Category-Independent Object Proposals with Diverse Ranking

By Ian Endres, Derek Hoiem. 2014

239 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

What Makes for Effective Detection Proposals? (2015, background): “• In §3 we introduce the notion of proposal repeatability, discuss its relevance when considering proposals for detection, and measure the repeatability of existing methods.”
COCO-Stuff: Thing and Stuff Classes in Context (2016, background): “Such classes have a specific size [21, 27] and shape [21, 51, 55, 39, 17, 14], and identifiable parts (e.”
Learning Open-World Object Proposals without Learning to Classify (2021, background): “In early works, the emphasis was on category-independent object proposals [21], [3], [22], [7], where the goal is to identify instances of all objects in the image irrespective of their category.”
Object-Proposal Evaluation Protocol is ‘Gameable’ (2015, background): “In early works [2, 4, 6], the emphasis was on category independent object proposals, where the goal is to identify instances of all objects in the image irrespective of their category.”
Scalable, High-Quality Object Detection (2014, background): “Given the recent success of novel sophisticated proposal generation methods [1, 22, 24, 14, 3, 10, 2, 11], the possibility of directly learned methods overtaking engineered meth-ods might seem far fetched at ﬁrst glance.”
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation (2015, methodology): “Top: original image, instance-level ground truth from COCO and our multiscale hierarchical segmentation.”
Self-taught object localization with deep networks (2014, methodology): “Objectness methods [2, 6, 30, 4, 3, 16, 36, 25, 21, 26, 32, 14] aim at generating bounding boxes that yield high recall for a high number of candidates, i.”
Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation (2021, methodology): “Since the definition of “object” is ambiguous, we follow the common approach in object proposal literature: [45, 10, 53, 39]: objects are defined as things not belonging to background or stuff.”
How good are detection proposals, really? (2014, background): “Endres2010 [9, 10]: The approach from [10] mixes a large set of cues.”
Learning to segment under various forms of weak supervision (2015, methodology): “The final set of effective methods are based on object proposal [4, 9, 1, 11, 16], where class-independent segments are generated, and subsequently classified into different categories using features computed on those segments.”
Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation (2015, methodology): “The representative works include CPMC [9] and Category Independent Object Proposal [10], which extend GrabCut to this scenario by using different seed sampling techniques (see Fig.”
Recent Advances in Deep Learning for Object Detection (2019, background): “Enreds and Hoiem [85] combined the idea of Selective Search [45] and CPMC [84].”
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation (2017, background): “[11], [15], [16], [6], [11], [17].”
Cracking BING and Beyond (2014, background): “In recent years, generic objectness proposal which aims to reduce the candidate windows in the preprocessing stage [1][2], has gained wide attention [7][8][10][16][17][12].”
Oriented Object Proposals (2015, background): “…detection performance , as OOPs reduce the ambiguity caused by orientation variation (see the realigned images with green borders). topic [1] [26] [10] [2] [25] [6] [32], several object detection algorithms use object proposals as input to significantly speed up the detection process, including…”