Search Results for author:
Found 14 papers, 10 papers with code
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Scaling up contrastive language-image pretraining (CLIP) is critical for empowering both vision and multimodal models.
Ranked #1 on
Zero-Shot Transfer Image Classification
on SUN
Image Classification
Zero-Shot Transfer Image Classification
Generative Multimodal Models are In-Context Learners
The human ability to easily solve multimodal tasks in context (i. e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggled to imitate.
Ranked #19 on
Visual Question Answering
on MM-Vet
CapsFusion: Rethinking Image-Text Data at Scale
To provide higher-quality and more scalable multimodal pretraining data, we propose CapsFusion, an advanced framework that leverages large language models to consolidate and refine information from both web-based image-text pairs and synthetic captions.
Generative Pretraining in Multimodality
We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context.
Ranked #1 on
Visual Question Answering
on VQA v2
SegGPT: Segmenting Everything In Context
We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images.
Ranked #1 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
(using extra training data)
SegGPT: Towards Segmenting Everything in Context
We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images.
HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling
A key idea of efficient implementation is to discard the masked image patches (or tokens) throughout the target network (encoder), which requires the encoder to be a plain vision transformer (e. g., ViT), albeit hierarchical vision transformers (e. g., Swin Transformer) have potentially better properties in formulating vision inputs.
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.
Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection
Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects.
Ranked #34 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
FreeAnchor: Learning to Match Anchors for Visual Object Detection
In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner.
Ranked #136 on
Object Detection
on COCO test-dev
Adversarial Samples on Android Malware Detection Systems for IoT Systems
An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis.
Weighted-Sampling Audio Adversarial Example Attack
In this paper, we propose~\textit{weighted-sampling audio adversarial examples}, focusing on the numbers and the weights of distortion to reinforce the attack.
A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm
Our experimental results show that both the MNIST images and the CIFAR-10 images can be perturbed to successful generate a black-box attack with 100\% probability on average.
