56 citations · 194 across the 24 of their papers we have counts for
24 papers · 1 filter
Inference Time Evidences of Adversarial Attacks for Forensic on Transformers
Hugo Lemarchant, Liangzi Li, Yiming Qian +2
Vision Transformers (ViTs) are becoming a very popular paradigm for vision tasks as they achieve state-of-the-art performance on image classification. However, although early works…
Understanding the Role of Scene Graphs in Visual Question Answering
Vinay Damodaran, Sharanya Chakravarthy, Akshay Kumar +5
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. In th…
Match Them Up: Visually Explainable Few-shot Image Classification
Bowen Wang, Liangzhi Li, Manisha Verma +3
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 nove…
Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation
Bowen Wang, Liangzhi Li, Yuta Nakashima +3
Semantic video segmentation is a key challenge for various applications. This paper presents a new model named Noisy-LSTM, which is trainable in an end-to-end manner, with convolut…
Constructing a Visual Relationship Authenticity Dataset
Chenhui Chu, Yuto Takebayashi, Mishra Vipul +1
A visual relationship denotes a relationship between two objects in an image, which can be represented as a triplet of (subject; predicate; object). Visual relationship detection i…
Uncovering Hidden Challenges in Query-Based Video Moment Retrieval
Mayu Otani, Yuta Nakashima, Esa Rahtu +1
The query-based moment retrieval is a problem of localising a specific clip from an untrimmed video according a query sentence. This is a challenging task that requires interpretat…
Demographic Influences on Contemporary Art with Unsupervised Style Embeddings
Nikolai Huckle, Noa Garcia, Yuta Nakashima
Computational art analysis has, through its reliance on classification tasks, prioritised historical datasets in which the artworks are already well sorted with the necessary annot…
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition
Liangzhi Li, Bowen Wang, Manisha Verma +3
Explainable artificial intelligence has been gaining attention in the past few years. However, most existing methods are based on gradients or intermediate features, which are not…
A Dataset and Baselines for Visual Question Answering on Art
Noa Garcia, Chentao Ye, Zihua Liu +5
Answering questions related to art pieces (paintings) is a difficult task, as it implies the understanding of not only the visual information that is shown in the picture, but also…
Depthwise Spatio-Temporal STFT Convolutional Neural Networks for Human Action Recognition
Sudhakar Kumawat, Manisha Verma, Yuta Nakashima +1
Conventional 3D convolutional neural networks (CNNs) are computationally expensive, memory intensive, prone to overfitting, and most importantly, there is a need to improve their f…
Knowledge-Based Video Question Answering with Unsupervised Scene Descriptions
Noa Garcia, Yuta Nakashima
To understand movies, humans constantly reason over the dialogues and actions shown in specific scenes and relate them to the overall storyline already seen. Inspired by this behav…
Joint Learning of Vessel Segmentation and Artery/Vein Classification with Post-processing
Liangzhi Li, Manisha Verma, Yuta Nakashima +2
Retinal imaging serves as a valuable tool for diagnosis of various diseases. However, reading retinal images is a difficult and time-consuming task even for experienced specialists…
Yoga-82: A New Dataset for Fine-grained Classification of Human Poses
Manisha Verma, Sudhakar Kumawat, Yuta Nakashima +1
Human pose estimation is a well-known problem in computer vision to locate joint positions. Existing datasets for the learning of poses are observed to be not challenging enough in…
Knowledge-Based Visual Question Answering in Videos
Noa Garcia, Mayu Otani, Chenhui Chu +1
We propose a novel video understanding task by fusing knowledge-based and video question answering. First, we introduce KnowIT VQA, a video dataset with 24,282 human-generated ques…
Historical and Modern Features for Buddha Statue Classification
Benjamin Renoust, Matheus Oliveira Franca, Jacob Chan +7
While Buddhism has spread along the Silk Roads, many pieces of art have been displaced. Only a few experts may identify these works, subjectively to their experience. The construct…
KnowIT VQA: Answering Knowledge-Based Questions about Videos
Noa Garcia, Mayu Otani, Chenhui Chu +1
We propose a novel video understanding task by fusing knowledge-based and video question answering. First, we introduce KnowIT VQA, a video dataset with 24,282 human-generated ques…
BUDA.ART: A Multimodal Content-Based Analysis and Retrieval System for Buddha Statues
Benjamin Renoust, Matheus Oliveira Franca, Jacob Chan +6
We introduce BUDA.ART, a system designed to assist researchers in Art History, to explore and analyze an archive of pictures of Buddha statues. The system combines different CBIR a…
Understanding Art through Multi-Modal Retrieval in Paintings
Noa Garcia, Benjamin Renoust, Yuta Nakashima
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, it…
Rethinking the Evaluation of Video Summaries
Mayu Otani, Yuta Nakashima, Esa Rahtu +1
Video summarization is a technique to create a short skim of the original video while preserving the main stories/content. There exists a substantial interest in automatizing this…
Context-Aware Embeddings for Automatic Art Analysis
Noa Garcia, Benjamin Renoust, Yuta Nakashima
Automatic art analysis aims to classify and retrieve artistic representations from a collection of images by using computer vision and machine learning techniques. In this work, we…
Representing a Partially Observed Non-Rigid 3D Human Using Eigen-Texture and Eigen-Deformation
Ryosuke Kimura, Akihiko Sayo, Fabian Lorenzo Dayrit +4
Reconstruction of the shape and motion of humans from RGB-D is a challenging problem, receiving much attention in recent years. Recent approaches for full-body reconstruction use a…
Summarization of User-Generated Sports Video by Using Deep Action Recognition Features
Antonio Tejero-de-Pablos, Yuta Nakashima, Tomokazu Sato +3
Automatically generating a summary of sports video poses the challenge of detecting interesting moments, or highlights, of a game. Traditional sports video summarization methods le…
Video Summarization using Deep Semantic Features
Mayu Otani, Yuta Nakashima, Esa Rahtu +2
This paper presents a video summarization technique for an Internet video to provide a quick way to overview its content. This is a challenging problem because finding important or…
Learning Joint Representations of Videos and Sentences with Web Image Search
Mayu Otani, Yuta Nakashima, Esa Rahtu +2
Our objective is video retrieval based on natural language queries. In addition, we consider the analogous problem of retrieving sentences or generating descriptions given an input…