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Found 39 papers, 23 papers with code
A Bayesian Perspective On Training Data Attribution
We recommend that future researchers and practitioners trust TDA estimates only in such cases.
Playing repeated games with Large Language Models
In a large set of two players-two strategies games, we find that LLMs are particularly good at games where valuing their own self-interest pays off, like the iterated Prisoner's Dilemma family.
Neglected Free Lunch; Learning Image Classifiers Using Annotation Byproducts
We refer to the new paradigm of training models with annotation byproducts as learning using annotation byproducts (LUAB).
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
We prove that these distributions recover the correct posteriors of the data-generating process, including its level of aleatoric uncertainty, up to a rotation of the latent space.
SelecMix: Debiased Learning by Contradicting-pair Sampling
Experiments on standard benchmarks demonstrate the effectiveness of the method, in particular when label noise complicates the identification of bias-conflicting examples.
Scratching Visual Transformer's Back with Uniform Attention
We observe that the inclusion of CB reduces the degree of density in the original attention maps and increases both the capacity and generalizability of the ViT models.
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets
- In these cases, studies on OOD generalization that use ID performance for model selection (a common recommended practice) will necessarily miss the best-performing models, making these studies blind to a whole range of phenomena.
Dataset Condensation via Efficient Synthetic-Data Parameterization
The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning.
ECCV Caption: Correcting False Negatives by Collecting Machine-and-Human-verified Image-Caption Associations for MS-COCO
Image-Text matching (ITM) is a common task for evaluating the quality of Vision and Language (VL) models.
Weakly Supervised Semantic Segmentation using Out-of-Distribution Data
However, training on class labels only, classifiers suffer from the spurious correlation between foreground and background cues (e. g. train and rail), fundamentally bounding the performance of WSSS.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification
Taken together, our contributions on the data augmentation strategies yield a strong training recipe for few-shot text classification tasks.
Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective
This phenomenon, also known as shortcut learning, is emerging as a key limitation of the current generation of machine learning models.
Keep CALM and Improve Visual Feature Attribution
The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks.
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
Effective control and prediction of dynamical systems often require appropriate handling of continuous-time and discrete, event-triggered processes.
Rethinking Spatial Dimensions of Vision Transformers
We empirically show that such a spatial dimension reduction is beneficial to a transformer architecture as well, and propose a novel Pooling-based Vision Transformer (PiT) upon the original ViT model.
Ranked #303 on
Image Classification
on ImageNet
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels
However, they have not fixed the training set, presumably because of a formidable annotation cost.
Ranked #21 on
Image Classification
on OmniBenchmark
Probabilistic Embeddings for Cross-Modal Retrieval
Instead, we propose to use Probabilistic Cross-Modal Embedding (PCME), where samples from the different modalities are represented as probabilistic distributions in the common embedding space.
VideoMix: Rethinking Data Augmentation for Video Classification
Recent data augmentation strategies have been reported to address the overfitting problems in static image classifiers.
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets
In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
Because of the scale invariance, this modification only alters the effective step sizes without changing the effective update directions, thus enjoying the original convergence properties of GD optimizers.
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods
Despite apparent human-level performances of deep neural networks (DNN), they behave fundamentally differently from humans.
Reliable Fidelity and Diversity Metrics for Generative Models
In this paper, we show that even the latest version of the precision and recall metrics are not reliable yet.
Evaluating Weakly Supervised Object Localization Methods Right
In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set.
On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention
Scene text recognition (STR) is the task of recognizing character sequences in natural scenes.
Ranked #2 on
Scene Text Recognition
on ICDAR 2003
Learning De-biased Representations with Biased Representations
This tactic is feasible in many scenarios where it is much easier to define a set of biased representations than to define and quantify bias.
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Modeling Uncertainty with Hedged Instance Embeddings
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.
What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
Many new proposals for scene text recognition (STR) models have been introduced in recent years.
Ranked #6 on
Scene Text Recognition
on ICDAR 2003
Modeling Uncertainty with Hedged Instance Embedding
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.
Sequential Attacks on Agents for Long-Term Adversarial Goals
As a result of our attack, the victim agent is misguided to optimise for the adversarial reward over time.
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning
At the core of FL is a network of anonymous user devices sharing training information (model parameter updates) computed locally on personal data.
Natural and Effective Obfuscation by Head Inpainting
As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection.
Towards Reverse-Engineering Black-Box Neural Networks
On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks -- we show that the revealed internal information helps generate more effective adversarial examples against the black box model.
Person Recognition in Personal Photo Collections
Person recognition in social media photos sets new challenges for computer vision, including non-cooperative subjects (e. g. backward viewpoints, unusual poses) and great changes in appearance.
Generating Descriptions with Grounded and Co-Referenced People
At training time, we first learn how to localize characters by relating their visual appearance to mentions in the descriptions via a semi-supervised approach.
Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective
We derive the optimal strategy for the user that assures an upper bound on the recognition rate independent of the recogniser's counter measure.
Exploiting saliency for object segmentation from image level labels
We show how to combine both information sources in order to recover 80% of the fully supervised performance - which is the new state of the art in weakly supervised training for pixel-wise semantic labelling.
Ranked #25 on
Semantic Segmentation
on PASCAL VOC 2012 val
Faceless Person Recognition; Privacy Implications in Social Media
As we shift more of our lives into the virtual domain, the volume of data shared on the web keeps increasing and presents a threat to our privacy.
Person Recognition in Personal Photo Collections
Recognising persons in everyday photos presents major challenges (occluded faces, different clothing, locations, etc.)
