Titlebook: Computer Vision – ACCV 2020; 15th Asian Conferenc Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi Conference proceedings 2021 Springer Nature Swi

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End-to-End Model-Based Gait Recognition. In this paper, we propose an end-to-end model-based gait recognition method. Specifically, we employ a skinned multi-person linear (SMPL) model for human modeling, and estimate its parameters using a pre-trained human mesh recovery (HMR) network. As the pre-trained HMR is not recognition-oriented,

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Backbone Based Feature Enhancement for Object Detectionection performance. However, almost all the architectures of feature pyramid are manually designed, which requires ad hoc design and prior knowledge. Meanwhile, existing methods focus on exploring more appropriate connections to generate features with strong semantics features from inherent pyramida

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Any-Shot Object Detection real world scenarios, it is less practical to expect that ‘.’ the novel classes are either unseen or have few-examples. Here, we propose a more realistic setting termed ‘.’, where totally unseen and few-shot categories can simultaneously co-occur during inference. Any-shot detection offers unique c

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Background Learnable Cascade for Zero-Shot Object Detectionemain several challenges for ZSD, including reducing the ambiguity between background and unseen objects as well as improving the alignment between visual and semantic concept. In this work, we propose a novel framework named Background Learnable Cascade (BLC) to improve ZSD performance. The major c

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Unsupervised Domain Adaptive Object Detection Using Forward-Backward Cyclic Adaptationadversarial training based domain adaptation methods have shown their effectiveness on minimizing domain discrepancy via marginal feature distributions alignment. However, aligning the marginal feature distributions does not guarantee the alignment of class conditional distributions. This limitation

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Synthesizing the Unseen for Zero-Shot Object Detectionresponding semantics during inference. However, since the unseen objects are never visualized during training, the detection model is skewed towards seen content, thereby labeling unseen as background or a seen class. In this work, we propose to . visual features for unseen classes, so that the mode