10 accepted papers
Long-term time series forecasting (LTSF) plays a crucial role in fields such as energy management, finance, and traffic prediction. Transformer-based models have adopted patch-based strategies to capture long-range dependencies, but accurately modeling shape similarities across patches and variables…
Zero-shot Human-object interaction (HOI) detection aims to locate humans and objects in images and recognize their interactions. While advances in open-vocabulary object detection provide promising solutions for object localization, interaction recognition (IR) remains challenging due to the combina…
Generalizable object keypoint localization is a fundamental computer vision task in understanding the object structure. It is challenging for existing keypoint localization methods because their limited training data cannot provide generalizable shape and semantic cues, leading to inferior performan…
In multimodal representation learning, synergistic interactions between modalities not only provide complementary information but also create unique outcomes through specific interaction patterns that no single modality could achieve alone. Existing methods may struggle to effectively capture the fu…
The capacity of existing human keypoint localization models is limited by keypoint priors provided by the training data. To alleviate this restriction and pursue more general model this work studies keypoint localization from a different perspective by reasoning locations based on keypiont clues in…
Regression-based keypoint localization shows advantages of high efficiency and better robustness to quantization errors than heatmap-based methods. However existing regression-based methods discard the spatial location prior in input image with a global pooling leading to inferior accuracy and are l…
Model-based 3D human mesh recovery aims to reconstruct a 3D human body mesh by estimating its parameters from monocular RGB images. Most of recent works adopt the Skinned Multi-Person Linear (SMPL) model to regress relative rotations for each body joint along the kinematics chain. This pipeline need…
Crowded scenes make it challenging to differentiate persons and locate their pose keypoints. This paper proposes the Contextual Instance Decoupling (CID), which presents a new pipeline for multi-person pose estimation. Instead of relying on person bounding boxes to spatially differentiate persons, C…
Multi-person pose estimation in crowded scenes is challenging because overlapping and occlusions make it difficult to detect person bounding boxes and infer pose cues from individual keypoints. To address those issues, this paper proposes a direct pose-level inference strategy that is free of boundi…
The challenge of unsupervised person re-identification (ReID) lies in learning discriminative features without true labels. This paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels. Our method starts by assigning each person image with a s…