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Found 22 papers, 13 papers with code
Meta Variance Transfer: Learning to Augment from the Others
Humans have the ability to robustly recognize objects with various factors of variations such as nonrigid transformation, background noise, and change in lighting conditions.
Selective Vision is the Challenge for Visual Reasoning: A Benchmark for Visual Argument Understanding
Understanding these arguments requires selective vision: only specific visual stimuli within an image are relevant to the argument, and relevance can only be understood within the context of a broader argumentative structure.
WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models
As WildJailbreak considerably upgrades the quality and scale of existing safety resources, it uniquely enables us to examine the scaling effects of data and the interplay of data properties and model capabilities during safety training.
WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs
We introduce WildGuard -- an open, light-weight moderation tool for LLM safety that achieves three goals: (1) identifying malicious intent in user prompts, (2) detecting safety risks of model responses, and (3) determining model refusal rate.
Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics
The idea of personality in descriptive psychology, traditionally defined through observable behavior, has now been extended to Large Language Models (LLMs) to better understand their behavior.
SMILE: Multimodal Dataset for Understanding Laughter in Video with Language Models
We introduce this new task to explain why people laugh in a particular video and a dataset for this task.
Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms
NORMLENS consists of 10K human judgments accompanied by free-form explanations covering 2K multimodal situations, and serves as a probe to address two questions: (1) to what extent can models align with average human judgment?
CHAMPAGNE: Learning Real-world Conversation from Large-Scale Web Videos
To train CHAMPAGNE, we collect and release YTD-18M, a large-scale corpus of 18M video-based dialogues.
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing
Our key idea is that an adversarial attack on a biased model that makes decisions based on spurious correlations may generate synthetic bias-conflicting samples, which can then be used as augmented training data for learning a debiased model.
Sample-wise Label Confidence Incorporation for Learning with Noisy Labels
Although recent studies on designing a robust objective function to label noise, known as the robust loss method, have shown promising results for learning with noisy labels, they suffer from the issue of underfitting not only noisy samples but also clean ones, leading to suboptimal model performance.
CORE: Co-planarity Regularized Monocular Geometry Estimation with Weak Supervision
Meanwhile, SANE easily establishes multi-task learning with CORE loss functions on both depth and surface normal estimation, leading to the whole performance leap.
Rethinking Feature-Based Knowledge Distillation for Face Recognition
By constraining the teacher's search space with reverse distillation, we narrow the intrinsic gap and unleash the potential of feature-only distillation.
Measuring and Improving Semantic Diversity of Dialogue Generation
In this paper, we introduce a new automatic evaluation metric to measure the semantic diversity of generated responses.
Towards Accurate Facial Landmark Detection via Cascaded Transformers
We formulate facial landmark detection as a coordinate regression task such that the model can be trained end-to-end.
Ranked #2 on
Face Alignment
on COFW
Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances
To better reflect the style of the character, PDP builds the prompts in the form of dialog that includes the character's utterances as dialog history.
Pushing the Performance Limit of Scene Text Recognizer without Human Annotation
A robust consistency regularization based semi-supervised framework is proposed for STR, which can effectively solve the instability issue due to domain inconsistency between synthetic and real images.
Understanding and Improving the Exemplar-based Generation for Open-domain Conversation
Extensive experiments demonstrate that our proposed training method alleviates the drawbacks of the existing exemplar-based generative models and significantly improves the performance in terms of appropriateness and informativeness.
Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation
G2R consists of two distinct techniques of distillation: the data-level G2R augments the dialogue dataset with additional responses generated by the large-scale generative model, and the model-level G2R transfers the response quality score assessed by the generative model to the score of the retrieval model by the knowledge distillation loss.
Quality-Agnostic Image Recognition via Invertible Decoder
Our two-stage scheme allows the network to produce clean-like and robust features from any quality images, by reconstructing their clean images via the invertible decoder.
Ranked #17 on
Domain Generalization
on ImageNet-C
Self-Reorganizing and Rejuvenating CNNs for Increasing Model Capacity Utilization
In this paper, we propose self-reorganizing and rejuvenating convolutional neural networks; a biologically inspired method for improving the computational resource utilization of neural networks.
Disentangling Label Distribution for Long-tailed Visual Recognition
Although this method surpasses state-of-the-art methods on benchmark datasets, it can be further improved by directly disentangling the source label distribution from the model prediction in the training phase.
Ranked #21 on
Long-tail Learning
on CIFAR-100-LT (ρ=10)
DiscFace: Minimum Discrepancy Learning for Deep Face Recognition
Softmax-based learning methods have shown state-of-the-art performances on large-scale face recognition tasks.
Ranked #1 on
Face Verification
on CALFW
