Co-localization in Real-World Images

By Kevin D. Tang, Armand Joulin, Li-Jia Li, Li Fei-Fei. 2014

199 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

What Makes for Effective Detection Proposals? (2015, background): “For example, they can be used to handle unknown categories at test time, or to enable weakly supervised learning [55]–[57].”
Joint Discovery of Object States and Manipulation Actions (2017, background): “Such methods can incorporate (weak) supervision that helps to steer the clustering towards a preferred solution [5, 13, 21, 24, 42].”
Unsupervised Learning by Predicting Noise (2017, methodology): “It has been successfully applied to several computer vision applications, like object discovery (Joulin et al., 2010; Tang et al., 2014) or video/text alignment (Bojanowski et al., 2013; 2014; Ra-manathan et al., 2014).”
Grounding of Textual Phrases in Images by Reconstruction (2015, background): “[29] solves co-localization problem with a model that jointly incorporates similarity, prior and discriminability, and address noisy images.”
Unsupervised Object Discovery and Tracking in Video Collections (2015, methodology): “To reduce human effort and subjective biases in manual annotation, recent work has addressed the discovery and localization of objects from weakly-annotated or even unlabelled datasets [4, 5, 9, 30, 32].”
A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection (2015, methodology): “Thus it can not only be used in the multi-camera system [4] directly, but also provide useful common foreground prior for some real-world applications, such as video foreground co-segmentation [5] and image co-localization [6, 7].”
Sampling Techniques for Large-Scale Object Detection From Sparsely Annotated Objects (2018, methodology): “Prediction results of a network are used to train an object detector with limited annotation in many previous works [20, 22, 2, 3, 23].”
Visual Motif Discovery via First-Person Vision (2016, methodology): “by p (i) t instead of the objectness used in [18].”
Unsupervised Image Matching and Object Discovery as Optimization (2019, background): “Several alternatives are possible, including weakly supervised approaches that rely on readily available meta-data [2, 9] or image-level labels [14, 23, 24, 25, 39, 45] instead of more complex annotations such as bounding boxes [17, 38] or object masks [20] as supervisory signal; semi supervised met”
The Frank-Wolfe Algorithm: A Short Introduction (2023, unknown): “…them very effective in the context of large-scale machine learning problems (see, e.g., Lacoste-Julien et al. [54], Jaggi [48], Négiar et al. [64], Dahik [26], Jing et al. [49]), image processing (see, e.g., Joulin et al. [50], Tang et al. [75]), quantum physics (see, e.g., Gilbert [41],…”
Weakly Supervised Object Localization Using Things and Stuff Transfer (2017, unknown): “As in [10, 12, 4, 19, 17], we also linearly combine the SVM score with a general measure of objectness [5, 7].”
Y Qualification Main task Y Return N N Y User qualified ? N Feedback Pass ? Pass ? Annotator training Annotating images (2017, methodology): “Objectness was used in WSOL before, to steer the localization process towards objects and away from background [11, 13, 21, 48, 58, 61, 59, 67, 75].”
Instance-Level Video Segmentation from Object Tracks (2016, methodology): “Discriminative clustering has recently been applied to several problems including image co-segmentation [24, 25], object co-localization in images [42], finding actor identities in movies [6, 35] and temporal action localization [7].”
Weakly Supervised Object Localization and Detection: A Survey (2021, unknown): “In [141], Tang et al. treat the weakly supervised object localization problem as an object co-localization task, and present a joint image-box formulation to mine reliable object locations via a Boolean constrained quadratic program.”
Unsupervised Discovery of Object Landmarks as Structural Representations (2018, background): “[57] Kevin Tang, Armand Joulin, Li-Jia Li, and Li Fei-Fei.”
We Don’t Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification (2016, background): “Second, like in [9, 12, 21, 38, 43, 47, 44, 51, 61], we combine the object detector score with a general measure of “objectness” [1], which measures how likely it is that a proposal tightly encloses an object of any class (e.”