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Found 15 papers, 8 papers with code
Understanding Deep Image Representations by Inverting Them
Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system.
Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images
Image representations, from SIFT and bag of visual words to Convolutional Neural Networks (CNNs) are a crucial component of almost all computer vision systems.
Cross Pixel Optical Flow Similarity for Self-Supervised Learning
We propose a novel method for learning convolutional neural image representations without manual supervision.
Self-Supervised Learning of Video-Induced Visual Invariances
We propose a general framework for self-supervised learning of transferable visual representations based on Video-Induced Visual Invariances (VIVI).
Ranked #15 on
Image Classification
on VTAB-1k
(using extra training data)
Object-Centric Learning with Slot Attention
Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features.
Representation learning from videos in-the-wild: An object-centric approach
We propose a method to learn image representations from uncurated videos.
Differentiable Patch Selection for Image Recognition
Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand.
Conditional Object-Centric Learning from Video
Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built.
Simple Open-Vocabulary Object Detection with Vision Transformers
Combining simple architectures with large-scale pre-training has led to massive improvements in image classification.
Ranked #1 on
One-Shot Object Detection
on MS COCO
Object Scene Representation Transformer
A compositional understanding of the world in terms of objects and their geometry in 3D space is considered a cornerstone of human cognition.
SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos
The visual world can be parsimoniously characterized in terms of distinct entities with sparse interactions.
Iterative Patch Selection for High-Resolution Image Recognition
We propose a simple method, Iterative Patch Selection (IPS), which decouples the memory usage from the input size and thus enables the processing of arbitrarily large images under tight hardware constraints.
RUST: Latent Neural Scene Representations from Unposed Imagery
Our main insight is that one can train a Pose Encoder that peeks at the target image and learns a latent pose embedding which is used by the decoder for view synthesis.
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning.
Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on
Zero-Shot Transfer Image Classification
on ObjectNet
