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Found 10 papers, 5 papers with code
SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization
Tehchniques for manipulating images are advancing rapidly; while these are helpful for many useful tasks, they also pose a threat to society with their ability to create believable misinformation.
Ranked #5 on
Image Manipulation Localization
on Columbia
Large Language Models are Good Prompt Learners for Low-Shot Image Classification
Thus, we propose LLaMP, Large Language Models as Prompt learners, that produces adaptive prompts for the CLIP text encoder, establishing it as the connecting bridge.
ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptation
Large-scale Pre-Training Vision-Language Model such as CLIP has demonstrated outstanding performance in zero-shot classification, e. g. achieving 76. 3% top-1 accuracy on ImageNet without seeing any example, which leads to potential benefits to many tasks that have no labeled data.
Efficient Feature Distillation for Zero-shot Annotation Object Detection
We propose a new setting for detecting unseen objects called Zero-shot Annotation object Detection (ZAD).
MixNorm: Test-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.
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.
SimMER: Simple Maximization of Entropy and Rank for Self-supervised Representation Learning
With both entropy and rank maximization, our method surpasses the state-of-the-art on CIFAR-10 and Mini-ImageNet under the standard linear evaluation protocol.
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification
Combining the Pair Loss with the techniques developed by the MixMatch family, our proposed SimPLE algorithm shows significant performance gains over previous algorithms on CIFAR-100 and Mini-ImageNet, and is on par with the state-of-the-art methods on CIFAR-10 and SVHN.
SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization
We present a novel framework, Spatial Pyramid Attention Network (SPAN) for detection and localization of multiple types of image manipulations.
Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval
We propose an unsupervised hashing method which aims to produce binary codes that preserve the ranking induced by a real-valued representation.
