• Publications
  • Influence
Measuring the Objectness of Image Windows
TLDR
A generic objectness measure, quantifying how likely it is for an image window to contain an object of any class, and uses objectness as a complementary score in addition to the class-specific model, which leads to fewer false positives. Expand
Fast Object Segmentation in Unconstrained Video
TLDR
This method is fast, fully automatic, and makes minimal assumptions about the video, which enables handling essentially unconstrained settings, including rapidly moving background, arbitrary object motion and appearance, and non-rigid deformations and articulations. Expand
COCO-Stuff: Thing and Stuff Classes in Context
TLDR
An efficient stuff annotation protocol based on superpixels is introduced, which leverages the original thing annotations, and the speed versus quality trade-off of the protocol is quantified and the relation between annotation time and boundary complexity is explored. Expand
What is an object?
TLDR
A generic objectness measure, quantifying how likely it is for an image window to contain an object of any class, is presented, combining in a Bayesian framework several image cues measuring characteristics of objects, such as appearing different from their surroundings and having a closed boundary. Expand
Learning object class detectors from weakly annotated video
TLDR
It is shown that training from a combination of weakly annotated videos and fully annotated still images using domain adaptation improves the performance of a detector trained from still images alone. Expand
Progressive search space reduction for human pose estimation
TLDR
An approach that progressively reduces the search space for body parts, to greatly improve the chances that pose estimation will succeed, and an integrated spatio- temporal model covering multiple frames to refine pose estimates from individual frames, with inference using belief propagation. Expand
Groups of Adjacent Contour Segments for Object Detection
TLDR
It is shown that kAS substantially outperform IPs for detecting shape-based classes, and the object detector is compared to the recent state-of-the-art system by Dalal and Triggs (2005). Expand
What's the Point: Semantic Segmentation with Point Supervision
TLDR
This work takes a natural step from image-level annotation towards stronger supervision: it asks annotators to point to an object if one exists, and incorporates this point supervision along with a novel objectness potential in the training loss function of a CNN model. Expand
From Images to Shape Models for Object Detection
TLDR
An object class detection approach which fully integrates the complementary strengths offered by shape matchers and can localize object boundaries accurately and does not need segmented examples for training (only bounding-boxes). Expand
Weakly Supervised Localization and Learning with Generic Knowledge
TLDR
A conditional random field that starts from generic knowledge and then progressively adapts to the new class is proposed that allows training any state-of-the-art object detector in a weakly supervised fashion, although it would normally require object location annotations. Expand
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