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1. VLTinT: Visual-Linguistic Transformer-in-Transformer for Coherent Video Paragraph Captioning

   https://doi.org/10.1609/aaai.v37i3.25412  

   Yamazaki, Kashu; Vo, Khoa; Truong, Quang Sang; Raj, Bhiksha; Le, Ngan (June 2023, Proceedings of the AAAI Conference on Artificial Intelligence)

   Video Paragraph Captioning aims to generate a multi-sentence description of an untrimmed video with multiple temporal event locations in a coherent storytelling. Following the human perception process, where the scene is effectively understood by decomposing it into visual (e.g. human, animal) and non-visual components (e.g. action, relations) under the mutual influence of vision and language, we first propose a visual-linguistic (VL) feature. In the proposed VL feature, the scene is modeled by three modalities including (i) a global visual environment; (ii) local visual main agents; (iii) linguistic scene elements. We then introduce an autoregressive Transformer-in-Transformer (TinT) to simultaneously capture the semantic coherence of intra- and inter-event contents within a video. Finally, we present a new VL contrastive loss function to guarantee the learnt embedding features are consistent with the captions semantics. Comprehensive experiments and extensive ablation studies on the ActivityNet Captions and YouCookII datasets show that the proposed Visual-Linguistic Transformer-in-Transform (VLTinT) outperforms previous state-of-the-art methods in terms of accuracy and diversity. The source code is made publicly available at: https://github.com/UARK-AICV/VLTinT. 

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2. Open Vocabulary Semantic Scene Sketch Understanding

   Bourouis, Ahmed; Fan, Judith E; Gryaditskaya, Yulia (June 2024, Computer Vision Foundation)

   We study the underexplored but fundamental vision problem of machine understanding of abstract freehand scene sketches We introduce a sketch encoder that results in semantically- aware feature space, which we evaluate by testing its performance on a semantic sketch seg- mentation task. To train our model we rely only on the availability of bitmap sketches with their brief captions and do not require any pixel-level annotations. To obtain generalization to a large set of sketches and categories, we build on a vision transformer encoder pretrained with the CLIP model. We freeze the text encoder and perform visual-prompt tuning of the visual encoder branch while introducing a set of critical modifications. Firstly, we augment the classical key-query (k-q) self-attention blocks with value-value (v-v) self-attention blocks. Central to our model is a two-level hierarchical network design that enables efficient semantic disentanglement: The first level ensures holistic scene sketch encoding, and the second level focuses on individual categories. We, then, in the second level of the hierarchy, introduce a cross-attention between textual and visual branches. Our method outperforms zero-shot CLIP pixel accuracy of segmentation results by 37 points, reaching an accuracy of 85.5% on the FS-COCO sketch dataset. Finally, we conduct a user study that allows us to identify further improvements needed over our method to reconcile machine and human understanding of scene sketches. 

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3. TrEP: Transformer-Based Evidential Prediction for Pedestrian Intention with Uncertainty

   https://doi.org/10.1609/aaai.v37i3.25463  

   Zhang, Zhengming; Tian, Renran; Ding, Zhengming (June 2023, Proceedings of the AAAI Conference on Artificial Intelligence)

   With rapid development in hardware (sensors and processors) and AI algorithms, automated driving techniques have entered the public’s daily life and achieved great success in supporting human driving performance. However, due to the high contextual variations and temporal dynamics in pedestrian behaviors, the interaction between autonomous-driving cars and pedestrians remains challenging, impeding the development of fully autonomous driving systems. This paper focuses on predicting pedestrian intention with a novel transformer-based evidential prediction (TrEP) algorithm. We develop a transformer module towards the temporal correlations among the input features within pedestrian video sequences and a deep evidential learning model to capture the AI uncertainty under scene complexities. Experimental results on three popular pedestrian intent benchmarks have verified the effectiveness of our proposed model over the state-of-the-art. The algorithm performance can be further boosted by controlling the uncertainty level. We systematically compare human disagreements with AI uncertainty to further evaluate AI performance in confusing scenes. The code is released at https://github.com/zzmonlyyou/TrEP.git. 

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4. Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning

   https://doi.org/10.18653/v1/2020.emnlp-main.61  

   Fang, Zhiyuan; Gokhale, Tejas; Banerjee, Pratyay; Baral, Chitta; Yang, Yezhou (January 2020, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))

   null (Ed.)

   Captioning is a crucial and challenging task for video understanding. In videos that involve active agents such as humans, the agent{'}s actions can bring about myriad changes in the scene. Observable changes such as movements, manipulations, and transformations of the objects in the scene, are reflected in conventional video captioning. Unlike images, actions in videos are also inherently linked to social aspects such as intentions (why the action is taking place), effects (what changes due to the action), and attributes that describe the agent. Thus for video understanding, such as when captioning videos or when answering questions about videos, one must have an understanding of these commonsense aspects. We present the first work on generating \textit{commonsense} captions directly from videos, to describe latent aspects such as intentions, effects, and attributes. We present a new dataset {``}Video-to-Commonsense (V2C){''} that contains {\textasciitilde}9k videos of human agents performing various actions, annotated with 3 types of commonsense descriptions. Additionally we explore the use of open-ended video-based commonsense question answering (V2C-QA) as a way to enrich our captions. Both the generation task and the QA task can be used to enrich video captions. 

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5. A Semantic and Motion-Aware Spatiotemporal Transformer Network for Action Detection

   https://doi.org/10.1109/TPAMI.2024.3377192  

   Korban, Matthew; Youngs, Peter; Acton, Scott T (January 2024, IEEE Transactions on Pattern Analysis and Machine Intelligence)

   Lee, Kyoung Mu (Ed.)

   This paper presents a novel spatiotemporal transformer network that introduces several original components to detect actions in untrimmed videos. First, the multi-feature selective semantic attention model calculates the correlations between spatial and motion features to model the spatiotemporal interactions between different action semantics properly. Second, the motion-aware network encodes the locations of action semantics in video frames utilizing the motion-aware 2D positional encoding algorithm. Such a motion-aware mechanism memorizes the dynamic spatiotemporal variations in action frames that current methods cannot exploit. Third, the sequence-based temporal attention model captures the heterogeneous temporal dependencies in action frames. In contrast to standard temporal attention used in natural language processing, primarily aimed at finding similarities between linguistic words, the proposed sequence-based temporal attention is designed to determine both the differences and similarities between video frames that jointly define the meaning of actions. The proposed approach outperforms the state-of-the-art solutions on four spatiotemporal action datasets: AVA 2.2, AVA 2.1, UCF101-24, and EPIC-Kitchens. 

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