Fast, invariant representation for human action in the visual system
Humans can effortlessly recognize others' actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition, however, the underlying neural computations remain poorly understood. We use magnetoencephalography (MEG) decoding and a dataset of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discounts changes in 3D viewpoint relative to when it initially discriminates between actions. We measure the latency difference between invariant and non-invariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. Our results show no difference in decoding latency or temporal profile between invariant and non-invariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time, and that both form and motion information are crucial for fast, invariant action recognition.
Code (0)
등록된 구현이 없습니다.
Tasks
Action RecognitionFormSimilar Papers 제목 키워드 기반
Invariant recognition drives neural representations of action sequences
Recognizing the actions of others from visual stimuli is a crucial aspect of human visual perception that allows individuals to respond to social cues. Humans are able to identify similar behaviors and discriminate betwe…
Action RecognitionObject RecognitionNature Inspired Dimensional Reduction Technique for Fast and Invariant Visual Feature Extraction
Fast and invariant feature extraction is crucial in certain computer vision applications where the computation time is constrained in both training and testing phases of the classifier. In this paper, we propose a nature…
Dimensionality ReductionView-invariant action recognition
Human action recognition is an important problem in computer vision. It has a wide range of applications in surveillance, human-computer interaction, augmented reality, video indexing, and retrieval. The varying pattern …
Action RecognitionRetrievalTemporal Action LocalizationViA: View-invariant Skeleton Action Representation Learning via Motion Retargeting
Current self-supervised approaches for skeleton action representation learning often focus on constrained scenarios, where videos and skeleton data are recorded in laboratory settings. When dealing with estimated skeleto…
Action ClassificationAction Recognitionmotion retargetingRepresentation Learning+2A Cognitive Process-Inspired Architecture for Subject-Agnostic Brain Visual Decoding
Subject-agnostic brain decoding, which aims to reconstruct continuous visual experiences from fMRI without subject-specific training, holds great potential for clinical applications. However, this direction remains under…
Contrastive LearningBrain Decoding