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Found 151 papers, 59 papers with code
dS^2LBI: Exploring Structural Sparsity on Deep Network via Differential Inclusion Paths
Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error.
fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding
The exploration of brain activity and its decoding from fMRI data has been a longstanding pursuit, driven by its potential applications in brain-computer interfaces, medical diagnostics, and virtual reality.
The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation
To accommodate these scenarios, we consider a new setting dubbed as multiple treatments and multiple outcomes.
Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation
Furthermore, we propose a multi-view fusion layer based temporal module which is equipped with a set of object slots and interacts with features from different views by attention mechanism to fulfill sufficient object representation completion.
Doubly Robust Proximal Causal Learning for Continuous Treatments
To address these challenges, we propose a kernel-based DR estimator that can well handle continuous treatments.
Unsupervised Open-Vocabulary Object Localization in Videos
In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video object localization.
Object-Centric Multiple Object Tracking
Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing the annotation burden of multiple-object tracking (MOT) pipelines.
Coarse-to-Fine Amodal Segmentation with Shape Prior
To address this issue, we propose a convolution refine module to inject fine-grained information and provide a more precise amodal object segmentation based on visual features and coarse-predicted segmentation.
WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model
The target instruction is then forwarded to a visual grounding system for object pose and size estimation, following which the robot grasps the object accordingly.
Rethinking Person Re-identification from a Projection-on-Prototypes Perspective
In this paper, we rethink the role of the classifier in person Re-ID, and advocate a new perspective to conceive the classifier as a projection from image features to class prototypes.
Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
Images with similar so-called fine-grained attributes (e. g., clothes and viewpoints) are encouraged to cluster together.
Local Consensus Enhanced Siamese Network with Reciprocal Loss for Two-view Correspondence Learning
First, we propose a Local Feature Consensus (LFC) plugin block to augment the features of existing models.
Pushing the Limits of 3D Shape Generation at Scale
Through the adaptation of the Auto-Regressive model and the utilization of large language models, we have developed a remarkable model with an astounding 3. 6 billion trainable parameters, establishing it as the largest 3D shape generation model to date, named Argus-3D.
GeoVLN: Learning Geometry-Enhanced Visual Representation with Slot Attention for Vision-and-Language Navigation
Technically, we introduce a two-stage module that combine local slot attention and CLIP model to produce geometry-enhanced representation from such input.
Harnessing Text-to-Image Attention Prior for Reference-based Multi-view Image Synthesis
In recent years, Text-to-Image (T2I) generative models have gained attention in various domains.
Faster OreFSDet : A Lightweight and Effective Few-shot Object Detector for Ore Images
For the ore particle size detection, obtaining a sizable amount of high-quality ore labeled data is time-consuming and expensive.
Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions
Most existing point cloud upsampling methods have roughly three steps: feature extraction, feature expansion and 3D coordinate prediction.
Learning Versatile 3D Shape Generation with Improved AR Models
Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space.
Joint fMRI Decoding and Encoding with Latent Embedding Alignment
Our Latent Embedding Alignment (LEA) model concurrently recovers visual stimuli from fMRI signals and predicts brain activity from images within a unified framework.
Rethinking Optical Flow from Geometric Matching Consistent Perspective
In this paper, we propose a rethinking to previous optical flow estimation.
Rethinking the Multi-view Stereo from the Perspective of Rendering-based Augmentation
To address these problems, we first apply one of the state-of-the-art learning-based MVS methods, --MVSFormer, to overcome intractable scenarios such as textureless and reflections regions suffered by traditional PatchMatch methods, but it fails in a few large scenes' reconstructions.
Improving Transformer-based Image Matching by Cascaded Capturing Spatially Informative Keypoints
Learning robust local image feature matching is a fundamental low-level vision task, which has been widely explored in the past few years.
Entity-Level Text-Guided Image Manipulation
In the image manipulation phase, SeMani adopts a generative model to synthesize new images conditioned on the entity-irrelevant regions and target text descriptions.
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
Thus, inspired by vanilla adversarial learning, a novel model-agnostic meta Style Adversarial training (StyleAdv) method together with a novel style adversarial attack method is proposed for CD-FSL.
Exploring Efficient Few-shot Adaptation for Vision Transformers
Essentially, despite ViTs have been shown to enjoy comparable or even better performance on other vision tasks, it is still very nontrivial to efficiently finetune the ViTs in real-world FSL scenarios.
Vocabulary-informed Zero-shot and Open-set Learning
Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels.
Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels
While Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline, we further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data.
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction
Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction.
Learning Versatile 3D Shape Generation with Improved Auto-regressive Models
Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space.
Split-PU: Hardness-aware Training Strategy for Positive-Unlabeled Learning
Since only part of the most confident positive samples are available and evidence is not enough to categorize the rest samples, many of these unlabeled data may also be the positive samples.
PatchMix Augmentation to Identify Causal Features in Few-shot Learning
We theoretically show that such an augmentation mechanism, different from existing ones, is able to identify the causal features.
RankDNN: Learning to Rank for Few-shot Learning
This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification.
Self-supervised Amodal Video Object Segmentation
The key intuition is that the occluded part of an object can be explained away if that part is visible in other frames, possibly deformed as long as the deformation can be reasonably learned.
ZITS++: Image Inpainting by Improving the Incremental Transformer on Structural Priors
Specifically, given one corrupt image, we present the Transformer Structure Restorer (TSR) module to restore holistic structural priors at low image resolution, which are further upsampled by Simple Structure Upsampler (SSU) module to higher image resolution.
ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning
Concretely, to solve the data imbalance problem between the source data with sufficient examples and the auxiliary target data with limited examples, we build our model under the umbrella of multi-expert learning.
Specialized Re-Ranking: A Novel Retrieval-Verification Framework for Cloth Changing Person Re-Identification
Cloth changing person re-identification(Re-ID) can work under more complicated scenarios with higher security than normal Re-ID and biometric techniques and is therefore extremely valuable in applications.
LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human Modeling
Recent progress in 4D implicit representation focuses on globally controlling the shape and motion with low dimensional latent vectors, which is prone to missing surface details and accumulating tracking error.
MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based Depth
In this paper, we propose a pre-trained ViT enhanced MVS network called MVSFormer, which can learn more reliable feature representations benefited by informative priors from ViT.
Ranked #1 on
3D Reconstruction
on DTU
Learning Prior Feature and Attention Enhanced Image Inpainting
To this end, this paper incorporates the pre-training based Masked AutoEncoder (MAE) into the inpainting model, which enjoys richer informative priors to enhance the inpainting process.
Vision Transformers: From Semantic Segmentation to Dense Prediction
In this work, for the first time we explore the global context learning potentials of ViTs for dense visual prediction (e. g., semantic segmentation).
RCLane: Relay Chain Prediction for Lane Detection
This is because most of the existing lane detection methods either treat the lane detection as a dense prediction or a detection task, few of them consider the unique topologies (Y-shape, Fork-shape, nearly horizontal lane) of the lane markers, which leads to sub-optimal solution.
A Simple Test-Time Method for Out-of-Distribution Detection
In contrast, this paper proposes a simple Test-time Linear Training (ETLT) method for OOD detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Local Slot Attention for Vision-and-Language Navigation
Since the attention mechanism in the transformer architecture can better integrate inter- and intra-modal information of vision and language.
Wavelet Prior Attention Learning in Axial Inpainting Network
Image inpainting is the task of filling masked or unknown regions of an image with visually realistic contents, which has been remarkably improved by Deep Neural Networks (DNNs) recently.
I Know What You Draw: Learning Grasp Detection Conditioned on a Few Freehand Sketches
The physical robot experiments confirm the utility of our method in object-cluttered scenes.
Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions
This paper studies the task of any objects grasping from the known categories by free-form language instructions.
ONCE-3DLanes: Building Monocular 3D Lane Detection
We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space.
Density-preserving Deep Point Cloud Compression
To address this, we propose a novel deep point cloud compression method that preserves local density information.
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition
One-shot fine-grained visual recognition often suffers from the problem of having few training examples for new fine-grained classes.
Fine-Grained Image Classification
Fine-Grained Visual Recognition
+3
Pixel2Mesh++: 3D Mesh Generation and Refinement from Multi-View Images
We study the problem of shape generation in 3D mesh representation from a small number of color images with or without camera poses.
ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and Generation
Existing text-guided image manipulation methods aim to modify the appearance of the image or to edit a few objects in a virtual or simple scenario, which is far from practical application.
DST: Dynamic Substitute Training for Data-free Black-box Attack
With the wide applications of deep neural network models in various computer vision tasks, more and more works study the model vulnerability to adversarial examples.
ImpDet: Exploring Implicit Fields for 3D Object Detection
Conventional 3D object detection approaches concentrate on bounding boxes representation learning with several parameters, i. e., localization, dimension, and orientation.
A Framework of Meta Functional Learning for Regularising Knowledge Transfer
The MFL computes meta-knowledge on functional regularisation generalisable to different learning tasks by which functional training on limited labelled data promotes more discriminative functions to be learned.
Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison
To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes.
QS-Craft: Learning to Quantize, Scrabble and Craft for Conditional Human Motion Animation
To this end, this paper proposes a novel model of learning to Quantize, Scrabble, and Craft (QS-Craft) for conditional human motion animation.
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning
The key challenge of CD-FSL lies in the huge data shift between source and target domains, which is typically in the form of totally different visual styles.
Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels
Noisy training set usually leads to the degradation of generalization and robustness of neural networks.
H4D: Human 4D Modeling by Learning Neural Compositional Representation
A simple yet effective linear motion model is proposed to provide a rough and regularized motion estimation, followed by per-frame compensation for pose and geometry details with the residual encoded in the auxiliary code.
Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding
The proposed model restores holistic image structures with a powerful attention-based transformer model in a fixed low-resolution sketch space.
Clustering by the Probability Distributions from Extreme Value Theory
In contrast, the GPD fits the distribution of distance to the centroid exceeding a sufficiently large threshold, leading to a more stable performance of GPD k-means.
Learning To Memorize Feature Hallucination for One-Shot Image Generation
In this paper, we propose a novel model to explicitly learn and memorize reusable features that can help hallucinate novel category images.
Ranking Distance Calibration for Cross-Domain Few-Shot Learning
The calibrated distance in this target-aware non-linear subspace is complementary to that in the pre-trained representation.
FEDERATED LEARNING FRAMEWORK BASED ON TRIMMED MEAN AGGREGATION RULES
Typically, aggregation rules are utilized to protect the model from the attacks in federated learning.
An Improved Composite Functional Gradient Learning by Wasserstein Regularization for Generative adversarial networks
However, the discriminator using logistic regression from the CFG framework is gradually hard to discriminate between real and fake images while the training steps go on.
Relative Instance Credibility Inference for Learning with Noisy Labels
Specifically, we re-purpose a sparse linear model with incidental parameters as a unified Relative Instance Credibility Inference (RICI) framework, which will detect and remove outliers in the forward pass of each mini-batch and use the remaining instances to train the network.
The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations
The image-level performance of COVID-19 prescreening model in the China-Spain multicenter study achieved an AUC of 0. 913 (95% CI, 0. 898-0. 927), with a sensitivity of 0. 695 (95% CI, 0. 643-0. 748), a specificity of 0. 904 (95% CI, 0. 891 -0. 919), an accuracy of 0. 875(0. 861-0. 889), and a F1 of 0. 611(0. 568-0. 655).
Deep Hybrid Self-Prior for Full 3D Mesh Generation
We present a deep learning pipeline that leverages network self-prior to recover a full 3D model consisting of both a triangular mesh and a texture map from the colored 3D point cloud.
A Unified Efficient Pyramid Transformer for Semantic Segmentation
Semantic segmentation is a challenging problem due to difficulties in modeling context in complex scenes and class confusions along boundaries.
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data
Secondly, a novel disentangle module together with a domain classifier is proposed to extract the disentangled domain-irrelevant and domain-specific features.
Can Action be Imitated? Learn to Reconstruct and Transfer Human Dynamics from Videos
To achieve this, a novel Mesh-based Video Action Imitation (M-VAI) method is proposed by us.
SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation
The concatenation of the observed point cloud and symmetric one reconstructs a coarse object shape, thus facilitating object center (3D translation) and 3D size estimation.
Rapid COVID-19 Risk Screening by Eye-region Manifestations
It is still nontrivial to develop a new fast COVID-19 screening method with the easier access and lower cost, due to the technical and cost limitations of the current testing methods in the medical resource-poor districts.
The Image Local Autoregressive Transformer
To address these limitations, we propose a novel model -- image Local Autoregressive Transformer (iLAT), to better facilitate the locally guided image synthesis.
NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection
Non-Maximum Suppression (NMS) is essential for object detection and affects the evaluation results by incorporating False Positives (FP) and False Negatives (FN), especially in crowd occlusion scenes.
Ranked #6 on
Pedestrian Detection
on Caltech
Delving into Data: Effectively Substitute Training for Black-box Attack
Previous substitute training approaches focus on stealing the knowledge of the target model based on real training data or synthetic data, without exploring what kind of data can further improve the transferability between the substitute and target models.
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection
The objective of this paper is to learn context- and depth-aware feature representation to solve the problem of monocular 3D object detection.
Ranked #11 on
Monocular 3D Object Detection
on KITTI Cars Moderate
Learning a Sketch Tensor Space for Image Inpainting of Man-made Scenes
To this end, this paper proposes learning a Sketch Tensor (ST) space for inpainting man-made scenes.
Learning Dynamic Alignment via Meta-filter for Few-shot Learning
Our insight is that these methods would lead to poor adaptation with redundant matching, and leveraging channel-wise adjustment is the key to well adapting the learned knowledge to new classes.
Learning Salient Boundary Feature for Anchor-free Temporal Action Localization
Temporal action localization is an important yet challenging task in video understanding.
Incrementally Zero-Shot Detection by an Extreme Value Analyzer
However, zero-shot learning models assume that all seen classes should be known beforehand, while incremental learning models cannot recognize unseen classes.
Learning Compositional Representation for 4D Captures with Neural ODE
To model the motion, a neural Ordinary Differential Equation (ODE) is trained to update the initial state conditioned on the learned motion code, and a decoder takes the shape code and the updated state code to reconstruct the 3D model at each time stamp.
A Simple Feature Augmentation for Domain Generalization
The topical domain generalization (DG) problem asks trained models to perform well on an unseen target domain with different data statistics from the source training domains.
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
In this paper, we aim to provide an alternative perspective by treating semantic segmentation as a sequence-to-sequence prediction task.
Ranked #2 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
Whose hand is this? Person Identification from Egocentric Hand Gestures
But while one's own face is not frequently visible, their hands are: in fact, hands are among the most common objects in one's own field of view.
Data-efficient Alignment of Multimodal Sequences by Aligning Gradient Updates and Internal Feature Distributions
The task of video and text sequence alignment is a prerequisite step toward joint understanding of movie videos and screenplays.
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
Humans can easily recognize actions with only a few examples given, while the existing video recognition models still heavily rely on the large-scale labeled data inputs.
Ranked #1 on
Few Shot Action Recognition
on Kinetics-100
M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging
To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic.
A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools
After more than four months study, we found that the confirmed cases of COVID-19 present the consistent ocular pathological symbols; and we propose a new screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19 with very high accuracy.
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking
Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution.
How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning
We rank the credibility of pseudo-labeled instances along the regularization path of their corresponding incidental parameters, and the most trustworthy pseudo-labeled examples are preserved as the augmented labeled instances.
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error.
Self-supervised Video Object Segmentation
The objective of this paper is self-supervised representation learning, with the goal of solving semi-supervised video object segmentation (a. k. a.
One-shot visual object segmentation
Representation Learning
+3
Long-Term Cloth-Changing Person Re-identification
Specifically, we consider that under cloth-changes, soft-biometrics such as body shape would be more reliable.
Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt
Unfortunately, the representation learned by SketchRNN is primarily for the generation tasks, rather than the other tasks of recognition and retrieval of sketches.
Instance Credibility Inference for Few-Shot Learning
To measure the credibility of each pseudo-labeled instance, we then propose to solve another linear regression hypothesis by increasing the sparsity of the incidental parameters and rank the pseudo-labeled instances with their sparsity degree.
Neural Pose Transfer by Spatially Adaptive Instance Normalization
Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh.
When Person Re-identification Meets Changing Clothes
We find that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes.
Learning to Augment Expressions for Few-shot Fine-grained Facial Expression Recognition
Considering the phenomenon of uneven data distribution and lack of samples is common in real-world scenarios, we further evaluate several tasks of few-shot expression learning by virtue of our F2ED, which are to recognize the facial expressions given only few training instances.
Facial Expression Recognition
Facial Expression Recognition (FER)
DeepSFM: Structure From Motion Via Deep Bundle Adjustment
The explicit constraints on both depth (structure) and pose (motion), when combined with the learning components, bring the merit from both traditional BA and emerging deep learning technology.
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition
One-shot fine-grained visual recognition often suffers from the problem of training data scarcity for new fine-grained classes.
Fine-Grained Image Classification
Fine-Grained Visual Recognition
+2
Extreme Value k-means Clustering
We thus propose a novel algorithm called Extreme Value k-means (EV k-means), including GEV k-means and GPD k-means.
Split LBI for Deep Learning: Structural Sparsity via Differential Inclusion Paths
Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error.
DeepEnFM: Deep neural networks with Encoder enhanced Factorization Machine
Instead of learning the cross features directly, DeepEnFM adopts the Transformer encoder as a backbone to align the feature embeddings with the clues of other fields.
Boosting Network: Learn by Growing Filters and Layers via SplitLBI
Network structures are important to learning good representations of many tasks in computer vision and machine learning communities.
Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation
We study the problem of shape generation in 3D mesh representation from a few color images with known camera poses.
A Fine-Grained Facial Expression Database for End-to-End Multi-Pose Facial Expression Recognition
First, we create a new facial expression dataset of more than 200k images with 119 persons, 4 poses and 54 expressions.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Image Deformation Meta-Networks for One-Shot Learning
Humans can robustly learn novel visual concepts even when images undergo various deformations and lose certain information.
Exploring Structural Sparsity of Deep Networks via Inverse Scale Spaces
To fill in this gap, this paper proposes a new approach based on differential inclusions of inverse scale spaces, which generate a family of models from simple to complex ones along the dynamics via coupling a pair of parameters, such that over-parameterized deep models and their structural sparsity can be explored simultaneously.
PRUNING IN TRAINING: LEARNING AND RANKING SPARSE CONNECTIONS IN DEEP CONVOLUTIONAL NETWORKS
This paper proposes a Pruning in Training (PiT) framework of learning to reduce the parameter size of networks.
$S^{2}$-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning
This paper proposes a novel Stochastic Split Linearized Bregman Iteration ($S^{2}$-LBI) algorithm to efficiently train the deep network.
Question Guided Modular Routing Networks for Visual Question Answering
Particularly, The QGMRN is composed of visual, textual and routing network.
A Multi-task Neural Approach for Emotion Attribution, Classification and Summarization
However, the sparsity of emotional expressions in the videos poses an obstacle to visual emotion analysis.
Domain-Aware SE Network for Sketch-based Image Retrieval with Multiplicative Euclidean Margin Softmax
This paper proposes a novel approach for Sketch-Based Image Retrieval (SBIR), for which the key is to bridge the gap between sketches and photos in terms of the data representation.
Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning
Zero-Shot Learning (ZSL) is generally achieved via aligning the semantic relationships between the visual features and the corresponding class semantic descriptions.
Instance-level Sketch-based Retrieval by Deep Triplet Classification Siamese Network
Additionally, to study the tasks of sketch-based hairstyle retrieval, this paper contributes a new instance-level photo-sketch dataset - Hairstyle Photo-Sketch dataset, which is composed of 3600 sketches and photos, and 2400 sketch-photo pairs.
Learning the Compositional Spaces for Generalized Zero-shot Learning
This paper studies the problem of Generalized Zero-shot Learning (G-ZSL), whose goal is to classify instances belonging to both seen and unseen classes at the test time.
VECTORIZATION METHODS IN RECOMMENDER SYSTEM
The most used recommendation method is collaborative filtering, and the key part of collaborative filtering is to compute the similarity.
Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks
With asymptotic pruning, the information of the training set would be gradually concentrated in the remaining filters, so the subsequent training and pruning process would be stable.
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks
Therefore, the network trained by our method has a larger model capacity to learn from the training data.
Detecting Tiny Moving Vehicles in Satellite Videos
Even worse, the noise signals also existed in the video frames, since the background of the video frame has the subpixel-level and uneven moving thanks to the motion of satellites.
SCSP: Spectral Clustering Filter Pruning with Soft Self-adaption Manners
In this paper, we proposed a novel filter pruning for convolutional neural networks compression, namely spectral clustering filter pruning with soft self-adaption manners (SCSP).
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
To solve this task, $L_{1}$ regularization is widely used for the pursuit of feature selection and avoiding overfitting, and yet the sparse estimation of features in $L_{1}$ regularization may cause the underfitting of training data.
Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning
To this end, we propose a novel stacked semantics-guided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions.
Multi-level Semantic Feature Augmentation for One-shot Learning
In semantic space, we search for related concepts, which are then projected back into the image feature spaces by the decoder portion of the TriNet.
A Large-scale Attribute Dataset for Zero-shot Learning
In classical ZSL algorithms, attributes are introduced as the intermediate semantic representation to realize the knowledge transfer from seen classes to unseen classes.
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
We propose an end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image.
Ranked #3 on
3D Object Reconstruction
on Data3D−R2N2
(Avg F1 metric)
Learning to score the figure skating sports videos
This paper targets at learning to score the figure skating sports videos.
VOCABULARY-INFORMED VISUAL FEATURE AUGMENTATION FOR ONE-SHOT LEARNING
Besides directly augmenting image features, we transform the image features to semantic space using the encoder and perform the data augmentation.
Pose-Normalized Image Generation for Person Re-identification
Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations.
AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
Significant progress has been achieved in Computer Vision by leveraging large-scale image datasets.
Dual Skipping Networks
Inspired by the recent neuroscience studies on the left-right asymmetry of the human brain in processing low and high spatial frequency information, this paper introduces a dual skipping network which carries out coarse-to-fine object categorization.
Recent Advances in Zero-shot Recognition
With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data.
Multi-scale Deep Learning Architectures for Person Re-identification
Our model is able to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for matching.
A Jointly Learned Deep Architecture for Facial Attribute Analysis and Face Detection in the Wild
Facial attribute analysis in the real world scenario is very challenging mainly because of complex face variations.
Vocabulary-informed Extreme Value Learning
To solve this problem, we propose the Extreme Value Learning (EVL) formulation to learn the mapping from visual feature to semantic space.
Semi-Latent GAN: Learning to generate and modify facial images from attributes
Generating and manipulating human facial images using high-level attributal controls are important and interesting problems.
Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models
In order to bridge the gap between panel attributes and the composition within each panel, we also propose a recursive page splitting algorithm to generate the panel layout for a poster.
Deep Learning for Video Classification and Captioning
Accelerated by the tremendous increase in Internet bandwidth and storage space, video data has been generated, published and spread explosively, becoming an indispensable part of today's big data.
Harnessing Object and Scene Semantics for Large-Scale Video Understanding
Large-scale action recognition and video categorization are important problems in computer vision.
Semi-supervised Vocabulary-informed Learning
Despite significant progress in object categorization, in recent years, a number of important challenges remain, mainly, ability to learn from limited labeled data and ability to recognize object classes within large, potentially open, set of labels.
Learning to Generate Posters of Scientific Papers
Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized.
Robust Classification by Pre-conditioned LASSO and Transductive Diffusion Component Analysis
Collecting datasets in this way, however, requires robust and efficient ways for detecting and excluding outliers that are common and prevalent.
Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization
Emotion is a key element in user-generated videos.
Ranked #5 on
Video Emotion Recognition
on Ekman6
Learning from Synthetic Data Using a Stacked Multichannel Autoencoder
In this paper, we investigate and formalize a general framework-Stacked Multichannel Autoencoder (SMCAE) that enables bridging the synthetic gap and learning from synthetic data more efficiently.
Transductive Multi-class and Multi-label Zero-shot Learning
Recently, zero-shot learning (ZSL) has received increasing interest.
Transductive Multi-label Zero-shot Learning
Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems.
Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder
Experimental results on two datasets validate the efficiency of our MCAE model and our methodology of generating synthetic data.
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels
In this paper, we propose a more principled way to identify annotation outliers by formulating the subjective visual property prediction task as a unified robust learning to rank problem, tackling both the outlier detection and learning to rank jointly.
Transductive Multi-view Zero-Shot Learning
A projection from a low-level feature space to the semantic representation space is learned from the auxiliary dataset and is applied without adaptation to the target dataset.
Multi-view Metric Learning for Multi-view Video Summarization
Traditional methods on video summarization are designed to generate summaries for single-view video records; and thus they cannot fully exploit the redundancy in multi-view video records.
