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Found 49 papers, 34 papers with code
Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics
Multi-modal large language models (MLLMs) are trained based on large language models (LLM), with an enhanced capability to comprehend multi-modal inputs and generate textual responses.
SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation
To address this limitation, we present Masked Multi-view with Swin Transformers (SwinMM), a novel multi-view pipeline for enabling accurate and data-efficient self-supervised medical image analysis.
Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation
Specifically, our approach first involves training a segmentation model on a small set of clean labeled images to generate initial labels for unlabeled data.
CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a \$10,000 Budget; An Extra \$4,000 Unlocks 81.8% Accuracy
The recent work CLIPA presents an inverse scaling law for CLIP training -- whereby the larger the image/text encoders used, the shorter the sequence length of image/text tokens that can be applied in training.
An Inverse Scaling Law for CLIP Training
CLIP, the first foundation model that connects images and text, has enabled many recent breakthroughs in computer vision.
Ranked #8 on
Zero-Shot Transfer Image Classification
on ImageNet
Diffusion Models as Masked Autoencoders
There has been a longstanding belief that generation can facilitate a true understanding of visual data.
On the Adversarial Robustness of Camera-based 3D Object Detection
In recent years, camera-based 3D object detection has gained widespread attention for its ability to achieve high performance with low computational cost.
Benchmarking Robustness in Neural Radiance Fields
We find that NeRF-based models are significantly degraded in the presence of corruption, and are more sensitive to a different set of corruptions than image recognition models.
Unleashing the Power of Visual Prompting At the Pixel Level
This paper presents a simple and effective visual prompting method for adapting pre-trained models to downstream recognition tasks.
Localization vs. Semantics: How Can Language Benefit Visual Representation Learning?
In this work, we investigate how language can help with visual representation learning from a probing perspective.
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
One shortcoming of this is the fact that these deep neural networks cannot be easily evaluated for robustness issues with respect to specific scene variations.
Navigation as Attackers Wish? Towards Building Byzantine-Robust Embodied Agents under Federated Learning
However, since the local data is inaccessible to the server under federated learning, attackers may easily poison the training data of the local client to build a backdoor in the agent without notice.
SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training
Coupling all these designs allows our method to enjoy both competitive performances on text-to-video retrieval and video question answering tasks, and much less pre-training costs by 1. 9X or more.
Bag of Tricks for FGSM Adversarial Training
Adversarial training (AT) with samples generated by Fast Gradient Sign Method (FGSM), also known as FGSM-AT, is a computationally simple method to train robust networks.
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%.
A Simple Data Mixing Prior for Improving Self-Supervised Learning
More notably, our SDMP is the first method that successfully leverages data mixing to improve (rather than hurt) the performance of Vision Transformers in the self-supervised setting.
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.
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks
The score-based query attacks (SQAs) pose practical threats to deep neural networks by crafting adversarial perturbations within dozens of queries, only using the model's output scores.
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks
Based on OPS, we introduce an unlearnable dataset called CIFAR-10-S, which is indistinguishable from CIFAR-10 by humans but induces the trained model to extremely low accuracy.
In Defense of Image Pre-Training for Spatiotemporal Recognition
Inspired by this observation, we hypothesize that the key to effectively leveraging image pre-training lies in the decomposition of learning spatial and temporal features, and revisiting image pre-training as the appearance prior to initializing 3D kernels.
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.
Learning to Bootstrap for Combating Label Noise
Specifically, our method dynamically adjusts the per-sample importance weight between the real observed labels and pseudo-labels, where the weights are efficiently determined in a meta process.
Ranked #8 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
iBOT: Image BERT Pre-Training with Online Tokenizer
We present a self-supervised framework iBOT that can perform masked prediction with an online tokenizer.
Ranked #1 on
Unsupervised Image Classification
on ImageNet
Are Transformers More Robust Than CNNs?
Transformer emerges as a powerful tool for visual recognition.
Ranked #1 on
Adversarial Robustness
on ImageNet-A
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images
Our experiments show CCO substantially boosts the performance of neural symbolic methods on real images.
Image BERT Pre-training with Online Tokenizer
The success of language Transformers is primarily attributed to the pretext task of masked language modeling (MLM), where texts are first tokenized into semantically meaningful pieces.
Simulated Adversarial Testing of Face Recognition Models
In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios.
Robust and Accurate Object Detection via Adversarial Learning
Data augmentation has become a de facto component for training high-performance deep image classifiers, but its potential is under-explored for object detection.
Ranked #18 on
Object Detection
on COCO-O
Batch Normalization with Enhanced Linear Transformation
Batch normalization (BN) is a fundamental unit in modern deep networks, in which a linear transformation module was designed for improving BN's flexibility of fitting complex data distributions.
Shape-Texture Debiased Neural Network Training
To prevent models from exclusively attending on a single cue in representation learning, we augment training data with images with conflicting shape and texture information (eg, an image of chimpanzee shape but with lemon texture) and, most importantly, provide the corresponding supervisions from shape and texture simultaneously.
Ranked #559 on
Image Classification
on ImageNet
Smooth Adversarial Training
SAT also works well with larger networks: it helps EfficientNet-L1 to achieve 82. 2% accuracy and 58. 6% robustness on ImageNet, outperforming the previous state-of-the-art defense by 9. 5% for accuracy and 11. 6% for robustness.
PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning
PatchAttack induces misclassifications by superimposing small textured patches on the input image.
Neural Architecture Search for Lightweight Non-Local Networks
However, it has been rarely explored to embed the NL blocks in mobile neural networks, mainly due to the following challenges: 1) NL blocks generally have heavy computation cost which makes it difficult to be applied in applications where computational resources are limited, and 2) it is an open problem to discover an optimal configuration to embed NL blocks into mobile neural networks.
Ranked #60 on
Neural Architecture Search
on ImageNet
Adversarial Examples Improve Image Recognition
We show that AdvProp improves a wide range of models on various image recognition tasks and performs better when the models are bigger.
Ranked #6 on
Domain Generalization
on VizWiz-Classification
Universal Physical Camouflage Attacks on Object Detectors
In this paper, we study physical adversarial attacks on object detectors in the wild.
Intriguing properties of adversarial training at scale
This two-domain hypothesis may explain the issue of BN when training with a mixture of clean and adversarial images, as estimating normalization statistics of this mixture distribution is challenging.
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses
We observe the property of regional homogeneity in adversarial perturbations and suggest that the defenses are less robust to regionally homogeneous perturbations.
Feature Denoising for Improving Adversarial Robustness
This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks.
Learning Transferable Adversarial Examples via Ghost Networks
The critical principle of ghost networks is to apply feature-level perturbations to an existing model to potentially create a huge set of diverse models.
Adversarial Attacks and Defences Competition
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them.
Improving Transferability of Adversarial Examples with Input Diversity
We hope that our proposed attack strategy can serve as a strong benchmark baseline for evaluating the robustness of networks to adversaries and the effectiveness of different defense methods in the future.
Single-Shot Object Detection with Enriched Semantics
Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module.
Visual Concepts and Compositional Voting
We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles.
Mitigating Adversarial Effects Through Randomization
Convolutional neural networks have demonstrated high accuracy on various tasks in recent years.
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
The first layer extracts the evidence of local visual cues, and the second layer performs a voting mechanism by utilizing the spatial relationship between visual cues and semantic parts.
Detecting Semantic Parts on Partially Occluded Objects
Our approach detects semantic parts by accumulating the confidence of local visual cues.
Adversarial Examples for Semantic Segmentation and Object Detection
Our observation is that both segmentation and detection are based on classifying multiple targets on an image (e. g., the basic target is a pixel or a receptive field in segmentation, and an object proposal in detection), which inspires us to optimize a loss function over a set of pixels/proposals for generating adversarial perturbations.
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Unsupervised learning of object semantic parts from internal states of CNNs by population encoding
We address the key question of how object part representations can be found from the internal states of CNNs that are trained for high-level tasks, such as object classification.
