Object Classification

137 papers with code • 1 benchmarks • 9 datasets

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Greatest papers with code

TOOD: Task-aligned One-stage Object Detection

PaddlePaddle/PaddleDetection ICCV 2021

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two tasks.

2D Object Detection Object Classification

Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

facebookresearch/vissl 30 Mar 2016

By following the principles of self-supervision, we build a convolutional neural network (CNN) that can be trained to solve Jigsaw puzzles as a pretext task, which requires no manual labeling, and then later repurposed to solve object classification and detection.

Object Classification Representation Learning +1

Events-to-Video: Bringing Modern Computer Vision to Event Cameras

uzh-rpg/event-based_vision_resources CVPR 2019

Since the output of event cameras is fundamentally different from conventional cameras, it is commonly accepted that they require the development of specialized algorithms to accommodate the particular nature of events.

Object Classification

Contrastive Multiview Coding

HobbitLong/PyContrast ECCV 2020

We analyze key properties of the approach that make it work, finding that the contrastive loss outperforms a popular alternative based on cross-view prediction, and that the more views we learn from, the better the resulting representation captures underlying scene semantics.

Contrastive Learning Object Classification +2

Decoupled Adaptation for Cross-Domain Object Detection

thuml/Transfer-Learning-Library 6 Oct 2021

Besides, previous methods focused on category adaptation but ignored another important part for object detection, i. e., the adaptation on bounding box regression.

Domain Adaptation Object Classification +1

Adversarial Discriminative Domain Adaptation

thuml/Transfer-Learning-Library CVPR 2017

Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains.

General Classification Object Classification +2

DeepGCNs: Making GCNs Go as Deep as CNNs

lightaime/deep_gcns_torch 15 Oct 2019

This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.

Node Classification Object Classification +1