Search Results for author:
Found 65 papers, 26 papers with code
Robust Latent Matters: Boosting Image Generation with Sampling Error
With the latent perturbation, we further propose (1) a novel tokenizer evaluation metric, i. e., pFID, which successfully correlates the tokenizer performance to generation quality and (2) a plug-and-play tokenizer training scheme, which significantly enhances the robustness of tokenizer thus boosting the generation quality and convergence speed.
Ranked #17 on
Image Generation
on ImageNet 256x256
Hierarchical Knowledge Graph Construction from Images for Scalable E-Commerce
For e-commerce, an efficient and low-cost automated knowledge graph construction method is the foundation of enabling various successful downstream applications.
Efficient Autoregressive Audio Modeling via Next-Scale Prediction
Audio generation has achieved remarkable progress with the advance of sophisticated generative models, such as diffusion models (DMs) and autoregressive (AR) models.
A Reference-Based 3D Semantic-Aware Framework for Accurate Local Facial Attribute Editing
Facial attribute editing plays a crucial role in synthesizing realistic faces with specific characteristics while maintaining realistic appearances.
RTGen: Generating Region-Text Pairs for Open-Vocabulary Object Detection
Open-vocabulary object detection (OVD) requires solid modeling of the region-semantic relationship, which could be learned from massive region-text pairs.
Ranked #12 on
Open Vocabulary Object Detection
on LVIS v1.0
(using extra training data)
Fairness in Visual Clustering: A Novel Transformer Clustering Approach
Promoting fairness for deep clustering models in unsupervised clustering settings to reduce demographic bias is a challenging goal.
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance.
Boosting Transductive Few-Shot Fine-Tuning With Margin-Based Uncertainty Weighting and Probability Regularization
This observation further motivates us to propose the Transductive Fine-tuning with Margin-based uncertainty weighting and Probability regularization (TF-MP), which learns a more balanced class marginal distribution.
Enhanced Training of Query-Based Object Detection via Selective Query Recollection
This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage.
Ranked #14 on
Object Detection
on COCO 2017 val
An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning
While standard SSL assumes uniform data distribution, we consider a more realistic and challenging setting called imbalanced SSL, where imbalanced class distributions occur in both labeled and unlabeled data.
Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition
We therefore introduce a new method named Attention-based Bijective Generative Adversarial Networks in a Distillation framework (DAB-GAN) to synthesize faces of a subject given his/her extracted face recognition features.
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets
Beyond classification, Conv-Adapter can generalize to detection and segmentation tasks with more than 50% reduction of parameters but comparable performance to the traditional full fine-tuning.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.
Powering Finetuning in Few-Shot Learning: Domain-Agnostic Bias Reduction with Selected Sampling
Class-agnostic bias is defined as the distribution shifting introduced by domain difference, which we propose Distribution Calibration Module(DCM) to reduce.
Unitail: Detecting, Reading, and Matching in Retail Scene
To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene.
Ranked #1 on
Dense Object Detection
on SKU-110K
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey
In this work, we provide a detailed review of recent and state-of-the-art research advances of deep reinforcement learning in computer vision.
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study
This work aims to empirically clarify a recently discovered perspective that label smoothing is incompatible with knowledge distillation.
Unsupervised Disentanglement of Linear-Encoded Facial Semantics
We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision.
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection
In this work, we investigate utilizing this semantic relation together with the visual information and introduce explicit relation reasoning into the learning of novel object detection.
Ranked #16 on
Few-Shot Object Detection
on MS-COCO (30-shot)
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration
In this paper, we focus on this more difficult scenario: learning networks where both weights and activations are binary, meanwhile, without any human annotated labels.
Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning
A common practice for this task is to train a model on the base set first and then transfer to novel classes through fine-tuning (Here fine-tuning procedure is defined as transferring knowledge from base to novel data, i. e. learning to transfer in few-shot scenario.)
Contrast and Order Representations for Video Self-Supervised Learning
To address this, we present a contrast-and-order representation (CORP) framework for learning self-supervised video representations that can automatically capture both the appearance information within each frame and temporal information across different frames.
Ranked #3 on
Self-Supervised Action Recognition Linear
on UCF101
Action Recognition
Self-Supervised Action Recognition Linear
+1
Improve Novel Class Generalization By Adaptive Feature Distribution for Few-Shot Learning
By addressing the difference between feature distributions of base and novel classes, we propose the adaptive feature distribution method which is to finetune one scale vector using the support set of novel classes.
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation
The first objective is performed by our proposed contextual brain tumor detection network, which plays a role of an attention gate and focuses on the region around brain tumor only while ignoring the far neighbor background which is less correlated to the tumor.
Online Ensemble Model Compression using Knowledge Distillation
This paper presents a novel knowledge distillation based model compression framework consisting of a student ensemble.
MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks
Our result can be regarded as a strong baseline using knowledge distillation, and to our best knowledge, this is also the first method that is able to boost vanilla ResNet-50 to surpass 80% on ImageNet without architecture modification or additional training data.
Ranked #11 on
Image Classification
on OmniBenchmark
Binarizing MobileNet via Evolution-based Searching
Inspired by one-shot architecture search frameworks, we manipulate the idea of group convolution to design efficient 1-Bit Convolutional Neural Networks (CNNs), assuming an approximately optimal trade-off between computational cost and model accuracy.
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification
In this paper, we propose Attentive CutMix, a naturally enhanced augmentation strategy based on CutMix.
Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning
This drawback hinders the model from learning subtle variance and fine-grained information.
ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions
In this paper, we propose several ideas for enhancing a binary network to close its accuracy gap from real-valued networks without incurring any additional computational cost.
Solving Missing-Annotation Object Detection with Background Recalibration Loss
In this paper, we introduce a superior solution called Background Recalibration Loss (BRL) that can automatically re-calibrate the loss signals according to the pre-defined IoU threshold and input image.
SAPD：Soft Anchor-Point Detector
In this work, we aim at finding a new balance of speed and accuracy for anchor-free detectors.
Soft Anchor-Point Object Detection
In this work, we boost the performance of the anchor-point detector over the key-point counterparts while maintaining the speed advantage.
Ranked #3 on
Dense Object Detection
on SKU-110K
Towards a Hypothesis on Visual Transformation based Self-Supervision
Overall, this paper aims to shed light on the phenomenon of visual transformation based self-supervision.
Learning Non-Parametric Invariances from Data with Permanent Random Connectomes
One of the fundamental problems in supervised classification and in machine learning in general, is the modelling of non-parametric invariances that exist in data.
SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses
Unsupervised domain adaptive object detection aims to learn a robust detector in the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissimilar or even totally different.
Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement
In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously.
MoBiNet: A Mobile Binary Network for Image Classification
MobileNet and Binary Neural Networks are two among the most widely used techniques to construct deep learning models for performing a variety of tasks on mobile and embedded platforms. In this paper, we present a simple yet efficient scheme to exploit MobileNet binarization at activation function and model weights.
Proximal Splitting Networks for Image Restoration
This is in contrast to the common practice in literature of having the prior to be fixed and fully instantiated even during training stages.
Feature Selective Anchor-Free Module for Single-Shot Object Detection
The general concept of the FSAF module is online feature selection applied to the training of multi-level anchor-free branches.
Ranked #140 on
Object Detection
on COCO test-dev
RankGAN: A Maximum Margin Ranking GAN for Generating Faces
We present a new stage-wise learning paradigm for training generative adversarial networks (GANs).
Deep Recurrent Level Set for Segmenting Brain Tumors
In each step, the Convolutional Layer is fed with the LevelSet map to obtain a brain tumor feature map.
Bounding Box Regression with Uncertainty for Accurate Object Detection
Large-scale object detection datasets (e. g., MS-COCO) try to define the ground truth bounding boxes as clear as possible.
Ranked #21 on
Object Detection
on PASCAL VOC 2007
Perturbative Neural Networks
Convolutional neural networks are witnessing wide adoption in computer vision systems with numerous applications across a range of visual recognition tasks.
Ring loss: Convex Feature Normalization for Face Recognition
We motivate and present Ring loss, a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as Softmax.
Seeing Small Faces from Robust Anchor's Perspective
This paper introduces a novel anchor design to support anchor-based face detection for superior scale-invariant performance, especially on tiny faces.
Non-Parametric Transformation Networks
In this paper, we introduce a new class of deep convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn \textit{general} invariances and symmetries directly from data.
Improving Object Detection from Scratch via Gated Feature Reuse
In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD when learning from scratch (i. e., without pre-trained models).
Max-Margin Invariant Features from Transformed Unlabelled Data
The study of representations invariant to common transformations of the data is important to learning.
Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning
The proposed model is experimented in both tasks of face aging synthesis and cross-age face verification.
Class Correlation affects Single Object Localization using Pre-trained ConvNets
The problem of object localization has become one of the mainstream problems of vision.
Max-Margin Invariant Features from Transformed Unlabeled Data
The study of representations invariant to common transformations of the data is important to learning.
Faster Than Real-time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses
We present our novel approach to simultaneously extract the 3D shape of the face and the semantically consistent 2D alignment through a 3D Spatial Transformer Network (3DSTN) to model both the camera projection matrix and the warping parameters of a 3D model.
Ranked #11 on
Face Alignment
on AFLW2000-3D
Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking
A recent advance called the WGAN based on Wasserstein distance can improve on the KL and JS-divergence based GANs, and alleviate the gradient vanishing, instability, and mode collapse issues that are common in the GAN training.
Deep Contextual Recurrent Residual Networks for Scene Labeling
Designed as extremely deep architectures, deep residual networks which provide a rich visual representation and offer robust convergence behaviors have recently achieved exceptional performance in numerous computer vision problems.
Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation
To address these issues and boost the classic variational LS methods to a new level of the learnable deep learning approaches, we propose a novel definition of contour evolution named Recurrent Level Set (RLS)} to employ Gated Recurrent Unit under the energy minimization of a variational LS functional.
Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition
Modeling the long-term facial aging process is extremely challenging due to the presence of large and non-linear variations during the face development stages.
How ConvNets model Non-linear Transformations
In this paper, we theoretically address three fundamental problems involving deep convolutional networks regarding invariance, depth and hierarchy.
Emergence of Selective Invariance in Hierarchical Feed Forward Networks
We illustrate the general notion of selective invari- ance through object categorization experiments on large- scale datasets such as SVHN and ILSVRC 2012.
Towards a Deep Learning Framework for Unconstrained Face Detection
Robust face detection is one of the most important pre-processing steps to support facial expression analysis, facial landmarking, face recognition, pose estimation, building of 3D facial models, etc.
Local Binary Convolutional Neural Networks
We propose local binary convolution (LBC), an efficient alternative to convolutional layers in standard convolutional neural networks (CNN).
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
Robust face detection in the wild is one of the ultimate components to support various facial related problems, i. e. unconstrained face recognition, facial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc.
Ranked #29 on
Face Detection
on WIDER Face (Medium)
Discriminative Invariant Kernel Features: A Bells-and-Whistles-Free Approach to Unsupervised Face Recognition and Pose Estimation
We propose an explicitly discriminative and `simple' approach to generate invariance to nuisance transformations modeled as unitary.
Unitary-Group Invariant Kernels and Features from Transformed Unlabeled Data
The study of representations invariant to common transformations of the data is important to learning.
