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.
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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%.
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.
GenLIP pretrains ViTs to generate language tokens from visual tokens via autoregressive language modeling, matching strong baselines on multimodal tasks with less data.
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
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Hierarchically Robust Zero-shot Vision-language Models
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.
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OD3: Optimization-free Dataset Distillation for Object Detection
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%.
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End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer
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.
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SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
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.
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Graph-based Knowledge Distillation by Multi-head Attention Network
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.
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Let ViT Speak: Generative Language-Image Pre-training
GenLIP pretrains ViTs to generate language tokens from visual tokens via autoregressive language modeling, matching strong baselines on multimodal tasks with less data.
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Seed1.5-VL Technical Report
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.