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Found 15 papers, 12 papers with code
MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators
Recent advances in Text-to-Video generation (T2V) have achieved remarkable success in synthesizing high-quality general videos from textual descriptions.
Envision3D: One Image to 3D with Anchor Views Interpolation
To address this issue, we propose a novel cascade diffusion framework, which decomposes the challenging dense views generation task into two tractable stages, namely anchor views generation and anchor views interpolation.
DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing
In this work, we extend this editing framework to diffusion models and propose a novel approach DragDiffusion.
Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models
Public large-scale text-to-image diffusion models, such as Stable Diffusion, have gained significant attention from the community.
Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning
However, in this work, we find that the CE loss is not ideal for the base session training as it suffers poor class separation in terms of representations, which further degrades generalization to novel classes.
Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning
To remedy this problem caused by the data heterogeneity, we propose {\sc FedDecorr}, a novel method that can effectively mitigate dimensional collapse in federated learning.
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning
Specifically, we experimentally show that directly encouraging CIL Learner at the initial phase to output similar representations as the model jointly trained on all classes can greatly boost the CIL performance.
Refiner: Refining Self-attention for Vision Transformers
Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs.
Ranked #174 on
Image Classification
on ImageNet
Continual Learning via Bit-Level Information Preserving
Continual learning tackles the setting of learning different tasks sequentially.
All Tokens Matter: Token Labeling for Training Better Vision Transformers
In this paper, we present token labeling -- a new training objective for training high-performance vision transformers (ViTs).
Ranked #3 on
Efficient ViTs
on ImageNet-1K (With LV-ViT-S)
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study.
Ranked #403 on
Image Classification
on ImageNet
Towards Disentangling Non-Robust and Robust Components in Performance Metric
Then, we show by experiments that DNNs under standard training rely heavily on optimizing the non-robust component in achieving decent performance.
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric
Despite many previous works studying the reason behind such adversarial behavior, the relationship between the generalization performance and adversarial behavior of DNNs is still little understood.
Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks
Our work is based on an excellent idea that whitening the inputs of neural networks can achieve a fast convergence speed.
Learning Pixel-wise Labeling from the Internet without Human Interaction
Collecting sufficient annotated data is very expensive in many applications, especially for pixel-level prediction tasks such as semantic segmentation.
