Search Results for author:
Found 30 papers, 19 papers with code
Dense Text-to-Image Generation with Attention Modulation
To address this, we propose DenseDiffusion, a training-free method that adapts a pre-trained text-to-image model to handle such dense captions while offering control over the scene layout.
Overcoming Weak Visual-Textual Alignment for Video Moment Retrieval
Video moment retrieval (VMR) identifies a specific moment in an untrimmed video for a given natural language query.
Ranked #2 on
Moment Retrieval
on Charades-STA
Query-Efficient Black-Box Red Teaming via Bayesian Optimization
To this end, we propose Bayesian red teaming (BRT), novel query-efficient black-box red teaming methods based on Bayesian optimization, which iteratively identify diverse positive test cases leading to model failures by utilizing the pre-defined user input pool and the past evaluations.
Panoramic Image-to-Image Translation
This task is difficult due to the geometric distortion of panoramic images and the lack of a panoramic image dataset with diverse conditions, like weather or time.
Text-Conditioned Sampling Framework for Text-to-Image Generation with Masked Generative Models
Token-based masked generative models are gaining popularity for their fast inference time with parallel decoding.
Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation
Text-to-3D generation has shown rapid progress in recent days with the advent of score distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neural radiance field (NeRF) in the zero-shot setting.
UniXGen: A Unified Vision-Language Model for Multi-View Chest X-ray Generation and Report Generation
We also find that view-specific special tokens can distinguish between different views and properly generate specific views even if they do not exist in the dataset, and utilizing multi-view chest X-rays can faithfully capture the abnormal findings in the additional X-rays.
Robust Camera Pose Refinement for Multi-Resolution Hash Encoding
Multi-resolution hash encoding has recently been proposed to reduce the computational cost of neural renderings, such as NeRF.
Semi-Parametric Video-Grounded Text Generation
Efficient video-language modeling should consider the computational cost because of a large, sometimes intractable, number of video frames.
Ranked #6 on
Video Question Answering
on NExT-QA
SelecMix: Debiased Learning by Contradicting-pair Sampling
Experiments on standard benchmarks demonstrate the effectiveness of the method, in particular when label noise complicates the identification of bias-conflicting examples.
Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval
Video corpus moment retrieval (VCMR) is the task to retrieve the most relevant video moment from a large video corpus using a natural language query.
Ranked #2 on
Video Corpus Moment Retrieval
on TVR
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models
To combine parameter-efficient adaptation and model compression, we propose AlphaTuning consisting of post-training quantization of the pre-trained language model and fine-tuning only some parts of quantized parameters for a target task.
The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
As a result, GST scales the amount of training data up to an order of magnitude that of VisDial (1. 2M to 12. 9M QA data).
Conditional Text Generation
Out-of-Distribution Detection
+1
Mutual Information Divergence: A Unified Metric for Multimodal Generative Models
Based on a recent trend that multimodal generative evaluations exploit a vison-and-language pre-trained model, we propose the negative Gaussian cross-mutual information using the CLIP features as a unified metric, coined by Mutual Information Divergence (MID).
Ranked #1 on
Human Judgment Classification
on Pascal-50S
Hallucination Pair-wise Detection (1-ref)
Hallucination Pair-wise Detection (4-ref)
+4
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning
Cross-domain few-shot learning (CD-FSL), where there are few target samples under extreme differences between source and target domains, has recently attracted huge attention.
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
This data enables self-supervised pre-training on the target domain, in addition to supervised pre-training on the source domain.
Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation
We present the efficiency of semi-orthogonal embedding for unsupervised anomaly segmentation.
Ranked #1 on
Unsupervised Anomaly Detection
on KolektorSDD
(using extra training data)
Logit As Auxiliary Weak-supervision for More Reliable and Accurate Prediction
When a person identifies objects, he or she can think by associating objects to many classes and conclude by taking inter-class relations into account.
Multi-step Estimation for Gradient-based Meta-learning
To validate our method, we experiment on meta-transfer learning and few-shot learning tasks for multiple settings.
Analysis of Online Conversations to Detect Cyberpredators Using Recurrent Neural Networks
The task is divided into two stages, 1) the classification of each message, and 2) the classification of the entire conversation.
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context.
Multimodal Dual Attention Memory for Video Story Question Answering
We confirm the best performance of the dual attention mechanism combined with late fusion by ablation studies.
Bilinear Attention Networks
In this paper, we propose bilinear attention networks (BAN) that find bilinear attention distributions to utilize given vision-language information seamlessly.
Ranked #11 on
Phrase Grounding
on Flickr30k Entities Test
Visual Explanations from Hadamard Product in Multimodal Deep Networks
Kim et al. (2016) show that the Hadamard product in multimodal deep networks, which is well-known for the joint function of visual question answering tasks, implicitly performs an attentional mechanism for visual inputs.
CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication
The game involves two players: a Teller and a Drawer.
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Catastrophic forgetting is a problem of neural networks that loses the information of the first task after training the second task.
Hadamard Product for Low-rank Bilinear Pooling
Bilinear models provide rich representations compared with linear models.
Multimodal Residual Learning for Visual QA
We present Multimodal Residual Networks (MRN) for the multimodal residual learning of visual question-answering, which extends the idea of the deep residual learning.
rnn : Recurrent Library for Torch
The rnn package provides components for implementing a wide range of Recurrent Neural Networks.
