Search Results for author:
Found 64 papers, 30 papers with code
A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain
We annotate 35, 000 SNS posts with both the writer’s subjective sentiment polarity labels and the reader’s objective ones to construct a Japanese sentiment analysis dataset.
IDSOU at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
We introduce the IDSOU submission for the WNUT-2020 task 2: identification of informative COVID-19 English Tweets.
No Annotations for Object Detection in Art through Stable Diffusion
Object detection in art is a valuable tool for the digital humanities, as it allows for faster identification of objects in artistic and historical images compared to humans.
VASCAR: Content-Aware Layout Generation via Visual-Aware Self-Correction
To answer this, we propose Visual-Aware Self-Correction LAyout GeneRation (VASCAR) for LVLM-based content-aware layout generation.
ReLayout: Towards Real-World Document Understanding via Layout-enhanced Pre-training
Recent approaches for visually-rich document understanding (VrDU) uses manually annotated semantic groups, where a semantic group encompasses all semantically relevant but not obviously grouped words.
Putting People in LLMs' Shoes: Generating Better Answers via Question Rewriter
By enhancing the intelligibility of human questions for black-box LLMs, our question rewriter improves the quality of generated answers.
SANER: Annotation-free Societal Attribute Neutralizer for Debiasing CLIP
To mitigate societal bias in CLIP and overcome these limitations simultaneously, we introduce a simple-yet-effective debiasing method called SANER (societal attribute neutralizer) that eliminates attribute information from CLIP text features only of attribute-neutral descriptions.
DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models
Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain.
Explainable Image Recognition via Enhanced Slot-attention Based Classifier
A novel loss function specifically for ESCOUTER is designed to fine-tune the model's behavior, enabling it to toggle between positive and negative explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes
We tackle societal bias in image-text datasets by removing spurious correlations between protected groups and image attributes.
From Descriptive Richness to Bias: Unveiling the Dark Side of Generative Image Caption Enrichment
Large language models (LLMs) have enhanced the capacity of vision-language models to caption visual text.
Enhancing Fake News Detection in Social Media via Label Propagation on Cross-modal Tweet Graph
Our method consistently improves the performance over the state-of-the-art methods on all benchmark datasets and effectively demonstrates its aptitude for generalizing fake news detection in social media.
Would Deep Generative Models Amplify Bias in Future Models?
We investigate the impact of deep generative models on potential social biases in upcoming computer vision models.
Stable Diffusion Exposed: Gender Bias from Prompt to Image
Several studies have raised awareness about social biases in image generative models, demonstrating their predisposition towards stereotypes and imbalances.
Instruct Me More! Random Prompting for Visual In-Context Learning
Our findings suggest that InMeMo offers a versatile and efficient way to enhance the performance of visual ICL with lightweight training.
Improving Facade Parsing with Vision Transformers and Line Integration
We introduce a new pipeline known as Revision-based Transformer Facade Parsing (RTFP).
Learning Bottleneck Concepts in Image Classification
Using some image classification tasks as our testbed, we demonstrate BotCL's potential to rebuild neural networks for better interpretability.
Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis
The duality of content and style is inherent to the nature of art.
Model-Agnostic Gender Debiased Image Captioning
From this observation, we hypothesize that there are two types of gender bias affecting image captioning models: 1) bias that exploits context to predict gender, and 2) bias in the probability of generating certain (often stereotypical) words because of gender.
Uncurated Image-Text Datasets: Shedding Light on Demographic Bias
The increasing tendency to collect large and uncurated datasets to train vision-and-language models has raised concerns about fair representations.
Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation
Human evaluation is critical for validating the performance of text-to-image generative models, as this highly cognitive process requires deep comprehension of text and images.
Inference Time Evidences of Adversarial Attacks for Forensic on Transformers
Vision Transformers (ViTs) are becoming a very popular paradigm for vision tasks as they achieve state-of-the-art performance on image classification.
Contrastive Losses Are Natural Criteria for Unsupervised Video Summarization
Video summarization aims to select the most informative subset of frames in a video to facilitate efficient video browsing.
Learning More May Not Be Better: Knowledge Transferability in Vision and Language Tasks
Is more data always better to train vision-and-language models?
Gender and Racial Bias in Visual Question Answering Datasets
Our findings suggest that there are dangers associated to using VQA datasets without considering and dealing with the potentially harmful stereotypes.
AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval
First, it is rank-insensitive: It ignores the rank positions of successfully localised moments in the top-$K$ ranked list by treating the list as a set.
Quantifying Societal Bias Amplification in Image Captioning
We study societal bias amplification in image captioning.
Optimal Correction Cost for Object Detection Evaluation
OC-cost computes the cost of correcting detections to ground truths as a measure of accuracy.
Transferring Domain-Agnostic Knowledge in Video Question Answering
Video question answering (VideoQA) is designed to answer a given question based on a relevant video clip.
Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation
Have you ever looked at a painting and wondered what is the story behind it?
Built Year Prediction from Buddha Face with Heterogeneous Labels
Buddha statues are a part of human culture, especially of the Asia area, and they have been alongside human civilisation for more than 2, 000 years.
Attending Self-Attention: A Case Study of Visually Grounded Supervision in Vision-and-Language Transformers
The impressive performances of pre-trained visually grounded language models have motivated a growing body of research investigating what has been learned during the pre-training.
PoseRN: A 2D pose refinement network for bias-free multi-view 3D human pose estimation
We propose a new 2D pose refinement network that learns to predict the human bias in the estimated 2D pose.
Ranked #75 on
3D Human Pose Estimation
on Human3.6M
3D Human Pose Estimation
Multi-view 3D Human Pose Estimation
A Picture May Be Worth a Hundred Words for Visual Question Answering
This paper delves into the effectiveness of textual representations for image understanding in the specific context of VQA.
WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations
We annotate 17, 000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset.
GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph
In this paper, we propose a novel use of a knowledge graph, that is constructed on annotated data and pseudo-labeled data.
Development of a Vertex Finding Algorithm using Recurrent Neural Network
Deep learning is a rapidly-evolving technology with possibility to significantly improve physics reach of collider experiments.
Understanding the Role of Scene Graphs in Visual Question Answering
Visual Question Answering (VQA) is of tremendous interest to the research community with important applications such as aiding visually impaired users and image-based search.
Match Them Up: Visually Explainable Few-shot Image Classification
Few-shot learning (FSL) approaches are usually based on an assumption that the pre-trained knowledge can be obtained from base (seen) categories and can be well transferred to novel (unseen) categories.
Ranked #36 on
Few-Shot Image Classification
on CIFAR-FS 5-way (5-shot)
Automated Grading System of Retinal Arterio-venous Crossing Patterns: A Deep Learning Approach Replicating Ophthalmologist's Diagnostic Process of Arteriolosclerosis
Our severity grading method was able to validate crossing points with precision and recall of 96. 3% and 96. 3%, respectively.
Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation
Semantic video segmentation is a key challenge for various applications.
Constructing a Visual Relationship Authenticity Dataset
Visual relationship detection is crucial for scene understanding in images.
Demographic Influences on Contemporary Art with Unsupervised Style Embeddings
Art produced today, on the other hand, is numerous and easily accessible, through the internet and social networks that are used by professional and amateur artists alike to display their work.
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition
Explainable artificial intelligence has been gaining attention in the past few years.
Uncovering Hidden Challenges in Query-Based Video Moment Retrieval
In this paper, we present a series of experiments assessing how well the benchmark results reflect the true progress in solving the moment retrieval task.
A Dataset and Baselines for Visual Question Answering on Art
Our dataset inherently consists of visual (painting-based) and knowledge (comment-based) questions.
Depthwise Spatio-Temporal STFT Convolutional Neural Networks for Human Action Recognition
To address these issues, we propose spatio-temporal short term Fourier transform (STFT) blocks, a new class of convolutional blocks that can serve as an alternative to the 3D convolutional layer and its variants in 3D CNNs.
Knowledge-Based Video Question Answering with Unsupervised Scene Descriptions
To understand movies, humans constantly reason over the dialogues and actions shown in specific scenes and relate them to the overall storyline already seen.
Joint Learning of Vessel Segmentation and Artery/Vein Classification with Post-processing
Retinal imaging serves as a valuable tool for diagnosis of various diseases.
Constructing a Public Meeting Corpus
In this paper, we propose a full pipeline of analysis of a large corpus about a century of public meeting in historical Australian news papers, from construction to visual exploration.
Yoga-82: A New Dataset for Fine-grained Classification of Human Poses
To handle more variety in human poses, we propose the concept of fine-grained hierarchical pose classification, in which we formulate the pose estimation as a classification task, and propose a dataset, Yoga-82, for large-scale yoga pose recognition with 82 classes.
Knowledge-Based Visual Question Answering in Videos
We propose a novel video understanding task by fusing knowledge-based and video question answering.
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases.
Ranked #6 on
Retinal Vessel Segmentation
on CHASE_DB1
KnowIT VQA: Answering Knowledge-Based Questions about Videos
We propose a novel video understanding task by fusing knowledge-based and video question answering.
Historical and Modern Features for Buddha Statue Classification
While Buddhism has spread along the Silk Roads, many pieces of art have been displaced.
BUDA.ART: A Multimodal Content-Based Analysis and Retrieval System for Buddha Statues
We introduce BUDA. ART, a system designed to assist researchers in Art History, to explore and analyze an archive of pictures of Buddha statues.
Understanding Art through Multi-Modal Retrieval in Paintings
In computer vision, visual arts are often studied from a purely aesthetics perspective, mostly by analysing the visual appearance of an artistic reproduction to infer its style, its author, or its representative features.
Context-Aware Embeddings for Automatic Art Analysis
Whereas visual representations are able to capture information about the content and the style of an artwork, our proposed context-aware embeddings additionally encode relationships between different artistic attributes, such as author, school, or historical period.
Rethinking the Evaluation of Video Summaries
Video summarization is a technique to create a short skim of the original video while preserving the main stories/content.
Representing a Partially Observed Non-Rigid 3D Human Using Eigen-Texture and Eigen-Deformation
For full-body reconstruction with loose clothes, we propose to use lower dimensional embeddings of texture and deformation referred to as eigen-texturing and eigen-deformation, to reproduce views of even unobserved surfaces.
iParaphrasing: Extracting Visually Grounded Paraphrases via an Image
These extracted VGPs have the potential to improve language and image multimodal tasks such as visual question answering and image captioning.
Summarization of User-Generated Sports Video by Using Deep Action Recognition Features
The labels are provided by annotators possessing different experience with respect to Kendo to demonstrate how the proposed method adapts to different needs.
Video Summarization using Deep Semantic Features
For this, we design a deep neural network that maps videos as well as descriptions to a common semantic space and jointly trained it with associated pairs of videos and descriptions.
Learning Joint Representations of Videos and Sentences with Web Image Search
In description generation, the performance level is comparable to the current state-of-the-art, although our embeddings were trained for the retrieval tasks.
