Jinyoung Moon

Kanghoon Yoon, Kibum Kim, Jinyoung Moon et al.

Recent scene graph generation (SGG) frameworks have focused on learning complex relationships among multiple objects in an image. Thanks to the nature of the message passing neural network (MPNN) that models high-order interactions between objects and their neighboring objects, they are dominant representation learning modules for SGG. However, existing MPNN-based frameworks assume the scene graph as a homogeneous graph, which restricts the context-awareness of visual relations between objects. That is, they overlook the fact that the relations tend to be highly dependent on the objects with which the relations are associated. In this paper, we propose an unbiased heterogeneous scene graph generation (HetSGG) framework that captures relation-aware context using message passing neural networks. We devise a novel message passing layer, called relation-aware message passing neural network (RMP), that aggregates the contextual information of an image considering the predicate type between objects. Our extensive evaluations demonstrate that HetSGG outperforms state-of-the-art methods, especially outperforming on tail predicate classes.

Jungkyoo Shin, Jinyoung Moon

Temporal moment localization aims to retrieve the best video segment matching a moment specified by a query. The existing methods generate the visual and semantic embeddings independently and fuse them without full consideration of the long-term temporal relationship between them. To address these shortcomings, we introduce a novel recurrent unit, cross-modal long short-term memory (CM-LSTM), by mimicking the human cognitive process of localizing temporal moments that focuses on the part of a video segment related to the part of a query, and accumulates the contextual information across the entire video recurrently. In addition, we devise a two-stream attention mechanism for both attended and unattended video features by the input query to prevent necessary visual information from being neglected. To obtain more precise boundaries, we propose a two-stream attentive cross-modal interaction network (TACI) that generates two 2D proposal maps obtained globally from the integrated contextual features, which are generated by using CM-LSTM, and locally from boundary score sequences and then combines them into a final 2D map in an end-to-end manner. On the TML benchmark dataset, ActivityNet-Captions, the TACI outperform state-of-the-art TML methods with R@1 of 45.50% and 27.23% for IoU@0.5 and IoU@0.7, respectively. In addition, we show that the revised state-of-the-arts methods by replacing the original LSTM with our CM-LSTM achieve performance gains.

Youngwan Lee, Hyung-Il Kim, Kimin Yun et al.

Video classification researches that have recently attracted attention are the fields of temporal modeling and 3D efficient architecture. However, the temporal modeling methods are not efficient or the 3D efficient architecture is less interested in temporal modeling. For bridging the gap between them, we propose an efficient temporal modeling 3D architecture, called VoV3D, that consists of a temporal one-shot aggregation (T-OSA) module and depthwise factorized component, D(2+1)D. The T-OSA is devised to build a feature hierarchy by aggregating temporal features with different temporal receptive fields. Stacking this T-OSA enables the network itself to model short-range as well as long-range temporal relationships across frames without any external modules. Inspired by kernel factorization and channel factorization, we also design a depthwise spatiotemporal factorization module, named, D(2+1)D that decomposes a 3D depthwise convolution into two spatial and temporal depthwise convolutions for making our network more lightweight and efficient. By using the proposed temporal modeling method (T-OSA), and the efficient factorized component (D(2+1)D), we construct two types of VoV3D networks, VoV3D-M and VoV3D-L. Thanks to its efficiency and effectiveness of temporal modeling, VoV3D-L has 6x fewer model parameters and 16x less computation, surpassing a state-of-the-art temporal modeling method on both Something-Something and Kinetics-400. Furthermore, VoV3D shows better temporal modeling ability than a state-of-the-art efficient 3D architecture, X3D having comparable model capacity. We hope that VoV3D can serve as a baseline for efficient video classification.