The emotional video captioning (EVC) task, which aims to generate factual descriptions based on the perceived subtle visual emotion cues, has received more and more attention and research. However, EVC is essentially an objective video captioning task, and ignores the subjective emotional reactions of video viewers, which cannot reflect personalized affective understandings of different viewers on the same video. To fill the research gap, we investigate the subjective video captioning (SVC) task in this paper, which aims to generate emotional captions by incorporating viewers' personalized emotional reactions upon the EVC task. SVC is extremely challenging, which lies in two aspects: 1) the correlative emotion perception between subjective and objective emotions and 2) the collaborative generation between emotional and factual information. To this end, we propose the Subjective-Objective Emotion-Correlated Generation Network (SO-ECGN) in this paper. Specifically, our SO-ECGN leverages the proposed dynamic mask attention and emotion domain shifting module to achieve the objective emotion incremental learning, and then, a subjective-objective emotions correlation module is proposed to adaptively combine two perspective emotions to provide accurate emotion guidance (i.e., emotional polarity and intensity) for each generation step. Furthermore, an emotion-correlated decoder is proposed to generate subjective captions by adaptively referring to factual information and emotional information. Extensive experiments on three challenging datasets demonstrate the superiority of our approach and each proposed module, i.e., reaching 79.2%, 45.1% on BLEU-1, CIDEr metrics on EmVidCap-L dataset.

Affective Explanation Captioning (AEC) aims to perform viewer-centered visual emotion analysis by not only identifying the emotions evoked by an image but also explaining their underlying causes. Prior efforts have achieved promising results by fine-tuning LLMs on affective data; however, two key challenges remain: 1) the inherent subjectivity of human emotion leads to diverse interpretations of the same image, making it difficult for models to catch dominant emotions; and 2) the affective gap between abstract emotions and concrete visual content hinders models from capturing both semantic and emotional aspects effectively. To tackle these challenges, we propose Consensus-Prompted Emotion Reasoning (CPER), a new framework that explicitly models emotional diversity and enforces emotional-semantic alignment. Inspired by psychological studies, we observe that common emotional patterns often emerge within certain groups, which we refer to as affective consensus. Capturing this consensus across varying levels is helpful for bridging the subjectivity in AEC. Specifically, we introduce a consensus-based bucket prompt, which depicts the consensus level of each emotional perspective, serving as a control signal to adjust the emotion reasoning. To reconcile abstract emotion understanding and concrete visual grounding, we design a dual-space representation, where a CLIP encoder extracts objective semantic evidence and an emotion encoder captures abstract affective cues for AEC. Furthermore, an emotion consistency learning strategy is devised, which explicitly aligns the generated explanation with the input image and the emotion label, ensuring both emotionally and semantically grounded explanations. Extensive experiments on three benchmark datasets, ranging from visual arts (ArtEmis v1.0 and ArtEmis v2.0) and real-world images (Affection), demonstrate the effectiveness of our CPER in terms of emotional diversity and semantic coherence compared to state-of-the-art methods. Our code is publicly available at https://github.com/songpipi/CPER.

Multivariate time series (MTS) anomaly detection is a critical task that involves identifying abnormal patterns or events in data that consist of multiple interrelated time series. In order to better model the complex interdependence between entities and the various inherent characteristics of each entity, the graph neural network (GNN) based methods are widely adopted by existing methods. In each layer of GNN, node features aggregate information from their neighboring nodes to update their information. In doing so, from shallow layer to deep layer in GNN, original individual node features continue to be weakened and more structural information, i.e., from short-distance neighborhood to long-distance neighborhood, continues to be enhanced. However, research to date has largely ignored the understanding of how hierarchical graph information is represented and their characteristics that can benefit anomaly detection. Existing methods simply leverage the output from the last layer of GNN for anomaly estimation while neglecting the essential information contained in the intermediate GNN layers. To address such limitations, in this paper, we propose a Graph Mixture of Experts (Graph-MoE) network for multivariate time series anomaly detection, which incorporates the mixture of experts (MoE) module to adaptively represent and integrate hierarchical multi-layer graph information into entity representations. It is worth noting that our Graph-MoE can be integrated into any GNN-based MTS anomaly detection method in a plug-and-play manner. In addition, the memory-augmented routers are proposed in this paper to capture the correlation temporal information in terms of the global historical features of MTS to adaptively weigh the obtained entity representations to achieve successful anomaly estimation. Extensive experiments on five challenging datasets prove the superiority of our approach and each proposed module.

Emotional Video Captioning (EVC) is an emerging task that aims to describe factual content with the intrinsic emotions expressed in videos. Existing EVC methods perceive global emotional cues through visual features at first, and then combine them with the video features to guide the emotional caption generation, which ignores the critical characteristic of the EVC task that emotional cues have intrinsic motivational causes reflected in the video content. Such video causes have a facilitative effect on both emotion perception and emotion-attributed caption generation. To this end, a multi-round mutual emotion-cause pair extraction network (MM-ECPE) is proposed in this paper for the joint extraction of emotional cues and visual causes through iterative mutual refinement. Specifically, in the 1st-round mutual learning, we propose a spatio-temporal disentangled visual adaptive refinement (ST-DVAR) and a multi-level video-guided emotion affine transformation (MV-EAT) to achieve preliminary refinement on video features and emotion lexicon to eliminate the noise caused by emotion-irrelevant visual information and video-irrelevant emotional information. Then, in the 2nd-round mutual learning, we exploit the cross-attention of the preliminary refined features and the original features to obtain the ultimate emotional cues and visual causes, and couple them in pair-wise extraction through contrastive loss. Overall, our approach optimizes complex semantic understanding and emotion perception of videos, leading to a promising performance in emotional captioning. Extensive experiments on three challenging datasets demonstrate the superiority of our approach and each proposed module, e.g., improving the latest records by +97.5% and +76.2% w.r.t. CIDEr and CFS, respectively, on the EVC-MSVD dataset.

Composed Image Retrieval (CIR) takes a composed query of a reference image and a text describing the user's intention, with the aim to retrieve the target image under both conditions. Conventional CIR approaches heavily rely on massive annotated triplets, which often comes at a considerable cost. Zero-Shot CIR (ZS-CIR) offers a new solution that can perform diverse CIR tasks without training on the triplet datasets. The key to the ZS-CIR task is to make specified changes to specific objects in the reference image based on the text. Previous works utilize a projection module to map the reference image into single or multiple pseudo words. However, they are either only applicable to single-object scenarios, or naively convert entire image features into multiple pseudo words and fail to focus on the desired target objects specified by the text description. In this work, we rethink how to learn pseudo words based on the objects attended by the text and propose a Multi-Object Aware ZS-CIR framework (MOA). Specifically, a multi-object recognizer first recognizes valid objects in the reference image guided by a set of learnable object queries. Then, we devise an object filtering strategy, which utilizes contextual prompts comprised of noun categories to guide the model in precisely screening out the objects that need to be modified. Finally, the pseudo word learning branch adaptively converts the screened objects into multiple pseudo words for accurate ZS-CIR. Although simple, our MOA consistently outperforms previous state-of-the-art methods across diverse benchmarks and even achieves competitive results with many supervised methods.

Video summarization aims to generate a compact summary of the original video by selecting and combining the most representative parts. Most existing approaches only focus on recognizing key video segments to generate the summary, which lacks holistic considerations. The transitions between selected video segments are usually abrupt and inconsistent, making the summary confusing. Indeed, the coherence of video summaries is crucial to improve the quality and user viewing experience. However, the coherence between video segments is hard to measure and optimize from a pure vision perspective. To this end, we propose a Language-guided Segment Coherence-Aware Network (LS-CAN), which integrates entire coherence considerations into the key segment recognition. The main idea of LS-CAN is to explore the coherence of corresponding text modality to facilitate the entire coherence of the video summary, which leverages the natural property in the language that contextual coherence is easy to measure. In terms of text coherence measures, specifically, we propose the multi-graph correlated neural network module (MGCNN), which constructs a graph for each sentence based on three key components, i.e., subject, attribute, and action words. For each sentence pair, the node features are then discriminatively learned by incorporating neighbors of its own graph and information of its dual graph, reducing the error of synonyms or reference relationships in measuring the correlation between sentences, as well as the error caused by considering each component separately. In doing so, MGCNN utilizes subject agreement, attribute coherence, and action succession to measure text coherence. Besides, with the help of large language models, we augment the original text coherence annotations, improving the ability of MGCNN to judge coherence. Extensive experiments on three challenging datasets demonstrate the superiority of our approach and each proposed module, especially improving the latest records by +3.8%, +14.2% and +12% w.r.t. F1 scores, $\tau $ and $\rho $ metrics on the BLiSS dataset.

Partially relevant video retrieval (PRVR) is a practical yet challenging task in text-to-video retrieval, where videos are untrimmed and contain much background content. The pursuit here is of both effective and efficient solutions to capture the partial correspondence between text queries and untrimmed videos. Existing PRVR methods, which typically focus on modeling multi-scale clip representations, however, suffer from content independence and information redundancy, impairing retrieval performance. To overcome these limitations, we propose a simple yet effective approach with active moment discovering (AMDNet). We are committed to discovering video moments that are semantically consistent with their queries. By using learnable span anchors to capture distinct moments and applying masked multi-moment attention to emphasize salient moments while suppressing redundant backgrounds, we achieve more compact and informative video representations. To further enhance moment modeling, we introduce a moment diversity loss to encourage different moments of distinct regions and a moment relevance loss to promote semantically query-relevant moments, which cooperate with a partially relevant retrieval loss for end-to-end optimization. Extensive experiments on two large-scale video datasets (i.e., TVR and ActivityNet Captions) demonstrate the superiority and efficiency of our AMDNet. In particular, AMDNet is about 15.5 times smaller (#parameters) while 6.0 points higher (SumR) than the up-to-date method GMMFormer on TVR.

Radiology report generation (RRG) aims to automatically generate a free-text description from a specific clinical radiograph, e.g., chest X-Ray images. Existing approaches tend to perform RRG with specific models trained on the public yet limited data from scratch, where they often lead to inferior performance owing to the problem of inefficient capabilities in both aligning visual and textual features and generating informative reports accordingly. Currently, large language models (LLMs) offered a promising solution to text generation with their power in learning from big data, especially for cross-modal scenarios such as RRG. However, most existing LLMs are pre-trained on general data, and suffer from the same problem of conventional approaches caused by knowledge gap between general and medical domain if they are applied to RRG. Therefore in this paper, we propose an approach to bootstrapping LLMs for RRG with a in-domain instance induction and a coarse-to-fine decoding process. Specifically, the in-domain instance induction process learns to align the LLM to radiology reports from general texts through contrastive learning. The coarse-to-fine decoding performs a text elevating process for those reports from the ranker, further enhanced with visual features and refinement prompts. Experimental results on two prevailing RRG datasets, namely, IU X-Ray and MIMIC-CXR, demonstrate the superiority of our approach to previous state-of-the-art solutions. Further analyses illustrate that, for the LLM, the induction process enables it to better align with the medical domain and the coarse-to-fine generation allows it to conduct more precise text generation.

Radiology report generation (RRG) aims to automatically provide observations and insight into a patient’s condition based on radiology images, which is able to greatly reduce the workload of physicians on the premise of ensuring the quality of medical treatment. Existing works leverage the Transformer decoder to generate reports word-by-wordly. However, unlike image captioning, radiology reports are long text containing many semantic words. The autoregressive method, such as the Transformer-base method, will accumulate errors in the generation process and generate unsatisfied reports. Benefiting from the recent success of Diffusion, we propose a novel Diffusion-based paradigm for RRG, which leverages visual information as a condition, making the generation process focus on pathological features within the radiology image. Meanwhile, we integrate a discriminator into each layer of the Diffusion to actively judge whether the generated words are meaningful, which, on the one hand, controls the length of predicted reports and, on the other hand, calibrates confidence scores and token generation results, improving the quality of the generated reports. Extensive experiment results demonstrate the superiority of our proposed method. Source code is available at: https://github.com/Yuda-Jin/D-2-Net.

Emotional Video Captioning (EVC) is an emerging task that aims to describe factual content with the intrinsic emotions expressed in videos. The essential of the EVC task is to effectively perceive subtle and ambiguous visual emotional cues during the caption generation, which is neglected by the traditional video captioning. Existing emotional video captioning methods perceive global visual emotional cues at first, and then combine them with the video features to guide the emotional caption generation, which neglects two characteristics of the EVC task. Firstly, their methods neglect the dynamic subtle changes in the intrinsic emotions of the video, which makes it difficult to meet the needs of common scenes with diverse and changeable emotions. Secondly, as their methods incorporate emotional cues into each step, the guidance role of emotion is overemphasized, which makes factual content more or less ignored during generation. To this end, we propose a dual-path collaborative generation network, which dynamically perceives visual emotional cues evolutions while generating emotional captions by collaborative learning. The two paths promote each other and significantly improve the generation performance. Specifically, in the dynamic emotion perception path, we propose a dynamic emotion evolution module, which first aggregates visual features and historical caption features to summarize the global visual emotional cues, and then dynamically selects emotional cues required to be re-composed at each stage as well as re-composed them to achieve emotion evolution by dynamically enhancing or suppressing different granularity subspace's semantics. Besides, in the adaptive caption generation path, to balance the description of factual content and emotional cues, we propose an emotion adaptive decoder, which firstly estimates emotion intensity via the alignment of emotional features and historical caption features at each generation step, and then, emotional guidance adaptively incorporate into the caption generation based on the emotional intensity. Thus, our methods can generate emotion-related words at the necessary time step, and our caption generation balances the guidance of factual content and emotional cues well. Extensive experiments on three challenging datasets demonstrate the superiority of our approach and each proposed module.

Automatic live video commenting is getting increasing attention due to its significance in narration generation, topic explanation, etc. However, the diverse sentiment consideration of the generated comments is missing from current methods. Sentimental factors are critical in interactive commenting, and there has been lack of research so far. Thus, in this article, we propose a Sentiment-oriented Transformer-based Variational Autoencoder (So-TVAE) network, which consists of a sentiment-oriented diversity encoder module and a batch attention module, to achieve diverse video commenting with multiple sentiments and multiple semantics. Specifically, our sentiment-oriented diversity encoder elegantly combines a VAE and random mask mechanism to achieve semantic diversity under sentiment guidance, which is then fused with cross-modal features to generate live video comments. A batch attention module is also proposed in this article to alleviate the problem of missing sentimental samples, caused by the data imbalance that is common in live videos as the popularity of videos varies. Extensive experiments on Livebot and VideoIC datasets demonstrate that the proposed So-TVAE outperforms the state-of-the-art methods in terms of the quality and diversity of generated comments. Related code is available at https://github.com/fufy1024/So-TVAE .

Image captioning (IC), bringing vision to language, has drawn extensive attention. A crucial aspect of IC is the accurate depiction of visual relations among image objects. Visual relations encompass two primary facets: content relations and structural relations. Content relations, which comprise geometric positions content (i.e., distances and sizes) and semantic interactions content (i.e., actions and possessives), unveil the mutual correlations between objects. In contrast, structural relations pertain to the topological connectivity of object regions. Existing Transformer-based methods typically resort to geometric positions to enhance the visual relations, yet only using the shallow geometric content is unable to precisely cover actional content correlations and structural connection relations. In this article, we adopt a comprehensive perspective to examine the correlations between objects, incorporating both content relations (i.e., geometric and semantic relations) and structural relations, with the aim of generating plausible captions. To achieve this, first, we construct a geometric graph from bounding box features and a semantic graph from the scene graph parser to model the content relations. Innovatively, we construct a topology graph that amalgamates the sparsity characteristics of the geometric and semantic graphs, enabling the representation of image structural relations. Second, we propose a novel unified approach to enrich image relation representations by integrating semantic, geometric, and structural relations into self-attention. Finally, in the language decoding stage, we further leverage the semantic relation as prior knowledge to generate accurate words. Extensive experiments on MS-COCO dataset demonstrate the effectiveness of our model, with improvements of CIDEr from 128.6% to 136.6%. Codes have been released at https://github.com/CrossmodalGroup/ER-SAN/tree/main/VG-Cap .

Text style transfer (TST) is an important task in natural language generation, which aims to alter the stylistic attributes (e.g., sentiment) of a sentence and keep its semantic meaning unchanged.Most existing studies mainly focus on the transformation between styles, yet ignore that this transformation can be actually carried out via different hidden transfer patterns.To address this problem, we propose a novel approach, contrastive transfer pattern mining (CTPM), which automatically mines and utilizes inherent latent transfer patterns to improve the performance of TST.Specifically, we design an adaptive clustering module to automatically discover hidden transfer patterns from the data, and introduce contrastive learning based on the discovered patterns to obtain more accurate sentence representations, and thereby benefit the TST task.To the best of our knowledge, this is the first work that proposes the concept of transfer patterns in TST, and our approach can be applied in a plug-andplay manner to enhance other TST methods to further improve their performance.Extensive experiments on benchmark datasets verify the effectiveness and generality of our approach.1

Having the difficulty of solving the semantic gap between images and texts for the image captioning task, conventional studies in this area paid some attention to treating semantic concepts as a bridge between the two modalities and improved captioning performance accordingly.Although promising results on concept prediction were obtained, the aforementioned studies normally ignore the relationship among concepts, which relies on not only objects in the image, but also word dependencies in the text, so that offers a considerable potential for improving the process of generating good descriptions.In this paper, we propose a structured concept predictor (SCP) to predict concepts and their structures, then we integrate them into captioning, so as to enhance the contribution of visual signals in this task via concepts and further use their relations to distinguish cross-modal semantics for better description generation.Particularly, we design weighted graph convolutional networks (W-GCN) to depict concept relations driven by word dependencies, and then learns differentiated contributions from these concepts for following decoding process.Therefore, our approach captures potential relations among concepts and discriminatively learns different concepts, so that effectively facilitates image captioning with inherited information across modalities.Extensive experiments and their results demonstrate the effectiveness of our approach as well as each proposed module in this work.Source code is available

Scene graph serves as a crucial visual representation of an image, with salient objects providing richer semantics for detecting key relations. However, most methods use a one-step reasoning manner for key relation detection, which may not utilize potential clues effectively. Humans usually review and revise to achieve the final answer, and semantics of relations offer further linguistic clues. Therefore, we propose the Difference-aware Iterative Reasoning Network (DIRNet) to predict key relations in a multi-step manner. Our model estimates visual saliency, encodes contexts globally with message passing, and then refines predictions iteratively by considering the difference in predicted relation semantics and contextual information across iterations. Extensive experiments show that our model outperforms state-of-the-art methods in key relation prediction on the VG-KR benchmark, and achieves competitive results in common relation prediction on VG, demonstrating its generalization and superiority.

In real-world scenarios, it is common that a video contains multiple actors and their activities. Selectively localizing one specific actor and its action spatially and temporally via a language query becomes a vital and challenging task. Existing fully supervised methods require extensive elaborately annotated data and are sensitive to the class labels, which cannot satisfy real-world applications’ needs. Thus, we introduce the task of weakly supervised actor-action video segmentation from a sentence query (AAVSS) in this work, where only the video-sentence pairs are provided. To the best of our knowledge, our work is the first to perform AAVSS under weakly supervised situations. However, this task is extremely challenging not only because the task aims to learn the complex interactions between two heterogeneous modalities but also because the task needs to learn fine-grained analysis of video content without pixel-level annotations. To overcome the challenges, we propose a two-stage network. The network first follows the sentence guidance to localize the candidate region and then performs segmentation to achieve selective segmentation. Specifically, a novel tracker-based clip-level multiple instance learning paradigm is proposed in this article to learn the matches between regions and sentences, which makes our two-stage network robust to the region proposal network. Furthermore, two intrinsic characteristics of the video, temporal consistency and motion information, are utilized in companion with the weak supervision to facilitate the region-query matching. Through extensive experiments, the proposed method achieves comparable performance to state-of-the-art fully supervised approaches on two large-scale benchmarks, including A2D Sentences and J-HMDB Sentences.

Referring video object segmentation aims to segment the object referred by a given language expression. Existing works typically require compressed video bitstream to be decoded to RGB frames before being segmented, which increases computation and storage requirements and ultimately slows the inference down. This may hamper its application in real-world computing resource limited scenarios, such as autonomous cars and drones. To alleviate this problem, in this paper, we explore the referring object segmenta- tion task on compressed videos, namely on the original video data flow. Besides the inherent difficulty of the video referring object segmentation task itself, obtaining discriminative representation from compressed video is also rather challenging. To address this problem, we propose a multi-attention network which consists of dual-path dual-attention module and a query-based cross-modal Transformer module. Specifically, the dual-path dual-attention module is designed to extract effective representation from compressed data in three modalities, i.e., I-frame, Motion Vector and Residual. The query-based cross-modal Transformer firstly models the corre- lation between linguistic and visual modalities, and then the fused multi-modality features are used to guide object queries to generate a content-aware dynamic kernel and to predict final segmentation masks. Different from previous works, we propose to learn just one kernel, which thus removes the complicated post mask-matching procedure of existing methods. Extensive promising experimental results on three challenging datasets show the effectiveness of our method compared against several state-of-the-art methods which are proposed for processing RGB data. Source code is available at: https://github.com/DexiangHong/MANet.

In this paper, we address the problem that selectively segments the actor and its action in the video clip given the sentence description. The main challenge is to match the local semantic features of the video with the heterogeneous textual features. A widely used language processing method in previous works is to leverage bi-LSTM and self-attention, which fixed the attention of the sentence and neglected the personality of the video, leading the attention of the sentence mismatch the most discriminative feature of the video. The proposed algorithm in this paper allows the sentence to learn the most discriminative features of the video, remarkably improving the accuracy of matching and segmentation. Specifically, we propose a cascade cross-modal attention to leverage two perspectives visual features to attend language from coarse to fine to generate the discriminative vision-aware language features. Moreover, equipping our framework with a contrastive learning method and a designed hard negative mining strategy benefits our proposed network from identifying the positive sample from numbers of negatives, and further improving the performance. To demonstrate the effectiveness of our approach, we conduct experiments on two datasets: A2D Sentences and J-HMDB Sentences. Experimental results show that our method significantly improves the performance over recent state-of-the-art methods.