A hierarchical adversarial fine-tuning method for VLMs aligns image and text embeddings at multiple hierarchy depths with theoretical margin connections to boost robustness to leaf and superclass attacks while using multiple trees for semantic variety.
Imagenet: A large-scale hierarchical image database
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
roles
dataset 1polarities
use dataset 1representative citing papers
OD3 presents an optimization-free dataset distillation framework for object detection that reports new state-of-the-art accuracy on COCO and VOC at compression ratios from 0.25% to 5%.
GenLIP pretrains ViTs to generate language tokens from images via LM objective without contrastive batches or extra decoders, matching baselines on less data and improving on OCR after multi-resolution continued pretraining.
An end-to-end autoregressive model with a jointly trained 1D semantic tokenizer achieves state-of-the-art FID 1.48 on ImageNet 256x256 generation without guidance.
SDXL improves upon prior Stable Diffusion versions through a larger UNet backbone, dual text encoders, novel conditioning, and a refinement model, producing higher-fidelity images competitive with black-box state-of-the-art generators.
Multi-head attention constructs a graph of dataset relations from the teacher embedding procedure and transfers it to the student via multi-task learning, yielding 7.05% higher CIFAR-100 accuracy than the student alone and 2.46% above prior SOTA.
Seed1.5-VL is a compact multimodal model that sets new records on dozens of vision-language benchmarks and outperforms prior systems on agent-style tasks.
citing papers explorer
No citing papers match the current filters.