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Found 49 papers, 25 papers with code
Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding
Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science.
TNF: Tri-branch Neural Fusion for Multimodal Medical Data Classification
This paper presents a Tri-branch Neural Fusion (TNF) approach designed for classifying multimodal medical images and tabular data.
Unsupervised LLM Adaptation for Question Answering
In this task, we leverage a pre-trained LLM, a publicly available QA dataset (source data), and unlabeled documents from the target domain.
A Transformer Model for Symbolic Regression towards Scientific Discovery
Once trained, we apply our best model to the SRSD datasets (Symbolic Regression for Scientific Discovery datasets) which yields state-of-the-art results using the normalized tree-based edit distance, at no extra computational cost.
Exo2EgoDVC: Dense Video Captioning of Egocentric Procedural Activities Using Web Instructional Videos
We propose a novel benchmark for cross-view knowledge transfer of dense video captioning, adapting models from web instructional videos with exocentric views to an egocentric view.
Vision-Language Interpreter for Robot Task Planning
By generating PDs from language instruction and scene observation, we can drive symbolic planners in a language-guided framework.
WeaveNet for Approximating Two-sided Matching Problems
Matching, a task to optimally assign limited resources under constraints, is a fundamental technology for society.
A Critical Look at the Current Usage of Foundation Model for Dense Recognition Task
In recent years large model trained on huge amount of cross-modality data, which is usually be termed as foundation model, achieves conspicuous accomplishment in many fields, such as image recognition and generation.
Noisy Universal Domain Adaptation via Divergence Optimization for Visual Recognition
To transfer the knowledge learned from a labeled source domain to an unlabeled target domain, many studies have worked on universal domain adaptation (UniDA), where there is no constraint on the label sets of the source domain and target domain.
Neural Structure Fields with Application to Crystal Structure Autoencoders
We propose neural structure fields (NeSF) as an accurate and practical approach for representing crystal structures using neural networks.
SRSD: Rethinking Datasets of Symbolic Regression for Scientific Discovery
Symbolic Regression (SR) is a task of recovering mathematical expressions from given data and has been attracting attention from the research community to discuss its potential for scientific discovery.
Recipe Generation from Unsegmented Cooking Videos
However, unlike DVC, in recipe generation, recipe story awareness is crucial, and a model should extract an appropriate number of events in the correct order and generate accurate sentences based on them.
Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows
We present a new multimodal dataset called Visual Recipe Flow, which enables us to learn each cooking action result in a recipe text.
Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
For each of the 120 SRSD datasets, we carefully review the properties of the formula and its variables to design reasonably realistic sampling ranges of values so that our new SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method can (re)discover physical laws from such datasets.
3D Point Cloud Registration with Learning-based Matching Algorithm
Instead of only optimizing the feature extractor for a matching algorithm, we propose a learning-based matching module optimized to the jointly-trained feature extractor.
WeaveNet: A Differentiable Solver for Non-linear Assignment Problems
Assignment, a task to match a limited number of elements, is a fundamental problem in informatics.
Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation
We validated our method on domain adaptation of hand segmentation from real and simulation images.
WeaveNet for Approximating Assignment Problems
Assignment, a task to match a limited number of elements, is a fundamental problem in informatics.
Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning
Unsupervised image captioning is a challenging task that aims at generating captions without the supervision of image-sentence pairs, but only with images and sentences drawn from different sources and object labels detected from the images.
Divergence Optimization for Noisy Universal Domain Adaptation
Hence, we consider a new realistic setting called Noisy UniDA, in which classifiers are trained with noisy labeled data from the source domain and unlabeled data with an unknown class distribution from the target domain.
Visual Grounding Annotation of Recipe Flow Graph
Visual grounding is provided as bounding boxes to image sequences of recipes, and each bounding box is linked to an element of the workflow.
Crowd Density Forecasting by Modeling Patch-based Dynamics
To address this task, we have developed the patch-based density forecasting network (PDFN), which enables forecasting over a sequence of crowd density maps describing how crowded each location is in each video frame.
Decentralized Learning of Generative Adversarial Networks from Non-iid Data
This work addresses a new problem that learns generative adversarial networks (GANs) from multiple data collections that are each i) owned separately by different clients and ii) drawn from a non-identical distribution that comprises different classes.
Pose Graph Optimization for Unsupervised Monocular Visual Odometry
In this paper, we propose to leverage graph optimization and loop closure detection to overcome limitations of unsupervised learning based monocular visual odometry.
Strong-Weak Distribution Alignment for Adaptive Object Detection
This motivates us to propose a novel method for detector adaptation based on strong local alignment and weak global alignment.
Ranked #2 on
Unsupervised Domain Adaptation
on SIM10K to BDD100K
Multichannel Semantic Segmentation with Unsupervised Domain Adaptation
The other is a multitask learning approach that uses depth images as outputs.
Conditional Video Generation Using Action-Appearance Captions
The results demonstrate that CFT-GAN is able to successfully generate videos containing the action and appearances indicated in the captions.
Generating Easy-to-Understand Referring Expressions for Target Identifications
Moreover, we regard that sentences that are easily understood are those that are comprehended correctly and quickly by humans.
Class-Distinct and Class-Mutual Image Generation with GANs
To overcome this limitation, we address a novel problem called class-distinct and class-mutual image generation, in which the goal is to construct a generator that can capture between-class relationships and generate an image selectively conditioned on the class specificity.
Label-Noise Robust Generative Adversarial Networks
To remedy this, we propose a novel family of GANs called label-noise robust GANs (rGANs), which, by incorporating a noise transition model, can learn a clean label conditional generative distribution even when training labels are noisy.
Visual Question Generation for Class Acquisition of Unknown Objects
In this paper, we propose a method for generating questions about unknown objects in an image, as means to get information about classes that have not been learned.
Customized Image Narrative Generation via Interactive Visual Question Generation and Answering
Image description task has been invariably examined in a static manner with qualitative presumptions held to be universally applicable, regardless of the scope or target of the description.
Open Set Domain Adaptation by Backpropagation
Almost all of them are proposed for a closed-set scenario, where the source and the target domain completely share the class of their samples.
Viewpoint-aware Video Summarization
To satisfy these requirements (A)-(C) simultaneously, we proposed a novel video summarization method from multiple groups of videos.
MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes
We benchmarked our method by creating an RGB-Thermal dataset in which thermal and RGB images are combined.
Ranked #5 on
Thermal Image Segmentation
on KP day-night
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries.
Ranked #3 on
Domain Adaptation
on HMDBfull-to-UCF
Image Classification
Multi-Source Unsupervised Domain Adaptation
+2
Between-class Learning for Image Classification
Second, we propose a mixing method that treats the images as waveforms, which leads to a further improvement in performance.
Learning from Between-class Examples for Deep Sound Recognition
Deep learning methods have achieved high performance in sound recognition tasks.
Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture
FlowGAN generates optical flow, which contains only the edge and motion of the videos to be begerated.
Neural 3D Mesh Renderer
Using this renderer, we perform single-image 3D mesh reconstruction with silhouette image supervision and our system outperforms the existing voxel-based approach.
Ranked #6 on
3D Object Reconstruction
on Data3D−R2N2
(Avg F1 metric)
Adversarial Dropout Regularization
However, a drawback of this approach is that the critic simply labels the generated features as in-domain or not, without considering the boundaries between classes.
Ranked #2 on
Synthetic-to-Real Translation
on Syn2Real-C
Melody Generation for Pop Music via Word Representation of Musical Properties
Automatic melody generation for pop music has been a long-time aspiration for both AI researchers and musicians.
Sound Multimedia Audio and Speech Processing
Spatio-temporal Person Retrieval via Natural Language Queries
In this paper, we address the problem of spatio-temporal person retrieval from multiple videos using a natural language query, in which we output a tube (i. e., a sequence of bounding boxes) which encloses the person described by the query.
Asymmetric Tri-training for Unsupervised Domain Adaptation
Deep-layered models trained on a large number of labeled samples boost the accuracy of many tasks.
Ranked #5 on
Sentiment Analysis
on Multi-Domain Sentiment Dataset
DeMIAN: Deep Modality Invariant Adversarial Network
To obtain the common representations under such a situation, we propose to make the distributions over different modalities similar in the learned representations, namely modality-invariant representations.
The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)
Visual Question Answering (VQA) task has showcased a new stage of interaction between language and vision, two of the most pivotal components of artificial intelligence.
DualNet: Domain-Invariant Network for Visual Question Answering
Visual question answering (VQA) task not only bridges the gap between images and language, but also requires that specific contents within the image are understood as indicated by linguistic context of the question, in order to generate the accurate answers.
Common Subspace for Model and Similarity: Phrase Learning for Caption Generation From Images
In order to overcome the shortage of training samples, CoSMoS obtains a subspace in which (a) all feature vectors associated with the same phrase are mapped as mutually close, (b) classifiers for each phrase are learned, and (c) training samples are shared among co-occurring phrases.
Three Guidelines of Online Learning for Large-Scale Visual Recognition
In this paper, we would like to evaluate online learning algorithms for large-scale visual recognition using state-of-the-art features which are preselected and held fixed.
