research
∙
08/22/2023
Domain Generalization via Rationale Invariance
This paper offers a new perspective to ease the challenge of domain gene...
share
research
∙
06/28/2023
You Can Generate It Again: Data-to-text Generation with Verification and Correction Prompting
Despite significant advancements in existing models, generating text des...
research
∙
05/29/2023
Learning Conditional Attributes for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to train models to recogniz...
research
∙
05/23/2023
Out-of-Distribution Generalization in Text Classification: Past, Present, and Future
Machine learning (ML) systems in natural language processing (NLP) face ...
research
∙
05/22/2023
The CLIP Model is Secretly an Image-to-Prompt Converter
The Stable Diffusion model is a prominent text-to-image generation model...
research
∙
04/10/2023
Improved Test-Time Adaptation for Domain Generalization
The main challenge in domain generalization (DG) is to handle the distri...
research
∙
03/17/2023
Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation
Autoencoding, which aims to reconstruct the input images through a bottl...
research
∙
03/03/2023
Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems
Self-supervised learning (SSL) strategies have demonstrated remarkable p...
research
∙
12/05/2022
Generalizable Person Re-Identification via Viewpoint Alignment and Fusion
In the current person Re-identification (ReID) methods, most domain gene...
research
∙
10/10/2022
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
Semi-supervised semantic segmentation requires the model to effectively ...
research
∙
09/29/2022
Regularizing Neural Network Training via Identity-wise Discriminative Feature Suppression
It is well-known that a deep neural network has a strong fitting capabil...
research
∙
08/20/2022
Pretrained Language Encoders are Natural Tagging Frameworks for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) aims to extract the spans of ...
research
∙
08/18/2022
Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization
Open-world instance segmentation (OWIS) aims to segment class-agnostic i...
research
∙
08/01/2022
Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism
The challenge of fine-grained visual recognition often lies in discoveri...
research
∙
07/19/2022
Don't Stop Learning: Towards Continual Learning for the CLIP Model
The Contrastive Language-Image Pre-training (CLIP) Model is a recently p...
research
∙
07/06/2022
Two-stage Decision Improves Open-Set Panoptic Segmentation
Open-set panoptic segmentation (OPS) problem is a new research direction...
research
∙
03/23/2022
Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
Recent studies in deepfake detection have yielded promising results when...
research
∙
01/06/2022
Multi-Domain Joint Training for Person Re-Identification
Deep learning-based person Re-IDentification (ReID) often requires a lar...
research
∙
08/05/2021
Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation
Semantic segmentation is a crucial image understanding task, where each ...
research
∙
05/22/2021
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
Weakly-supervised anomaly detection aims at learning an anomaly detector...
research
∙
04/30/2021
Center Prediction Loss for Re-identification
The training loss function that enforces certain training sample distrib...
research
∙
03/09/2021
Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identification
Learning cross-view consistent feature representation is the key for acc...
research
∙
10/26/2020
Where to Look and How to Describe: Fashion Image Retrieval with an Attentional Heterogeneous Bilinear Network
Fashion products typically feature in compositions of a variety of style...
research
∙
10/12/2020
Dynamic Memory Enhanced Transformer for End-to-end Task-Oriented Dialogue System
Recent studies try to build task-oriented dialogue system in an end-to-e...
research
∙
07/07/2020
Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks
Most existing crowd counting systems rely on the availability of the obj...
research
∙
02/29/2020
Towards Using Count-level Weak Supervision for Crowd Counting
Most existing crowd counting methods require object location-level annot...
research
∙
02/11/2020
Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
Hyperspectral image(HSI) classification has been improved with convoluti...
research
∙
01/09/2020
Semi-supervised Learning via Conditional Rotation Angle Estimation
Self-supervised learning (SlfSL), aiming at learning feature representat...
research
∙
12/10/2019
To Balance or Not to Balance: An Embarrassingly Simple Approach for Learning with Long-Tailed Distributions
Real-world visual data often exhibits a long-tailed distribution, where ...
research
∙
11/15/2019
DNNRE: A Dynamic Neural Network for Distant Supervised Relation Extraction
Distant Supervised Relation Extraction (DSRE) is usually formulated as a...
research
∙
11/12/2019
Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction
Charge prediction, determining charges for criminal cases by analyzing t...
research
∙
09/28/2019
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation
This paper tackles the problem of video object segmentation. We are spec...
research
∙
09/22/2019
Structured Binary Neural Networks for Image Recognition
We propose methods to train convolutional neural networks (CNNs) with bo...
research
∙
08/19/2019
In defense of OSVOS
As a milestone for video object segmentation, one-shot video object segm...
research
∙
08/10/2019
Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations
This paper tackles the problem of training a deep convolutional neural n...
research
∙
07/29/2019
V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices
One of the primary challenges faced by deep learning is the degree to wh...
research
∙
04/04/2019
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection
Deep autoencoder has been extensively used for anomaly detection. Traini...
research
∙
03/27/2019
Training Quantized Network with Auxiliary Gradient Module
In this paper, we seek to tackle two challenges in training low-precisio...
research
∙
03/01/2019
A Sketch Based 3D Shape Retrieval Approach Based on Efficient Deep Point-to-Subspace Metric Learning
One key issue in managing a large scale 3D shape dataset is to identify ...
research
∙
01/22/2019
RPC: A Large-Scale Retail Product Checkout Dataset
Over recent years, emerging interest has occurred in integrating compute...
research
∙
01/12/2019
Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
As the post-processing step for object detection, non-maximum suppressio...
research
∙
12/11/2018
Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification
Vehicle re-identification is an important problem and becomes desirable ...
research
∙
11/22/2018
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation
In this paper, we propose to train convolutional neural networks (CNNs) ...
research
∙
11/22/2018
Rethinking Binary Neural Network for Accurate Image Classification and Semantic Segmentation
In this paper, we propose to train a network with both binary weights an...
research
∙
08/30/2018
Towards Effective Deep Embedding for Zero-Shot Learning
Zero-shot learning (ZSL) attempts to recognize visual samples of unseen ...
research
∙
06/05/2018
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network
Deep neural networks have achieved remarkable success in single image su...
research
∙
05/11/2018
Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples
Humans are capable of learning a new fine-grained concept with very litt...
research
∙
11/01/2017
Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization
Landmark/pose estimation in single monocular images have received much e...
research
∙
11/01/2017
Towards Effective Low-bitwidth Convolutional Neural Networks
This paper tackles the problem of training a deep convolutional neural n...
research
∙
07/18/2017