state-of-the-art methods. Our code is released on https://github.com/wuyou-xia/Seek-Common-Ground-While-Reserving-Differences.

Large language models (LLMs) have taken a great step towards AGI. Meanwhile, an increasing number of domain-specific problems such as math and programming boost these general-purpose models to continuously evolve via learning deeper expertise. Now is thus the time further to extend the diversity of specialized applications for knowledgeable LLMs, though collecting high quality data with unexpected and informative tasks is challenging. In this paper, we propose to use advertisement (ad) videos as a challenging test-bed to probe the ability of LLMs in perceiving beyond the objective physical content of common visual domain. Our motivation is to take full advantage of the clue-rich and information-dense ad videos' traits, e.g., marketing logic, persuasive strategies, and audience engagement. Our contribution is three-fold: (1) To our knowledge, this is the first attempt to use ad videos with well-designed tasks to evaluate LLMs. We contribute AdsQA, a challenging ad Video QA benchmark derived from 1,544 ad videos with 10,962 clips, totaling 22.7 hours, providing 5 challenging tasks. (2) We propose ReAd-R, a Deepseek-R1 styled RL model that reflects on questions, and generates answers via reward-driven optimization. (3) We benchmark 14 top-tier LLMs on AdsQA, and our \texttt{ReAd-R}~achieves the state-of-the-art outperforming strong competitors equipped with long-chain reasoning capabilities by a clear margin.

Continual learning seeks to enable a model to assimilate knowledge from non-stationary data streams without catastrophic forgetting. Recently, methods based on Parameter-Efficient Tuning (PET) have achieved superior performance without even storing any historical exemplars, which train much fewer specific parameters for each task upon a frozen pre-trained model, and tailored parameters are retrieved to guide predictions during inference. However, reliance solely on pre-trained features for parameter matching exacerbates the inconsistency between the training and inference phases, thereby constraining the overall performance. To address this issue, we propose HRM-PET, which makes full use of the richer downstream knowledge inherently contained in the trained parameters. Specifically, we introduce a hybrid re-matching mechanism, which benefits from the initial predicted distribution to facilitate the parameter selections. The direct re-matching addresses misclassified samples identified with correct task identity in prediction, despite incorrect initial matching. Moreover, the confidence-based re-matching is specifically designed to handle other more challenging mismatched samples that cannot be calibrated by the former. Besides, to acquire task-invariant knowledge for better matching, we integrate a cross-task instance relationship distillation module into the PET-based method. Extensive experiments conducted on four datasets under five pre-trained settings demonstrate that HRM-PET performs favorably against the state-of-the-art methods. The code is available in the https://github.com/wei-cheng777/HRM-PET.

As one of the most popular and sought-after generative models in recent years, diffusion models have sparked the interests of many researchers and steadily shown excellent advantage in various generative tasks such as image synthesis, video generation, bioinformatics engineering, 3D scene rendering and multimodal generation, relying on their dense theoretical principles and reliable application practices. The remarkable success of these recent efforts on diffusion models comes largely from progressive design principles and efficient architecture, training, inference, and deployment methodologies. However, there has not been a comprehensive and in-depth review to summarize these principles and practices to help the rapid understanding and application of diffusion models. In this survey, we provide a new efficiency-oriented perspective on these existing efforts, which mainly focuses on the profound principles and efficient practices in architecture designs, model training, fast inference and reliable deployment, to guide further theoretical research, algorithm migration and model application for new scenarios in a reader-friendly way.

With the great success of text-conditioned diffusion models in creative text-to-image generation, various text-driven image editing approaches have attracted the attentions of many researchers. However, previous works mainly focus on discreteness-sensitive instructions such as adding, removing or replacing specific objects, background elements or global styles (i.e., “hard editing”), while generally ignoring subject-binding but semantically fine-changing continuity-sensitive instructions such as actions, poses or adjectives, and so on (i.e., “soft editing”), which hampers generative AI from generating user-customized visual contents. To mitigate this predicament, we propose a spatio-temporal guided adaptive editing algorithm AdapEdit, which realizes adaptive image editing by introducing a soft-attention strategy to dynamically vary the guiding degree from the editing conditions to visual pixels from both temporal and spatial perspectives. Note our approach has a significant advantage in preserving model priors and does not require model training, fine-tuning, extra data, or optimization. We present our results over a wide variety of raw images and editing instructions, demonstrating competitive performance and showing it significantly outperforms the previous approaches. Code is available: https://github.com/AnonymousPony/adap-edit.

Camouflaged vision perception is an important vision task with numerous practical applications. Due to the expensive collection and labeling costs, this community struggles with a major bottleneck that the species category of its datasets is limited to a small number of object species. However, the existing camouflaged generation methods require specifying the background manually, thus failing to extend the camouflaged sample diversity in a low-cost manner. In this paper, we propose a Latent Background Knowledge Retrieval-Augmented Diffusion (LAKE-RED) for camouflaged image generation. To our knowledge, our contributions mainly include: (1) For the first time, we propose a camouflaged generation paradigm that does not need to re-eive any background inputs. (2) Our LAKE-RED is the first knowledge retrieval-augmented method with interpretability for camouflaged generation, in which we propose an idea that knowledge retrieval and reasoning enhancement are separated explicitly, to alleviate the task-specific chal-lenges. Moreover, our method is not restricted to specific foreground targets or backgrounds, offering a potential for extending camouflaged vision perception to more diverse domains. (3) Experimental results demonstrate that our method outperforms the existing approaches, generating more realistic camouflage images. Our source code is released on https://github.com/PanchengZhaoILAKE-RED.

Recently, stable diffusion (SD) models have typically flourished in the field of image synthesis and personalized editing, with a range of photorealistic and unprecedented images being successfully generated. As a result, widespread interest has been ignited to develop and use various SD-based tools for visual content creation. However, the exposure of AI-created content on public platforms could raise both legal and ethical risks. In this regard, the traditional methods of adding watermarks to the already generated images (i.e. post-processing) may face a dilemma (e.g., being erased or modified) in terms of copyright protection and content monitoring, since the powerful image inversion and text-to-image editing techniques have been widely explored in SD-based methods. In this work, we propose a Safe and high-traceable Stable Diffusion framework (namely Safe-SD) to adaptively implant the graphical watermarks (e.g., QR code) into the imperceptible structure-related pixels during the generative diffusion process for supporting text-driven invisible watermarking and detection. Different from the previous high-cost injection-then-detection training framework, we design a simple and unified architecture, which makes it possible to simultaneously train watermark injection and detection in a single network, greatly improving the efficiency and convenience of use. Moreover, to further support text-driven generative watermarking and deeply explore its robustness and high-traceability, we elaborately design a λ-sampling and λ-encryption algorithm to fine-tune a latent diffuser wrapped by a VAE for balancing high-fidelity image synthesis and high-traceable watermark detection. We present our quantitative and qualitative results on two representative datasets LSUN, COCO and FFHQ, demonstrating state-of-the-art performance of Safe-SD and showing it significantly outperforms the previous approaches.

In online chatting, people increasingly prefer using stickers to supplement or replace text for replies, as sticker images can express vivid and varied emotions. The Sticker Response Selection (SRS) task aims to predict the sticker image that is most relevant to the history dialogue. Previous researches explore the semantic similarity between context and stickers, overlooking both unimodal and cross-modal emotional information. In this paper, we propose a 'Perceive before Respond' (PBR) training paradigm. PBR perceives sticker emotions through a knowledge distillation module. Variety representations of each emotion category are acquired from the large-scale sticker emotion recognition dataset and distilled into our model to enhance emotion comprehension. We further distinguish stickers with similar subject elements under the same topic. We perform contrastive learning at both inter- and intra-topic levels to obtain discriminative and diverse sticker representations. In addition, we improve the hard negative sampling method for image-text matching based on cross-modal sentiment association, conducting hard sample mining from both semantic similarity and sentiment polarity similarity for sticker-to-dialogue and dialogue-to-sticker. Extensive experiments verify the effectiveness of each proposed component. Ablation experiments on different backbone networks demonstrate the generality of our approach. Our code is released on https://github.com/wuyou-xia/Perceive-before-Respond.

Emotion and sentiment analysis (ESA) assists machines to serve humans more intelligently. However, collecting large-scale high-quality datasets for training ESA models in a supervised manner is expensive, time-consuming, and difficult in practice. This tutorial focuses on the label-efficient ESA (LeESA) learning methods. Specifically, we first introduce the stimuli and characteristics of emotion and then illustrate seven typical training paradigms, followed by applications and future directions of LeESA.

Emotions can be perceived from a person's gait, i.e., their walking style. Existing methods on gait emotion recognition mainly leverage the posture information as input, but ignore the body movement, which contains complementary information for recognizing emotions evoked in the gait. In this paper, we propose a Bilateral Posture and Movement Graph Convolutional Network (BPM-GCN) that consists of two parallel streams, namely posture stream and movement stream, to recognize emotions from two views. The posture stream aims to explicitly analyse the emotional state of the person. Specifically, we design a novel regression constraint based on the hand-engineered features to distill the prior affective knowledge into the network and boost the representation learning. The movement stream is designed to describe the intensity of the emotion, which is an implicitly cue for recognizing emotions. To achieve this goal, we employ a higher-order velocity-acceleration pair to construct graphs, in which the informative movement features are utilized. Besides, we design a PM-Interacted feature fusion mechanism to adaptively integrate the features from the two streams. Therefore, the two streams collaboratively contribute to the performance from two complementary views. Extensive experiments on the largest benchmark dataset Emotion-Gait show that BPM-GCN performs favorably against the state-of-the-art approaches (with at least$4.59\%$performance improvement). The source code is released onhttps://github.com/exped1230/BPM-GCN.

Sarcasm indicates the literal meaning is contrary to the real attitude. Considering the popularity and complementarity of image-text data, we investigate the task of multi-modal sarcasm detection. Different from other multi-modal tasks, for the sarcastic data, there exists intrinsic incongruity between a pair of image and text as demonstrated in psychological theories. To tackle this issue, we propose a Dual Incongruity Perceiving (DIP) network consisting of two branches to mine the sarcastic information from factual and affective levels. For the factual aspect, we introduce a channel-wise reweighting strategy to obtain semantically discriminative embeddings, and leverage gaussian distribution to model the uncertain correlation caused by the incongruity. The distribution is generated from the latest data stored in the memory bank, which can adaptively model the difference of semantic similarity between sarcastic and non-sarcastic data. For the affective aspect, we utilize siamese layers with shared parameters to learn cross-modal sentiment information. Furthermore, we use the polarity value to construct a relation graph for the mini-batch, which forms the continuous contrastive loss to acquire affective embeddings. Extensive experiments demonstrate that our proposed method performs favorably against state-of-the-art approaches. Our code is released on https://github.com/downdric/MSD.

Facial Expression Recognition (FER) plays a crucial role in the real-world applications. However, large-scale FER datasets collected in the wild usually contain noises. More importantly, due to the ambiguity of emotion, facial images with multiple emotions are hard to be distinguished from the ones with noisy labels. Therefore, it is challenging to train a robust model for FER. To address this, we propose Emotion Ambiguity-SEnsitive cooperative networks (EASE) which contain two components. First, the ambiguity-sensitive learning module divides the training samples into three groups. The samples with small-losses in both networks are considered as clean samples, and the ones with large-losses are noisy. Note for the conflict samples that one network disagrees with the other, we distinguish the samples conveying ambiguous emotions from the ones with noises, using the polarity cues of emotions. Here, we utilize KL divergence to optimize the networks, enabling them to pay attention to the non-dominant emotions. The second part of EASE aims to enhance the diversity of the cooperative networks. With the training epochs increasing, the cooperative networks would converge to a consensus. We construct a penalty term according to the correlation between the features, which helps the networks learn diverse representations from the images. Extensive experiments on 6 popular facial expression datasets demonstrate that EASE outperforms the state-of-the-art approaches.

Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this survey, we will comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges - the affective gap, perception subjectivity, and label noise and absence. We begin with an introduction to the key emotion representation models that have been widely employed in AICA and description of available datasets for performing evaluation with quantitative comparison of label noise and dataset bias. We then summarize and compare the representative approaches on (1) emotion feature extraction, including both handcrafted and deep features, (2) learning methods on dominant emotion recognition, personalized emotion prediction, emotion distribution learning, and learning from noisy data or few labels, and (3) AICA based applications. Finally, we discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.