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- Jitendra MalikSuggested from co-authorship
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Works333 from public data
- Microsoft COCO: Common Objects in Context56,192
A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
- Feature Pyramid Networks for Object Detection28,993
This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.
- Shape matching and object recognition using shape contexts6,951
A novel approach to measuring similarity between shapes and exploiting it for object recognition by treating recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image.
- Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization5,549
This paper presents a simple yet effective approach that for the first time enables arbitrary style transfer in real-time, comparable to the fastest existing approach, without the restriction to a pre-defined set of styles.
- Class-Balanced Loss Based on Effective Number of Samples3,490
This work designs a re-weighting scheme that uses the effective number of samples for each class to re-balance the loss, thereby yielding a class-balanced loss and introduces a novel theoretical framework to measure data overlap by associating with each sample a small neighboring region rather than a single point.
- DOTA: A Large-Scale Dataset for Object Detection in Aerial Images3,264
A large-scale Dataset for Object deTection in Aerial images (DOTA) is introduced and state-of-the-art object detection algorithms on DOTA are evaluated, demonstrating that DOTA well represents real Earth Vision applications and are quite challenging.
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- Multimodal Unsupervised Image-to-Image Translation2,788
A Multimodal Unsupervised Image-to-image Translation (MUNIT) framework that assumes that the image representation can be decomposed into a content code that is domain-invariant, and a style code that captures domain-specific properties.
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- Blobworld: image segmentation using expectation-maximization and its application to image querying1,655
Results indicating that querying for images using Blobworld produces higher precision than does querying using color and texture histograms of the entire image in cases where the image contains distinctive objects are presented.
- 1,635
- 1,529
- Caltech-UCSD Birds 2001,422
Caltech-UCSD Birds 200 (CUB-200) is a challenging image dataset annotated with 200 bird species to enable the study of subordinate categorization, which is not possible with other popular datasets that focus on basic level categories.
- Contour and Texture Analysis for Image Segmentation1,377
This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture, and introduces a gating operator based on the texturedness of the neighborhood at a pixel to facilitate cue combination.
- Integral Channel Features1,350
It is demonstrated that when designed properly, integral channel features not only outperform other features including histogram of oriented gradient (HOG), they also result in fast detectors when coupled with cascade classifiers.
- 1,317
- Residual networks behave like ensembles of relatively shallow networks1,174
This work proposes a novel interpretation of residual networks showing that they can be seen as a collection of many paths of differing length, and reveals one of the key characteristics that seem to enable the training of very deep networks: Residual networks avoid the vanishing gradient problem by introducing short paths which can carry gradient throughout the extent of veryDeep networks.
- Blobworld: A System for Region-Based Image Indexing and Retrieval955
This work indexes the blob descriptions using a lower-rank approximation to the high-dimensional distance to make large-scale retrieval feasible, and shows encouraging results for both querying and indexing.
- The multidimensional wisdom of crowds920
A method for estimating the underlying value of each image from (noisy) annotations provided by multiple annotators, based on a model of the image formation and annotation process, which predicts ground truth labels on both synthetic and real data more accurately than state of the art methods.
- PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows864
A principled probabilistic framework to generate 3D point clouds by modeling them as a distribution of distributions with the invertibility of normalizing flows enables the computation of the likelihood during training and allows the model to train in the variational inference framework.
- Detecting Oriented Text in Natural Images by Linking Segments775
SegLink, an oriented text detection method to decompose text into two locally detectable elements, namely segments and links, achieves an f-measure of 75.0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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