As forgery types continue to emerge consistently, Incremental Face Forgery Detection (IFFD) has become a crucial paradigm. However, existing methods typically rely on data replay or coarse binary supervision, which fails to explicitly constrain the feature space, leading to severe feature drift and catastrophic forgetting. To address this, we propose AIFIND, Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection, which leverages semantic anchors to stabilize incremental learning. We design the Artifact-Driven Semantic Prior Generator to instantiate invariant semantic anchors, establishing a fixed coordinate system from low-level artifact cues. These anchors are injected into the image encoder via Artifact-Probe Attention, which explicitly constrains volatile visual features to align with stable semantic anchors. Adaptive Decision Harmonizer harmonizes the classifiers by preserving angular relationships of semantic anchors, maintaining geometric consistency across tasks. Extensive experiments on multiple incremental protocols validate the superiority of AIFIND.

In this paper, we address the challenge of procedure planning in instructional videos, aiming to generate coherent and task-aligned action sequences from start and end visual observations. Previous work has mainly relied on text-level supervision to bridge the gap between observed states and unobserved actions, but it struggles with capturing intricate temporal relationships among actions. Building on these efforts, we propose the Masked Temporal Interpolation Diffusion (MTID) model that introduces a latent space temporal interpolation module within the diffusion model. This module leverages a learnable interpolation matrix to generate intermediate latent features, thereby augmenting visual supervision with richer mid-state details. By integrating this enriched supervision into the model, we enable end-to-end training tailored to task-specific requirements, significantly enhancing the model's capacity to predict temporally coherent action sequences. Additionally, we introduce an action-aware mask projection mechanism to restrict the action generation space, combined with a task-adaptive masked proximity loss to prioritize more accurate reasoning results close to the given start and end states over those in intermediate steps. Simultaneously, it filters out task-irrelevant action predictions, leading to contextually aware action sequences. Experimental results across three widely used benchmark datasets demonstrate that our MTID achieves promising action planning performance on most metrics.

An emotional support conversation (ESC) system aims to reduce users' emotional distress by engaging in conversation using various reply strategies as guidance. To develop instructive reply strategies for an ESC system, it is essential to consider the dynamic transitions of users' emotional states through the conversational turns. However, existing methods for strategy-guided ESC systems struggle to capture these transitions as they overlook the inference of fine-grained user intentions. This oversight poses a significant obstacle, impeding the model's ability to derive pertinent strategy information and, consequently, hindering its capacity to generate emotionally supportive responses. To tackle this limitation, we propose a novel dynamic strategy prompt reasoning model (DSR), which leverages sparse context relation deduction to acquire adaptive representation of reply strategies as prompts for guiding the response generation process. Specifically, we first perform turn-level commonsense reasoning with different approaches to extract auxiliary knowledge, which enhances the comprehension of user intention. Then we design a context relation deduction module to dynamically integrate interdependent dialogue information, capturing granular user intentions and generating effective strategy prompts. Finally, we utilize the strategy prompts to guide the generation of more relevant and supportive responses. DSR model is validated through extensive experiments conducted on a benchmark dataset, demonstrating its superior performance compared to the latest competitive methods in the field.

Active learning (AL) is to design label-efficient algorithms by labeling the most representative samples. It reduces annotation cost and attracts increasing attention from the community. However, previous AL methods suffer from the inadequacy of annotations and unreliable uncertainty estimation. Moreover, we find that they ignore the intra-diversity of selected samples, which leads to sampling redundancy. In view of these challenges, we propose an inductive state-relabeling adversarial AL model (ISRA) that consists of a unified representation generator, an inductive state-relabeling discriminator, and a heuristic clique rescaling module. The generator introduces contrastive learning to leverage unlabeled samples for self-supervised training, where the mutual information is utilized to improve the representation quality for AL selection. Then, we design an inductive uncertainty indicator to learn the state score from labeled data and relabel unlabeled data with different importance for better discrimination of instructive samples. To solve the problem of sampling redundancy, the heuristic clique rescaling module measures the intra-diversity of candidate samples and recurrently rescales them to select the most informative samples. The experiments conducted on eight datasets and two imbalanced scenarios show that our model outperforms the previous state-of-the-art AL methods. As an extension on the cross-modal AL task, we apply ISRA to the image captioning and it also achieves superior performance.

Active learning (AL) is designed to construct a high-quality labeled dataset by iteratively selecting the most informative samples. Such sampling heavily relies on data representation, while recently pre-training is popular for robust feature learning. However, as pre-training utilizes low-level pretext tasks that lack annotation, directly using pre-trained representation in AL is inadequate for determining the sampling score. To address this problem, we propose a downstream-pretext domain knowledge traceback (DOKT) method that traces the data interactions of downstream knowledge and pre-training guidance for selecting diverse and instructive samples near the decision boundary. DOKT consists of a traceback diversity indicator and a domain-based uncertainty estimator. The diversity indicator constructs two feature spaces based on the pre-training pretext model and the downstream knowledge from annotation, by which it locates the neighbors of unlabeled data from the downstream space in the pretext space to explore the interaction of samples. With this mechanism, DOKT unifies the data relations of low-level and high-level representations to estimate traceback diversity. Next, in the uncertainty estimator, domain mixing is designed to enforce perceptual perturbing to unlabeled samples with similar visual patches in the pretext space. Then the divergence of perturbed samples is measured to estimate the domain uncertainty. As a result, DOKT selects the most diverse and important samples based on these two modules. The experiments conducted on ten datasets show that our model outperforms other state-of-the-art methods and generalizes well to various application scenarios such as semantic segmentation and image captioning.

Joint video moment retrieval (MR) and highlight detection (HD) aims to find relevant video moments according to the query text. Existing methods are fully supervised based on manual annotation, and their coarse multi-modal information interactions easily lose details about video and text. In addition, some tasks introduce weakly supervised learning with random masks, while the single masking forces the model to focus on masked words and ignore multi-modal contextual information. In view of this, we attempt weakly supervised joint tasks (MR+HD) and propose Dynamic Contrastive Learning with Pseudo-Sample Intervention (CPI) for better multi-modal video comprehension. First, we design pseudo-samples over random masks for a more efficient contrastive learning manner. We introduce a proportional sampling strategy for pseudo-samples to ensure the semantic difference between the pseudo-samples and the query text. This balances the over-reliance from single random mask to global text semantics and makes the model learn multimodal context from each word fairly. Second, we design dynamic intervention contrastive loss to enhance the core feature-matching ability of the model dynamically. We add pseudo-sample intervention when negative proposals are close to positive proposals. This can help the model overcome the vision confusion phenomenon and achieve semantic similarity instead of word similarity. Extensive experiments demonstrate the effectiveness of CPI and the potential of weakly supervised joint tasks.

In recent years, multimodal task-oriented dialogue systems have attracted increasing attention from communities, owing to their ability to naturally and efficiently provide user service. Despite the commercial value of multimodal dialogue systems, they are still confronted with two challenges: (1) capture users' intention from lengthy context and side knowledge for question comprehension; (2) jointly consider the multimodal information for response generation. In view of the challenges, previous methods designed for specific scenario lack auxiliary reasoning structures with effective modality interaction, which hinders the comprehension of user's needs and impedes the generation of desired responses. To address these issues, we propose a Modality-aligned Thought Chain Reasoning (MaTCR) framework to insert explicit reasoning process for multimodal task-oriented dialogue generation. We construct a multimodal thought chain by summarizing intermediate user queries from aligned visual and textual context, which helps to guide the comprehension of user intentions for generating reasonable responses. To effectively extract and integrate multimodal information for high-quality thought chain reasoning, we design a multimodal reasoner consisting of visual representation learning and modality-aligned fusion. We comparatively justify MaTCR with several strong baselines, including the currently highly regarded large language model. Extensive experiments over a benchmark dataset demonstrate that MaTCR outperforms the existing methods and provides stronger interpretability.

Video captioning is a challenging task that aims to generate linguistic description based on video content. Most methods only incorporate visual features (2D/3D) as input for generating visual and non-visual words in the caption. However, generating non-visual words usually depends more on sentence-context than visual features. The wrong non-visual words can reduce the sentence fluency and even change the meaning of sentence. In this paper, we propose a syntax-guided hierarchical attention network (SHAN), which leverages semantic and syntax cues to integrate visual and sentence-context features for captioning. First, a globally-dependent context encoder is designed to extract the global sentence-context feature that facilitates generating non-visual words. Then, we introduce hierarchical content attention and syntax attention to adaptively integrate features in terms of temporality and feature characteristics respectively. Content attention helps focus on time intervals related to the semantic of current word, while cross-modal syntax attention uses syntax information to model importance of different features for target word’s generation. Moreover, such hierarchical attention can enhance the model interpretability for captioning. Experiments on MSVD and MSR-VTT datasets show the comparable performance of our method compared with current methods.

Graph neural networks (GNNs) emerged recently as a standard toolkit for learning from data on graphs. Current GNN designing works depend on immense human expertise to explore different message-passing mechanisms, and require manual enumeration to determine the proper message-passing depth. Inspired by the strong searching capability of neural architecture search (NAS) in CNN, this paper proposes Graph Neural Architecture Search (GNAS) with novel-designed search space. The GNAS can automatically learn better architecture with the optimal depth of message passing on the graph. Specifically, we design Graph Neural Architecture Paradigm (GAP) with tree-topology computation procedure and two types of fine-grained atomic operations (feature filtering & neighbor aggregation) from message-passing mechanism to construct powerful graph network search space. Feature filtering performs adaptive feature selection, and neighbor aggregation captures structural information and calculates neighbors’ statistics. Experiments show that our GNAS can search for better GNNs with multiple message-passing mechanisms and optimal message-passing depth. The searched network achieves remarkable improvement over state-of-the-art manual designed and search-based GNNs on five large-scale datasets at three classical graph tasks. Codes can be found at https://github.com/phython96/GNAS-MP.

Active learning is to design label-efficient algorithms by sampling the most representative samples to be labeled by an oracle. In this paper, we propose a state relabeling adversarial active learning model (SRAAL), that leverages both the annotation and the labeled/unlabeled state information for deriving the most informative unlabeled samples. The SRAAL consists of a representation generator and a state discriminator. The generator uses the complementary annotation information with traditional reconstruction information to generate the unified representation of samples, which embeds the semantic into the whole data representation. Then, we design an online uncertainty indicator in the discriminator, which endues unlabeled samples with different importance. As a result, we can select the most informative samples based on the discriminator's predicted state. We also design an algorithm to initialize the labeled pool, which makes subsequent sampling more efficient. The experiments conducted on various datasets show that our model outperforms the previous state-of-art active learning methods and our initially sampling algorithm achieves better performance.

Most image captioning models achieve superior performances with the help of large-scale surprised training data, but it is prohibitively costly to label the image captions. To solve this problem, we propose a structural semantic adversarial active learning (SSAAL) model that leverages both visual and textual information for deriving the most representative samples while maximizing the image captioning performance. SSAAL consists of a semantic constructor, a snapshot& caption (SC) supervisor, and a labeled/unlabeled state discriminator. The constructor is designed to generate a structural semantic representation describing the objects, attributes and object relationships in the image. The SC supervisor is proposed to supervise this representation at the word-level and sentence-level in a multi-task learning manner, which directly relates the representation to ground-truth captions and updates it in the caption generating process. Finally, we introduce a state discriminator to predict the sample state and select images with sufficient semantic and fine-grained diversity. Extensive experiments on standard captioning dataset show that our model outperforms other active learning methods and achieves a competitive performance even though selecting a small amount of samples.