Human action can be recognized in still images since the whole image represents an action with some spatial clues, such as human poses, action-specific parts, and global surroundings. To represent the spatial clues, the recent methods require labor-intensive annotations to locate the human body and objects, which are computationally intensive. To eliminate strong supervision, a Multiple Spatial Clues Network (MSCNet) is proposed to represent the spatial clues with only image-level action label. Neither accurately manual annotated bounding boxes nor extra labeled datasets are required as additional supervision. First, the proposed MSCNet exploits spatial-attention module to generate spatial attention regions, and detects the spatial clues with minimal supervision. Then, spatial clues exploitation is proposed to utilize the learned spatial clues with three modules: the context module, body + context module and body + semantics module. Experiments on three benchmark datasets demonstrate the effectiveness of the proposed MSCNet. © 2022 Elsevier B.V.