• Publications
  • Influence
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TLDR
This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Expand
Normalized cuts and image segmentation
  • Jianbo Shi, J. Malik
  • Mathematics, Computer Science
  • Proceedings of IEEE Computer Society Conference…
  • 17 June 1997
TLDR
This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups. Expand
Multiscale Combinatorial Grouping
TLDR
This work first develops a fast normalized cuts algorithm, then proposes a high-performance hierarchical segmenter that makes effective use of multiscale information, and proposes a grouping strategy that combines the authors' multiscales regions into highly-accurate object candidates by exploring efficiently their combinatorial space. Expand
Normalized Cuts and Image Segmentation
  • Jianbo Shi, J. Malik
  • Mathematics, Computer Science
  • IEEE Trans. Pattern Anal. Mach. Intell.
  • 1 August 2000
TLDR
This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups. Expand
AVA: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions
  • C. Gu, Chen Sun, +8 authors J. Malik
  • Computer Science
  • IEEE/CVF Conference on Computer Vision and…
  • 23 May 2017
TLDR
The AVA dataset densely annotates 80 atomic visual actions in 437 15-minute video clips, where actions are localized in space and time, resulting in 1.59M action labels with multiple labels per person occurring frequently. Expand
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation
We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we firstExpand
Cognitive Mapping and Planning for Visual Navigation
TLDR
The Cognitive Mapper and Planner is based on a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the task, and a spatial memory with the ability to plan given an incomplete set of observations about the world. Expand
Modeling and Rendering Architecture from Photographs
TLDR
This thesis presents an approach for modeling and rendering existing architectural scenes from sparse sets of still photographs and presents view-dependent texture mapping, a method of compositing multiple views of a scene that better simulates geometric detail on basic models. Expand
Matching with shape contexts
TLDR
This work introduces a new shape descriptor, the shape context, for measuring shape similarity and recovering point correspondences, and uses it as a vector-valued attribute in a bipartite graph matching framework. Expand
Multi-view Supervision for Single-View Reconstruction via Differentiable Ray Consistency
TLDR
A differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view is proposed by reformulating view consistency using a differentiable ray consistency (DRC) term and it is shown that this formulation can be incorporated in a learning framework to leverage different types of multi-view observations. Expand
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