Search Results for author:
Found 17 papers, 13 papers with code
I-Max: Maximize the Resolution Potential of Pre-trained Rectified Flow Transformers with Projected Flow
Rectified Flow Transformers (RFTs) offer superior training and inference efficiency, making them likely the most viable direction for scaling up diffusion models.
Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT
Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers that establishes a unified framework for transforming noise into various modalities, such as images and videos, conditioned on text instructions.
Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers
Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lacks sufficient implementation details.
DemoFusion: Democratising High-Resolution Image Generation With No $$$
High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but, due to the enormous capital investment required for training, it is increasingly centralised to a few large corporations, and hidden behind paywalls.
Multi-View Active Fine-Grained Visual Recognition
Despite the remarkable progress of Fine-grained visual classification (FGVC) with years of history, it is still limited to recognizing 2 images.
Fine-Grained Image Classification
Fine-Grained Visual Recognition
Task-aware Adaptive Learning for Cross-domain Few-shot Learning
In this paper, we first observe the dependence of task-specific parameter configuration on the target task.
On-the-Fly Category Discovery
Our code is available at https://github. com/PRIS-CV/On-the-fly-Category-Discovery.
An Erudite Fine-Grained Visual Classification Model
Therefore, we first propose a feature disentanglement module and a feature re-fusion module to reduce negative transfer and boost positive transfer between different datasets.
Multi-View Active Fine-Grained Recognition
As fine-grained visual classification (FGVC) being developed for decades, great works related have exposed a key direction -- finding discriminative local regions and revealing subtle differences.
Learning Invariant Visual Representations for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set.
Caption Feature Space Regularization for Audio Captioning
To eliminate this negative effect, in this paper, we propose a two-stage framework for audio captioning: (i) in the first stage, via the contrastive learning, we construct a proxy feature space to reduce the distances between captions correlated to the same audio, and (ii) in the second stage, the proxy feature space is utilized as additional supervision to encourage the model to be optimized in the direction that benefits all the correlated captions.
Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction
Adaptation to out-of-distribution data is a meta-challenge for all statistical learning algorithms that strongly rely on the i. i. d.
Clue Me In: Semi-Supervised FGVC with Out-of-Distribution Data
Despite great strides made on fine-grained visual classification (FGVC), current methods are still heavily reliant on fully-supervised paradigms where ample expert labels are called for.
Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained Features
Finally, we can obtain multiple discriminative regions on high-level feature channels and obtain multiple more minute regions within these discriminative regions on middle-level feature channels.
Knowledge Transfer Based Fine-grained Visual Classification
Convolution neural networks (CNNs), which employ the cross entropy loss (CE-loss) as the loss function, show poor performance since the model can only learn the most discriminative part and ignore other meaningful regions.
Ranked #41 on
Fine-Grained Image Classification
on CUB-200-2011
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
In this work, we propose a novel framework for fine-grained visual classification to tackle these problems.
Ranked #21 on
Fine-Grained Image Classification
on Stanford Cars
