Improving Classification by Improving Labelling: Introducing Probabilistic Multi-Label Object Interaction Recognition
This work deviates from easy-to-define class boundaries for object interactions. For the task of object interaction recognition, often captured using an egocentric view, we show that semantic ambiguities in verbs and recognising sub-interactions along with concurrent interactions result in legitimate class overlaps (Figure 1). We thus aim to model the mapping between observations and interaction classes, as well as class overlaps, towards a probabilistic multi-label classifier that emulates human annotators. Given a video segment containing an object interaction, we model the probability for a verb, out of a list of possible verbs, to be used to annotate that interaction. The proba- bility is learnt from crowdsourced annotations, and is tested on two public datasets, comprising 1405 video sequences for which we provide annotations on 90 verbs. We outper- form conventional single-label classification by 11% and 6% on the two datasets respectively, and show that learning from annotation probabilities outperforms majority voting and enables discovery of co-occurring labels.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationObjectSimilar Papers 제목 키워드 기반
Probabilistic Decoupling of Labels in Classification
We investigate probabilistic decoupling of labels supplied for training, from the underlying classes for prediction. Decoupling enables an inference scheme general enough to implement many classification problems, includ…
ClassificationGeneral ClassificationFrom Articles to Canopies: Knowledge-Driven Pseudo-Labelling for Tree Species Classification using LLM Experts
Hyperspectral tree species classification is challenging due to limited and imbalanced class labels, spectral mixing (overlapping light signatures from multiple species), and ecological heterogeneity (variability among e…
Enhance Robustness of Sequence Labelling with Masked Adversarial Training
Adversarial training (AT) has shown strong regularization effects on deep learning algorithms by introducing small input perturbations to improve model robustness. In language tasks, adversarial training brings word-leve…
Chunkingnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+4Weakly supervised localisation of prostate cancer using reinforcement learning for bi-parametric MR images
In this paper we propose a reinforcement learning based weakly supervised system for localisation. We train a controller function to localise regions of interest within an image by introducing a novel reward definition t…
Multiple Instance LearningObjectACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and multi-label (e.g., multiple-disease diagnosi…
image-classificationImage ClassificationMedical Image AnalysisMedical Image Classification+3