Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information
Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e.g., categories of the instances in the whole image) are used to train an object detector. Many existing methods follow the standard multiple instance learning (MIL) paradigm and have achieved promising performance. However, the lack of deterministic information leads to part domination and missing instances. To address these issues, this paper focuses on identifying and fully exploiting the deterministic information in WSOD. We discover that negative instances (i.e. absolutely wrong instances), ignored in most of the previous studies, normally contain valuable deterministic information. Based on this observation, we here propose a negative deterministic information (NDI) based method for improving WSOD, namely NDI-WSOD. Specifically, our method consists of two stages: NDI collecting and exploiting. In the collecting stage, we design several processes to identify and distill the NDI from negative instances online. In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively. Experimental results on several public benchmarks including VOC 2007, VOC 2012 and MS COCO show that our method achieves satisfactory performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningMultiple Instance Learningobject-detectionObject DetectionWeakly Supervised Object DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again
Knowledge Distillation (KD) aims at transferring the knowledge of a well-performed neural network (the {\it teacher}) to a weaker one (the {\it student}). A peculiar phenomenon is that a more accurate model doesn't neces…
Knowledge DistillationCollective Entity Disambiguation with Structured Gradient Tree Boosting
We present a gradient-tree-boosting-based structured learning model for jointly disambiguating named entities in a document. Gradient tree boosting is a widely used machine learning algorithm that underlies many top-perf…
Entity DisambiguationEnforcing Consistency in Weakly Supervised Semantic Parsing
The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons. Prior work uses elaborate search strategies to mitigate the prevalence …
Semantic ParsingVisual ReasoningLocal Boosting for Weakly-Supervised Learning
Boosting is a commonly used technique to enhance the performance of a set of base models by combining them into a strong ensemble model. Though widely adopted, boosting is typically used in supervised learning where the …
Weakly-supervised LearningGEFCOM 2014 - Probabilistic Electricity Price Forecasting
Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a well-known and established method, ARMA with exogenous variables with a relatively new t…
regressionTime SeriesTime Series AnalysisTime Series Forecasting