Browse > Computer Vision > Object Classification

Object Classification

32 papers with code · Computer Vision

State-of-the-art leaderboards

No evaluation results yet. Help compare methods by submit evaluation metrics.

Greatest papers with code

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

CVPR 2017 charlesq34/pointnet

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images.

OBJECT CLASSIFICATION SCENE SEGMENTATION SEMANTIC PARSING

SBNet: Sparse Blocks Network for Fast Inference

CVPR 2018 uber/sbnet

Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications. For many problems such as object detection and semantic segmentation, we are able to obtain a low-cost computation mask, either from a priori problem knowledge, or from a low-resolution segmentation network.

3D OBJECT DETECTION OBJECT CLASSIFICATION SEMANTIC SEGMENTATION

OctNet: Learning Deep 3D Representations at High Resolutions

CVPR 2017 griegler/octnet

We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution.

3D OBJECT CLASSIFICATION OBJECT CLASSIFICATION

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

12 Nov 2018qijiezhao/M2Det

Feature pyramids are widely exploited by both the state-of-the-art one-stage object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object detectors (e.g., Mask R-CNN, DetNet) to alleviate the problem arising from scale variation across object instances. Finally, we gather up the decoder layers with equivalent scales (sizes) to develop a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels.

OBJECT CLASSIFICATION OBJECT DETECTION

CNN Features off-the-shelf: an Astounding Baseline for Recognition

23 Mar 2014baldassarreFe/deep-koalarization

We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the \overfeat network which was trained to perform object classification on ILSVRC13. We use features extracted from the \overfeat network as a generic image representation to tackle the diverse range of recognition tasks of object image classification, scene recognition, fine grained recognition, attribute detection and image retrieval applied to a diverse set of datasets.

IMAGE CLASSIFICATION IMAGE RETRIEVAL OBJECT CLASSIFICATION SCENE RECOGNITION

Volumetric and Multi-View CNNs for Object Classification on 3D Data

CVPR 2016 charlesq34/3dcnn.torch

Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations. Overall, we are able to outperform current state-of-the-art methods for both volumetric CNNs and multi-view CNNs.

3D OBJECT RECOGNITION OBJECT CLASSIFICATION

Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

15 Aug 2016ajbrock/Generative-and-Discriminative-Voxel-Modeling

When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification.

OBJECT CLASSIFICATION

ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network

28 Nov 2018sacmehta/ESPNetv2

We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2, for modeling visual and sequential data. Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters.

IMAGE CLASSIFICATION LANGUAGE MODELLING OBJECT CLASSIFICATION SEMANTIC SEGMENTATION

Deep Unsupervised Similarity Learning using Partially Ordered Sets

CVPR 2017 asanakoy/deeppose_tf

Similarity learning is then formulated as a partial ordering task with soft correspondences of all samples to classes. Adopting a strategy of self-supervision, a CNN is trained to optimally represent samples in a mutually consistent manner while updating the classes.

OBJECT CLASSIFICATION POSE ESTIMATION

Learning a low-rank shared dictionary for object classification

31 Jan 2016tiepvupsu/DICTOL

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. Inspired by this observation, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification.

DICTIONARY LEARNING OBJECT CLASSIFICATION