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Datasets

Greatest papers with code

Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

30 Mar 2016facebookresearch/vissl

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 TRANSFER LEARNING

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

CVPR 2019 uzh-rpg/event-based_vision_resources

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

ECCV 2020 HobbitLong/PyContrast

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.

OBJECT CLASSIFICATION SELF-SUPERVISED ACTION RECOGNITION SELF-SUPERVISED IMAGE CLASSIFICATION

DeepGCNs: Making GCNs Go as Deep as CNNs

15 Oct 2019lightaime/deep_gcns_torch

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 SEMANTIC SEGMENTATION

And the Bit Goes Down: Revisiting the Quantization of Neural Networks

ICLR 2020 facebookresearch/kill-the-bits

In this paper, we address the problem of reducing the memory footprint of convolutional network architectures.

OBJECT CLASSIFICATION QUANTIZATION

Learning RoI Transformer for Oriented Object Detection in Aerial Images

CVPR 2019 dingjiansw101/AerialDetection

Object detection in aerial images is an active yet challenging task in computer vision because of the bird's-eye view perspective, the highly complex backgrounds, and the variant appearances of objects.

OBJECT CLASSIFICATION OBJECT DETECTION IN AERIAL IMAGES

SoundNet: Learning Sound Representations from Unlabeled Video

NeurIPS 2016 cvondrick/soundnet

We learn rich natural sound representations by capitalizing on large amounts of unlabeled sound data collected in the wild.

OBJECT CLASSIFICATION