Search Results for author:
Found 21 papers, 11 papers with code
ORID: Organ-Regional Information Driven Framework for Radiology Report Generation
To advance these approaches, this paper introduces an Organ-Regional Information Driven (ORID) framework which can effectively integrate multi-modal information and reduce the influence of noise from unrelated organs.
Croc: Pretraining Large Multimodal Models with Cross-Modal Comprehension
Then, we conceptualize visual tokens as analogous to a "foreign language" for the LLMs and propose a mixed attention mechanism with bidirectional visual attention and unidirectional textual attention to comprehensively enhance the understanding of visual tokens.
CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination
In this paper, we introduce CLIP-CID, a novel distillation mechanism that effectively transfers knowledge from a large vision-language foundation model to a smaller model.
VAR-CLIP: Text-to-Image Generator with Visual Auto-Regressive Modeling
However, the original VAR model is constrained to class-conditioned synthesis, relying solely on textual captions for guidance.
Multi-label Cluster Discrimination for Visual Representation Learning
In this paper, we propose a novel Multi-Label Cluster Discrimination method named MLCD to enhance representation learning.
Ranked #2 on
Visual Question Answering (VQA)
on DocVQA test
(using extra training data)
High-Fidelity Facial Albedo Estimation via Texture Quantization
In this paper, we present a novel facial albedo reconstruction model, HiFiAlbedo, which recovers the albedo map directly from a single image without the need for captured albedo data.
RWKV-CLIP: A Robust Vision-Language Representation Learner
Contrastive Language-Image Pre-training (CLIP) has significantly improved performance in various vision-language tasks by expanding the dataset with image-text pairs obtained from websites.
1st Place Solution to the 1st SkatingVerse Challenge
This paper presents the winning solution for the 1st SkatingVerse Challenge.
Plug-and-Play Grounding of Reasoning in Multimodal Large Language Models
P2G utilizes the tool-usage potential of MLLMs to employ expert agents for on-the-fly grounding of reasoning into critical visual and textual elements in images, thereby enabling deliberate reasoning through multimodal prompting.
IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models
Leveraging Stable Diffusion for the generation of personalized portraits has emerged as a powerful and noteworthy tool, enabling users to create high-fidelity, custom character avatars based on their specific prompts.
ALIP: Adaptive Language-Image Pre-training with Synthetic Caption
However, the presence of intrinsic noise and unmatched image-text pairs in web data can potentially affect the performance of representation learning.
Unicom: Universal and Compact Representation Learning for Image Retrieval
To further enhance the low-dimensional feature representation, we randomly select partial feature dimensions when calculating the similarities between embeddings and class-wise prototypes.
Killing Two Birds with One Stone:Efficient and Robust Training of Face Recognition CNNs by Partial FC
In each iteration, positive class centers and a random subset of negative class centers are selected to compute the margin-based softmax loss.
Ranked #1 on
Face Recognition
on MFR
Killing Two Birds With One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC
In each iteration, positive class centers and a random subset of negative class centers are selected to compute the margin-based softmax loss.
Point Cloud Registration using Representative Overlapping Points
3D point cloud registration is a fundamental task in robotics and computer vision.
Sequentially Aggregated Convolutional Networks
We thus exploit the aggregation nature of shortcut connections at a finer architectural level and place them within wide convolutional layers.
DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images
In this paper, we develop a novel unified framework called DeepText for text region proposal generation and text detection in natural images via a fully convolutional neural network (CNN).
Fully Convolutional Recurrent Network for Handwritten Chinese Text Recognition
This paper proposes an end-to-end framework, namely fully convolutional recurrent network (FCRN) for handwritten Chinese text recognition (HCTR).
Handwriting Recognition
Handwritten Chinese Text Recognition
+1
A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model
This paper proposes a deep leaning method to address the challenging facial attractiveness prediction problem.
Improved Deep Convolutional Neural Network For Online Handwritten Chinese Character Recognition using Domain-Specific Knowledge
Deep convolutional neural networks (DCNNs) have achieved great success in various computer vision and pattern recognition applications, including those for handwritten Chinese character recognition (HCCR).
DropSample: A New Training Method to Enhance Deep Convolutional Neural Networks for Large-Scale Unconstrained Handwritten Chinese Character Recognition
Inspired by the theory of Leitners learning box from the field of psychology, we propose DropSample, a new method for training deep convolutional neural networks (DCNNs), and apply it to large-scale online handwritten Chinese character recognition (HCCR).
