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Found 13 papers, 11 papers with code
iPREFER: An Intelligent Parameter Extractor based on Features for BSIM-CMG Models
This paper introduces an innovative parameter extraction method for BSIM-CMG compact models, seamlessly integrating curve feature extraction and machine learning techniques.
Kosmos-G: Generating Images in Context with Multimodal Large Language Models
These limitations keep them far from the ultimate goal of "image as a foreign language in image generation."
Kosmos-2: Grounding Multimodal Large Language Models to the World
We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new capabilities of perceiving object descriptions (e. g., bounding boxes) and grounding text to the visual world.
Ranked #11 on
Visual Question Answering
on ViP-Bench
Generic-to-Specific Distillation of Masked Autoencoders
Lightweight ViT models limited by the model capacity, however, benefit little from those pre-training mechanisms.
Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks
A big convergence of language, vision, and multimodal pretraining is emerging.
A Unified View of Masked Image Modeling
Masked image modeling has demonstrated great potential to eliminate the label-hungry problem of training large-scale vision Transformers, achieving impressive performance on various downstream tasks.
Foundation Transformers
A big convergence of model architectures across language, vision, speech, and multimodal is emerging.
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
A big convergence of language, vision, and multimodal pretraining is emerging.
Ranked #1 on
Visual Reasoning
on NLVR2 Test
BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers
The large-size BEiT v2 obtains 87. 3% top-1 accuracy for ImageNet-1K (224 size) fine-tuning, and 56. 7% mIoU on ADE20K for semantic segmentation.
Ranked #27 on
Self-Supervised Image Classification
on ImageNet
Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection
Except for the backbone networks, however, other components such as the detector head and the feature pyramid network (FPN) remain trained from scratch, which hinders fully tapping the potential of representation models.
Ranked #3 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Long-tailed Distribution Adaptation
We propose to jointly optimize empirical risks of the unbalanced and balanced domains and approximate their domain divergence by intra-class and inter-class distances, with the aim to adapt models trained on the long-tailed distribution to general distributions in an interpretable way.
Conformer: Local Features Coupling Global Representations for Visual Recognition
Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations.
Ranked #325 on
Image Classification
on ImageNet
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization
TS-CAM finally couples the patch tokens with the semantic-agnostic attention map to achieve semantic-aware localization.
