Search Results for author:
Found 31 papers, 19 papers with code
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis
Addressing the limitations of text as a source of accurate layout representation in text-conditional diffusion models, many works incorporate additional signals to condition certain attributes within a generated image.
Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding
In the weakly supervised temporal video grounding study, previous methods use predetermined single Gaussian proposals which lack the ability to express diverse events described by the sentence query.
Topic-VQ-VAE: Leveraging Latent Codebooks for Flexible Topic-Guided Document Generation
This paper introduces a novel approach for topic modeling utilizing latent codebooks from Vector-Quantized Variational Auto-Encoder~(VQ-VAE), discretely encapsulating the rich information of the pre-trained embeddings such as the pre-trained language model.
GeNAS: Neural Architecture Search with Better Generalization
Recent neural architecture search (NAS) approaches rely on validation loss or accuracy to find the superior network for the target data.
EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection
Based on our observation, we propose to employ ResNet with a highly reduced channel, which surprisingly allows high efficiency compared to other mobile-friendly networks (e. g., MobileNetV1, V2, V3).
Learning Features with Parameter-Free Layers
We aim to break the stereotype of organizing the spatial operations of building blocks into trainable layers.
Observations on K-image Expansion of Image-Mixing Augmentation for Classification
Image-mixing augmentations (e. g., Mixup and CutMix), which typically involve mixing two images, have become the de-facto training techniques for image classification.
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement
This semantic drift occurs confusion between background and instance in training and consequently degrades the segmentation performance.
Image-level Supervised Instance Segmentation
Point-Supervised Instance Segmentation
+5
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations
We propose a novel and effective purification based adversarial defense method against pre-processor blind white- and black-box attacks.
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
While the recent CISS algorithms utilize variants of the knowledge distillation (KD) technique to tackle the problem, they failed to fully address the critical challenges in CISS causing the catastrophic forgetting; the semantic drift of the background class and the multi-label prediction issue.
Ranked #1 on
Disjoint 15-5
on PASCAL VOC 2012
Rainbow Memory: Continual Learning with a Memory of Diverse Samples
Prevalent scenario of continual learning, however, assumes disjoint sets of classes as tasks and is less realistic rather artificial.
More than just an auxiliary loss: Anti-spoofing Backbone Training via Adversarial Pseudo-depth Generation
In this paper, a new method of training pipeline is discussed to achieve significant performance on the task of anti-spoofing with RGB image.
Rethinking Channel Dimensions for Efficient Model Design
We then investigate the channel configuration of a model by searching network architectures concerning the channel configuration under the computational cost restriction.
Ranked #293 on
Image Classification
on ImageNet
(using extra training data)
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples
The cost of annotating transcriptions for large speech corpora becomes a bottleneck to maximally enjoy the potential capacity of deep neural network-based automatic speech recognition models.
FrostNet: Towards Quantization-Aware Network Architecture Search
In this paper, we present a new network architecture search (NAS) procedure to find a network that guarantees both full-precision (FLOAT32) and quantized (INT8) performances.
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods
Despite apparent human-level performances of deep neural networks (DNN), they behave fundamentally differently from humans.
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
To solve the first problem, we introduce the new extremely lightweight portrait segmentation model SINet, containing an information blocking decoder and spatial squeeze modules.
Ranked #1 on
Portrait Segmentation
on EG1800
Neural Approximation of an Auto-Regressive Process through Confidence Guided Sampling
We first assume that the priors of future samples can be generated in an independently and identically distributed (i. i. d.)
Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold
(iii) The proposed regression is embedded into a generative model, and the whole procedure is developed by the variational autoencoder framework.
ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modules
In our qualitative and quantitative analysis on the EG1800 dataset, we show that our method outperforms various existing lightweight segmentation models.
EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse
In this paper, we propose a new multi-scale face detector having an extremely tiny number of parameters (EXTD), less than 0. 1 million, as well as achieving comparable performance to deep heavy detectors.
Ranked #23 on
Face Detection
on WIDER Face (Hard)
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation
To resolve this problem, we propose a new block called Concentrated-Comprehensive Convolution (C3) which applies the asymmetric convolutions before the depth-wise separable dilated convolution to compensate for the information loss due to dilated convolution.
MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis
This block enables MC-GAN to generate a realistic object image with the desired background by controlling the amount of the background information from the given base image using the foreground information from the text attributes.
Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation
In addition to this performance enhancement problem, we show that the proposed PGN can be adopted to solve the classical adversarial problem without utilizing the information on the target classifier.
Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams
In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way.
BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning
Our method learns hundreds to thousand times faster than the conventional methods by learning only a handful of core cluster information, which shows that deep RL agents can effectively learn through the shared knowledge from other agents.
Action-Decision Networks for Visual Tracking With Deep Reinforcement Learning
In contrast to the existing trackers using deep networks, the proposed tracker is designed to achieve a light computation as well as satisfactory tracking accuracy in both location and scale.
Superpixel-based Semantic Segmentation Trained by Statistical Process Control
Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks.
Visual Path Prediction in Complex Scenes With Crowded Moving Objects
In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects.
