Search Results for author:
Found 10 papers, 4 papers with code
Generate, Segment and Refine: Towards Generic Manipulation Segmentation
The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet.
Efficient Object Embedding for Spliced Image Retrieval
Detecting spliced images is one of the emerging challenges in computer vision.
Toward Realistic Image Compositing With Adversarial Learning
Compositing a realistic image is a challenging task and usually requires considerable human supervision using professional image editing software.
Unsupervised Deep Metric Learning via Auxiliary Rotation Loss
In this work, we propose to generate pseudo-labels for deep metric learning directly from clustering assignment and we introduce unsupervised deep metric learning (UDML) regularized by a self-supervision (SS) task.
Testing-Time Adaptation through Online Normalization Estimation
We present a simple and effective way to estimate the batch-norm statistics during test time, to fast adapt a source model to target test samples.
AdaViT: Adaptive Vision Transformers for Efficient Image Recognition
To this end, we introduce AdaViT, an adaptive computation framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use throughout the backbone on a per-input basis, aiming to improve inference efficiency of vision transformers with a minimal drop of accuracy for image recognition.
Rethinking Nearest Neighbors for Visual Classification
In this paper, we investigate $k$-Nearest-Neighbor (k-NN) classifiers, a classical model-free learning method from the pre-deep learning era, as an augmentation to modern neural network based approaches.
Visual Prompt Tuning
The current modus operandi in adapting pre-trained models involves updating all the backbone parameters, ie, full fine-tuning.
Ranked #2 on
Prompt Engineering
on ImageNet-21k
Raising the Bar on the Evaluation of Out-of-Distribution Detection
In this paper, we define 2 categories of OoD data using the subtly different concepts of perceptual/visual and semantic similarity to in-distribution (iD) data.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+2
Object Recognition as Next Token Prediction
We present an approach to pose object recognition as next token prediction.
