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Found 19 papers, 10 papers with code
Hyper3D: Efficient 3D Representation via Hybrid Triplane and Octree Feature for Enhanced 3D Shape Variational Auto-Encoders
Existing 3D shape VAEs often employ uniform point sampling and 1D/2D latent representations, such as vector sets or triplanes, leading to significant geometric detail loss due to inadequate surface coverage and the absence of explicit 3D representations in the latent space.
MagicNaming: Consistent Identity Generation by Finding a "Name Space" in T2I Diffusion Models
Specifically, we first extract the embeddings of celebrities' names in the Laion5B dataset with the text encoder of diffusion models.
Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm
Drawing on recent advancements in diffusion models for text-to-image generation, identity-preserved personalization has made significant progress in accurately capturing specific identities with just a single reference image.
PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning
In this work, we address the task of few-shot part segmentation, which aims to segment the different parts of an unseen object using very few labeled examples.
Null-text Guidance in Diffusion Models is Secretly a Cartoon-style Creator
Specifically, we proposed two disturbance methods, i. e., Rollback disturbance (Back-D) and Image disturbance (Image-D), to construct misalignment between the noisy images used for predicting null-text guidance and text guidance (subsequently referred to as \textbf{null-text noisy image} and \textbf{text noisy image} respectively) in the sampling process.
MagicFusion: Boosting Text-to-Image Generation Performance by Fusing Diffusion Models
The advent of open-source AI communities has produced a cornucopia of powerful text-guided diffusion models that are trained on various datasets.
Token Contrast for Weakly-Supervised Semantic Segmentation
Secondly, to further differentiate the low-confidence regions in CAM, we devised a Class Token Contrast module (CTC) inspired by the fact that class tokens in ViT can capture high-level semantics.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Automated machine learning (AutoML) seeks to build ML models with minimal human effort.
Unified Discrete Diffusion for Simultaneous Vision-Language Generation
The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multi-modality signals.
Leveraging GAN Priors for Few-Shot Part Segmentation
Overall, this work is an attempt to explore the internal relevance between generation tasks and perception tasks by prompt designing.
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs
Since data augmentation strategies have largely alleviated the training instability, how to further improve the generative performance of DE-GANs becomes a hotspot.
SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders
In this paper, we explore a potential visual analogue of words, i. e., semantic parts, and we integrate semantic information into the training process of MAE by proposing a Semantic-Guided Masking strategy.
Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment
And the reference space is optimized to capture deep image priors that are useful for quality assessment.
Learning Semantic-aware Normalization for Generative Adversarial Networks
Such a model disentangles latent factors according to the semantic of feature channels by channel-/group- wise fusion of latent codes and feature channels.
Learning Deep Bilinear Transformation for Fine-grained Image Representation
However, the computational cost to learn pairwise interactions between deep feature channels is prohibitively expensive, which restricts this powerful transformation to be used in deep neural networks.
Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition
Learning subtle yet discriminative features (e. g., beak and eyes for a bird) plays a significant role in fine-grained image recognition.
Ranked #1 on
Fine-Grained Image Classification
on iNaturalist
Fine-Grained Image Classification
Fine-Grained Image Recognition
Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition
Two losses are proposed to guide the multi-task learning of channel grouping and part classification, which encourages MA-CNN to generate more discriminative parts from feature channels and learn better fine-grained features from parts in a mutual reinforced way.
Ranked #23 on
Fine-Grained Image Classification
on CUB-200-2011
Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition
The learning at each scale consists of a classification sub-network and an attention proposal sub-network (APN).
Fine-Grained Image Classification
Fine-Grained Image Recognition
+1
