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09/13/2023
Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics
Multi-modal large language models (MLLMs) are trained based on large lan...
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07/21/2023
Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation
Medical imaging has witnessed remarkable progress but usually requires a...
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06/27/2023
CLIPA-v2: Scaling CLIP Training with 81.1 within a $10,000 Budget; An Extra $4,000 Unlocks 81.8
The recent work CLIPA presents an inverse scaling law for CLIP training ...
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05/11/2023
An Inverse Scaling Law for CLIP Training
CLIP, the first foundation model that connects images and text, has enab...
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04/06/2023
Diffusion Models as Masked Autoencoders
There has been a longstanding belief that generation can facilitate a tr...
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01/25/2023
On the Adversarial Robustness of Camera-based 3D Object Detection
In recent years, camera-based 3D object detection has gained widespread ...
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12/20/2022
Unleashing the Power of Visual Prompting At the Pixel Level
This paper presents a simple and effective visual prompting method for a...
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12/01/2022
Localization vs. Semantics: How Can Language Benefit Visual Representation Learning?
Despite the superior performance brought by vision-and-language pretrain...
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11/29/2022
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
Modern deep neural networks tend to be evaluated on static test sets. On...
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11/27/2022
Navigation as the Attacker Wishes? Towards Building Byzantine-Robust Embodied Agents under Federated Learning
Federated embodied agent learning protects the data privacy of individua...
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11/21/2022
SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training
Video-language pre-training is crucial for learning powerful multi-modal...
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09/06/2022
Bag of Tricks for FGSM Adversarial Training
Adversarial training (AT) with samples generated by Fast Gradient Sign M...
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08/25/2022
Masked Autoencoders Enable Efficient Knowledge Distillers
This paper studies the potential of distilling knowledge from pre-traine...
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06/15/2022
A Simple Data Mixing Prior for Improving Self-Supervised Learning
Data mixing (e.g., Mixup, Cutmix, ResizeMix) is an essential component f...
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06/07/2022
Can CNNs Be More Robust Than Transformers?
The recent success of Vision Transformers is shaking the long dominance ...
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05/24/2022
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks
Unlearnable examples (ULEs) aim to protect data from unauthorized usage ...
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05/24/2022
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 neur...
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05/03/2022
In Defense of Image Pre-Training for Spatiotemporal Recognition
Image pre-training, the current de-facto paradigm for a wide range of vi...
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04/21/2022
Fast AdvProp
Adversarial Propagation (AdvProp) is an effective way to improve recogni...
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02/09/2022
Learning to Bootstrap for Combating Label Noise
Deep neural networks are powerful tools for representation learning, but...
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11/15/2021
iBOT: Image BERT Pre-Training with Online Tokenizer
The success of language Transformers is primarily attributed to the pret...
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11/10/2021
Are Transformers More Robust Than CNNs?
Transformer emerges as a powerful tool for visual recognition. In additi...
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10/01/2021
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images
While neural symbolic methods demonstrate impressive performance in visu...
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06/08/2021
Simulated Adversarial Testing of Face Recognition Models
Most machine learning models are validated and tested on fixed datasets....
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03/23/2021
Robust and Accurate Object Detection via Adversarial Learning
Data augmentation has become a de facto component for training high-perf...
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11/28/2020
Batch Normalization with Enhanced Linear Transformation
Batch normalization (BN) is a fundamental unit in modern deep networks, ...
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10/12/2020
Shape-Texture Debiased Neural Network Training
Shape and texture are two prominent and complementary cues for recognizi...
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06/25/2020
Smooth Adversarial Training
It is commonly believed that networks cannot be both accurate and robust...
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04/12/2020
PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning
Patch-based attacks introduce a perceptible but localized change to the ...
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04/04/2020
Neural Architecture Search for Lightweight Non-Local Networks
Non-Local (NL) blocks have been widely studied in various vision tasks. ...
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11/21/2019
Adversarial Examples Improve Image Recognition
Adversarial examples are commonly viewed as a threat to ConvNets. Here w...
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09/10/2019
UPC: Learning Universal Physical Camouflage Attacks on Object Detectors
In this paper, we study physical adversarial attacks on object detectors...
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06/10/2019
Intriguing properties of adversarial training
Adversarial training is one of the main defenses against adversarial att...
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04/01/2019
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses
This paper focuses on learning transferable adversarial examples specifi...
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12/09/2018
Learning Transferable Adversarial Examples via Ghost Networks
The recent development of adversarial attack has proven that ensemble-ba...
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12/09/2018
Feature Denoising for Improving Adversarial Robustness
Adversarial attacks to image classification systems present challenges t...
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03/31/2018
Adversarial Attacks and Defences Competition
To accelerate research on adversarial examples and robustness of machine...
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03/19/2018
Improving Transferability of Adversarial Examples with Input Diversity
Though convolutional neural networks have achieved state-of-the-art perf...
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12/01/2017
Single-Shot Object Detection with Enriched Semantics
We propose a novel single shot object detection network named Detection ...
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11/13/2017
Visual Concepts and Compositional Voting
It is very attractive to formulate vision in terms of pattern theory Mum...
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11/06/2017
Mitigating adversarial effects through randomization
Convolutional neural networks have demonstrated their powerful ability o...
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09/14/2017
DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion
In this paper, we study the task of detecting semantic parts of an objec...
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07/25/2017
Detecting Semantic Parts on Partially Occluded Objects
In this paper, we address the task of detecting semantic parts on partia...
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03/24/2017