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Found 22 papers, 15 papers with code
SIMILE: Introducing Sequential Information towards More Effective Imitation Learning
Reinforcement learning (RL) is a metaheuristic aiming at teaching an agent to interact with an environment and maximizing the reward in a complex task.
Finding Visual Task Vectors
In this work, we analyze the activations of MAE-VQGAN, a recent Visual Prompting model, and find task vectors, activations that encode task-specific information.
Sequential Modeling Enables Scalable Learning for Large Vision Models
We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data.
Understanding Pan-Sharpening via Generalized Inverse
By the generalized inverse theory, we derived two forms of general inverse matrix formulations that can correspond to the two prominent classes of Pan-sharpening methods, that is, component substitution and multi-resolution analysis methods.
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search
(2) We find regions in the latent space that lead to distorted images independent of the text prompt, suggesting that parts of the latent space are not well-structured.
Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification
We hope that this study can direct future research on the application of Transformers to a larger variety of medical imaging tasks.
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
However, we uncover a striking contradiction to this promise: active learning fails to select data as efficiently as random selection at the first few choices.
Masked Autoencoders Enable Efficient Knowledge Distillers
For example, by distilling the knowledge from an MAE pre-trained ViT-L into a ViT-B, our method achieves 84. 0% ImageNet top-1 accuracy, outperforming the baseline of directly distilling a fine-tuned ViT-L by 1. 2%.
Can CNNs Be More Robust Than Transformers?
The recent success of Vision Transformers is shaking the long dominance of Convolutional Neural Networks (CNNs) in image recognition for a decade.
Fast AdvProp
Specifically, our modifications in Fast AdvProp are guided by the hypothesis that disentangled learning with adversarial examples is the key for performance improvements, while other training recipes (e. g., paired clean and adversarial training samples, multi-step adversarial attackers) could be largely simplified.
Point-Level Region Contrast for Object Detection Pre-Training
In this work we present point-level region contrast, a self-supervised pre-training approach for the task of object detection.
Are Transformers More Robust Than CNNs?
Transformer emerges as a powerful tool for visual recognition.
Ranked #1 on
Adversarial Robustness
on Stylized ImageNet
Glance-and-Gaze Vision Transformer
It is motivated by the Glance and Gaze behavior of human beings when recognizing objects in natural scenes, with the ability to efficiently model both long-range dependencies and local context.
CateNorm: Categorical Normalization for Robust Medical Image Segmentation
We propose a new normalization strategy, named categorical normalization (CateNorm), to normalize the activations according to categorical statistics.
TransFG: A Transformer Architecture for Fine-grained Recognition
Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences.
Ranked #4 on
Fine-Grained Image Classification
on CUB-200-2011
Mask Guided Matting via Progressive Refinement Network
We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance.
Unsupervised Part Discovery via Feature Alignment
Specifically, given a training image, we find a set of similar images that show instances of the same object category in the same pose, through an affine alignment of their corresponding feature maps.
Can Temporal Information Help with Contrastive Self-Supervised Learning?
To this end, we present Temporal-aware Contrastive self-supervised learningTaCo, as a general paradigm to enhance video CSL.
CoKe: Localized Contrastive Learning for Robust Keypoint Detection
In this paper, we introduce a contrastive learning framework for keypoint detection (CoKe).
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
3D convolution neural networks (CNN) have been proved very successful in parsing organs or tumours in 3D medical images, but it remains sophisticated and time-consuming to choose or design proper 3D networks given different task contexts.
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
Yet there has been evidence that current benchmark datasets suffer from bias, and current state-of-the-art models cannot be easily evaluated on their intermediate reasoning process.
Ranked #1 on
Referring Expression Segmentation
on CLEVR-Ref+
Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data
In particular, this enables images in the training dataset to be matched to a virtual 3D model of the object (for simplicity, we assume that the object viewpoint can be estimated by standard techniques).
