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Found 12 papers, 6 papers with code
Directed Diffusion: Direct Control of Object Placement through Attention Guidance
Text-guided diffusion models such as DALLE-2, Imagen, eDiff-I, and Stable Diffusion are able to generate an effectively endless variety of images given only a short text prompt describing the desired image content.
NewsStories: Illustrating articles with visual summaries
Thus, we explore a novel setting where the goal is to learn a self-supervised visual-language representation that is robust to varying text length and the number of images.
Recognizing Multimodal Entailment
How information is created, shared and consumed has changed rapidly in recent decades, in part thanks to new social platforms and technologies on the web.
Geo-Aware Networks for Fine-Grained Recognition
By leveraging geolocation information we improve top-1 accuracy in iNaturalist from 70. 1% to 79. 0% for a strong baseline image-only model.
Towards a Semantic Perceptual Image Metric
We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification.
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Recent deep networks are capable of memorizing the entire data even when the labels are completely random.
Ranked #16 on
Image Classification
on WebVision-1000
Improving the Robustness of Deep Neural Networks via Stability Training
In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network.
Pose Embeddings: A Deep Architecture for Learning to Match Human Poses
We present a method for learning an embedding that places images of humans in similar poses nearby.
MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching
We perform a comprehensive set of experiments on standard datasets to carefully study the contributions of each aspect of MatchNet, with direct comparisons to established methods.
Large-Scale Video Classification with Convolutional Neural Networks
We further study the generalization performance of our best model by retraining the top layers on the UCF-101 Action Recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63. 3% up from 43. 9%).
Ranked #9 on
Action Recognition
on Sports-1M
Learning Fine-grained Image Similarity with Deep Ranking
This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images. It has higher learning capability than models based on hand-crafted features.
Deep Convolutional Ranking for Multilabel Image Annotation
Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications.
