Dialogue emotion detection is always challenging due to human subjectivity and the randomness of dialogue content. In a conversation, the emotion of each person often develops via a cumulative process, which can be influenced by many elements of uncertainty. Much commonsense knowledge influences people's emotions imperceptibly, such as experiential or habitual knowledge. In the process of conversation, this commonsense knowledge information can be used to enrich the semantic information of each utterance and improve the accuracy of emotion recognition. In this paper, we propose a growing graph model for dialogues emotion detection based on retrieval of external knowledge atlas ATOMIC from local and global respectively, which can effectively represent the dialogues as a process variable in a sequence and the correlation among utterances also can be represented by the graph model. In particular, 1) we introduce a common sense knowledge graph for linking the commonsense knowledge retrieved from external knowledge atlas ATOMIC, which can effectively add auxiliary information to improve the performance of each utterance's representation. 2) We propose a novel self-supervised learning method for extracting the latent topic of each dialogue. Based on this design, we also propose an effective optimization mechanism to make the representation (embedding) of latent topic has a better distinction for the next operation. 3) Finally, the cross-attention module is utilized to combine the utterances' features and the latent conversation topic information. The attention mechanism can effectively use topic information to supplement the representation of utterances and improve recognition performance. The model is tested on three popular datasets in dialogue emotion detection and is empirically demonstrated to outperform the state-of-the-art approaches. Meanwhile, to demonstrate the performance of our approach, we also build a long dialogue dataset. The average length of each conversation is over 50 utterances. The final experimental results also demonstrate the superior performance of our approach.

As an important field of multimedia, 3D shape recognition has attracted much research attention in recent years. Various approaches have been proposed, within which the multiview-based methods show their promising performances. In general, an effective 3D shape recognition algorithm should take both the multiview local and global visual information into consideration, and explore the inherent properties of generated 3D descriptors to guarantee the performance of feature alignment in the common space. To tackle these issues, we propose a novel Hierarchical Multi-scale Transformer Network (HMTN) for the 3D shape recognition task. In HMTN, we propose a multi-level regional transformer (MLRT) module for shape descriptor generation. MLRT includes two branches that aim to extract the intra-view local characteristics by modeling region-wise dependencies and give the supervision of multiview global information under different granularities. Specifically, MLRT can comprehensively consider the relations of different regions and focus on the discriminative parts, which improves the effectiveness of the learned descriptors. Finally, we adopt the cross-granularity contrastive learning (CCL) mechanism for shape descriptor alignment in the common space. It can explore and utilize the cross-granularity semantic correlation to guide the descriptor extraction process while performing the instance alignment based on the category information. We evaluate the proposed network on several public benchmarks, and HMTN achieves competitive performance compared with the state-of-the-art (SOTA) methods.

Retrieving 3D shapes based on 2D images is a challenging research topic, due to the significant gap between different domains. Recently, various approaches have been proposed to handle this problem. However, the majority of methods target the cross-domain retrieval task as a pure domain adaptation problem, which focuses on the alignment but ignores the visual relevance between the 2D images and their corresponding 3D shapes. To fundamentally decrease the divergence between different domains, we propose a novel cross-domain learning network (CLN) for 2D image-based 3D shape retrieval task. First, we estimate the pose information from the 2D image to guide the view rendering of 3D shapes, which increases the visual correlations of the cross-domain data to eliminate the divergence between them. Second, we introduce a novel joint learning network, considering both the domain-specific characteristics and the cross-domain interactions for data alignment, which further compensates for the gap between different domains by controlling the distance of intra- and inter-classes. After the metric learning process, discriminative descriptors of images and shapes are generated for the cross-domain retrieval task. To prove the effectiveness and robustness of the proposed method, we conduct extensive experiments on the MI3DOR, SHREC’13, and SHREC’14 datasets. The experimental results demonstrate the superiority of our proposed method, and significant improvements have been achieved compared with state-of-the-art methods.

As an important field of multimedia, 3D shape recognition has attracted much research attention in recent years. A lot of deep learning models have been proposed for effective 3D shape representation. The view-based methods show the superiority due to the comprehensive exploration of the visual characteristics with the help of established 2D CNN architectures. Generally, the current approaches contain the following disadvantages: First, the most majority of methods lack the consideration for sequential information among the multiple views, which can provide descriptive characteristics for shape representation. Second, the incomprehensive exploration for the multi-view correlations directly affects the discrimination of shape descriptors. Finally, roughly aggregating multi-view features leads to the loss of descriptive information, which limits the shape representation effectiveness. To handle these issues, we propose a novel sequential view based hierarchical attention network (SVHAN) for 3D shape recognition. Specifically, we first divide the view sequence into several view blocks. Then, we introduce a novel hierarchical feature aggregation module (HFAM), which hierarchically exploits the view-level, block-level, and shape-level features, the intra- and inter- view-block correlations are also captured to improve the discrimination of learned features. Subsequently, a novel selective fusion module (SFM) is designed for feature aggregation, considering the correlations between different levels and preserving effective information. Finally, discriminative and informative shape descriptors are generated for the recognition task. We validate the effectiveness of our proposed method on two public databases. The experimental results show the superiority of SVHAN against the current state-of-the-art approaches.

Due to the wide applications in a rapidly increasing number of different fields, 3D shape recognition has become a hot topic in the computer vision field. Many approaches have been proposed in recent years. However, there remain huge challenges in two aspects: exploring the effective representation of 3D shapes and reducing the redundant complexity of 3D shapes. In this paper, we propose a novel deep-attention network (DAN) for 3D shape representation based on multiview information. More specifically, we introduce the attention mechanism to construct a deep multiattention network that has advantages in two aspects: 1) information selection, in which DAN utilizes the self-attention mechanism to update the feature vector of each view, effectively reducing the redundant information, and 2) information fusion, in which DAN applies attention mechanism that can save more effective information by considering the correlations among views. Meanwhile, deep network structure can fully consider the correlations to continuously fuse effective information. To validate the effectiveness of our proposed method, we conduct experiments on the public 3D shape datasets: ModelNet40, ModelNet10, and ShapeNetCore55. Experimental results and comparison with state-of-the-art methods demonstrate the superiority of our proposed method. Code is released on https://github.com/RiDang/DANN.

3D shape recognition is a challenging task due to the difficulty of representing the complex structure of 3D shapes. Recently, the view-based approaches that utilize the multiple views rendered from the shape for visual information extraction and feature aggregation to generate a global shape descriptor , achieved promising performance. However, the view-based approaches commonly ignore the exploration and utilization of local information in the multiple views, which influences the effectiveness of generated features. In this paper, we design a novel Multi-view based Local Information Fusion Network (MV-LFN) for the 3D shape recognition task. The local correlation attention mechanism (LCAM) is introduced to exploit the local correlations in the feature maps for generating a more effective view descriptor. Then, we hierarchically aggregate the multi-view feature maps to generate a shape super matrix (SSM). The local information is effectively extracted and maintained during the multi-view aggregation process, and the discrimination of shape descriptors is significantly improved. We conduct comparative experiments on the ModelNet and ShapeNetCore55 databases. The experimental performances effectively validate the superiority of MV-LFN.

3D shape retrieval has attracted much research attention due to its wide applications in the fields of computer vision and multimedia. Various approaches have been proposed in recent years for learning 3D shape descriptor from different modalities. The existing works contain the following disadvantages: 1) the vast majority methods rely on the large scale of training data with clear category information; 2) many approaches focus on the fusion of multi-modal information but ignore the guidance of correlations among different modalities for shape representation learning; 3) many methods pay attention to the structural feature learning of 3D shape but ignore the guidance of structural similarity between every two shapes. To solve these problems, we propose a novel multi-graph network (MGN) for unsupervised 3D shape retrieval, which utilizes the correlations among modalities and structural similarity between two models to guide the shape representation learning process without category information. More specifically, we propose two novel loss functions: auto-correlation loss and cross-correlation loss. The auto-correlation loss utilizes information from different modalities to increase the discrimination of shape descriptor. The cross-correlation loss utilizes the structural similarity between two models to strengthen the intra-class similarity and increase the inter-class distinction. Finally, an effective similarity measurement is designed for the shape retrieval task. To validate the effectiveness of our proposed method, we conduct experiments on the ModelNet dataset. Experimental results demonstrate the effectiveness of our proposed method, and significant improvements have been achieved compared with state-of-the-art methods.