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Found 32 papers, 0 papers with code
A Divide-and-Conquer Method for Scalable Low-Rank Latent Matrix Pursuit
To address this issue, we provide a scalable solution for large-scale low-rank latent matrix pursuit by a divide-andconquer method.
Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks.
Personalized Age Progression with Aging Dictionary
Second, it is challenging or even impossible to collect faces of all age groups for a particular subject, yet much easier and more practical to get face pairs from neighboring age groups.
Deep Recurrent Regression for Facial Landmark Detection
We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures.
Instance-Aware Hashing for Multi-Label Image Retrieval
The instance-aware representations not only bring advantages to semantic hashing, but also can be used in category-aware hashing, in which an image is represented by multiple pieces of hash codes and each piece of code corresponds to a category.
Personalized Age Progression with Bi-level Aging Dictionary Learning
Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e. g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process.
Learning Adaptive Receptive Fields for Deep Image Parsing Network
In this paper, we introduce a novel approach to regulate receptive field in deep image parsing network automatically.
Improved Search in Hamming Space using Deep Multi-Index Hashing
Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks.
Transductive Zero-Shot Hashing via Coarse-to-Fine Similarity Mining
It mainly consists of two building blocks in the proposed deep architecture: 1) a shared two-streams network, which the first stream operates on the source data and the second stream operates on the unlabeled data, to learn the effective common image representations, and 2) a coarse-to-fine module, which begins with finding the most representative images from target classes and then further detect similarities among these images, to transfer the similarities of the source data to the target data in a greedy fashion.
HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval
The proposed new adversarial network, HashGAN, consists of three building blocks: 1) the feature learning module to obtain feature representations, 2) the generative attention module to generate an attention mask, which is used to obtain the attended (foreground) and the unattended (background) feature representations, 3) the discriminative hash coding module to learn hash functions that preserve the similarities between different modalities.
Personalized and Occupational-aware Age Progression by Generative Adversarial Networks
Second, we propose a new occupational-aware adversarial face aging network, which learns human aging process under different occupations.
Regularizing Deep Hashing Networks Using GAN Generated Fake Images
To address this issue, in this paper, we propose a simple two-stage pipeline to learn deep hashing models, by regularizing the deep hashing networks using fake images.
Improving Deep Binary Embedding Networks by Order-aware Reweighting of Triplets
In this paper, we focus on triplet-based deep binary embedding networks for image retrieval task.
Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis
Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations.
Towards Multi-pose Guided Virtual Try-on Network
Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-on Network (MG-VTON) can generate a new person image after fitting the desired clothes into the input image and manipulating human poses.
Ranked #1 on
Virtual Try-on
on Deep-Fashion
Feature Pyramid Hashing
In recent years, deep-networks-based hashing has become a leading approach for large-scale image retrieval.
Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning
Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized training and decentralized execution(CTDE) scheme, where all agents are trained together by the centralized valuenetwork and each agent execute its policy independently.
Simultaneous Region Localization and Hash Coding for Fine-grained Image Retrieval
Fine-grained image hashing is a challenging problem due to the difficulties of discriminative region localization and hash code generation.
Modal-aware Features for Multimodal Hashing
The modal-aware operation consists of a kernel network and an attention network.
Controllable Face Aging
Second, since the image may contain other unwanted attributes, an attribute disentanglement network is used to separate the individual embedding and learn the common embedding that contains information about the face attribute (e. g., race).
Learning Expensive Coordination: An Event-Based Deep RL Approach
The main difficulties of expensive coordination are that i) the leader has to consider the long-term effect and predict the followers' behaviors when assigning bonuses and ii) the complex interactions between followers make the training process hard to converge, especially when the leader's policy changes with time.
Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear Connector
Our method also achieves 6. 33% higher accuracy on TinyImageNet.
ViT2Hash: Unsupervised Information-Preserving Hashing
Specifically, from pixels to continuous features, we first propose a feature-preserving module, using the corrupted image as input to reconstruct the original feature from the pre-trained ViT model and the complete image, so that the feature extractor can focus on preserving the meaningful information of original data.
Revisiting Few-Shot Learning from a Causal Perspective
Despite that these approaches have shown significant progress, the mechanism of why these methods succeed has not been well explored.
Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning
With the causal graph, a counterfactual inference is proposed to disentangle the disconnected reasoning from the total causal effect, which provides us a new perspective and technology to learn a QA model that exploits the true multi-hop reasoning instead of shortcuts.
Deep Hashing With Minimal-Distance-Separated Hash Centers
This paper presents an optimization method that finds hash centers with a constraint on the minimal distance between any pair of hash centers, which is non-trivial due to the non-convex nature of the problem.
From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser for Complex Question Answering over Knowledge Base
The parser uses the KoPL to generate the transparent logical forms.
Ranking-aware Uncertainty for Text-guided Image Retrieval
Specifically, our approach mainly comprises three components: (1) In-sample uncertainty, which aims to capture semantic diversity using a Gaussian distribution derived from both combined and target features; (2) Cross-sample uncertainty, which further mines the ranking information from other samples' distributions; and (3) Distribution regularization, which aligns the distributional representations of source inputs and targeted image.
Ranked #3 on
Image Retrieval
on Fashion IQ
Improving Entropy-Based Test-Time Adaptation from a Clustering View
In this paper, we introduce a new perspective on the EBTTA, which interprets these methods from a view of clustering.
Pretrain like Your Inference: Masked Tuning Improves Zero-Shot Composed Image Retrieval
Then, we propose a masked tuning, which uses the text and the masked image to learn the modifications of the original image.
MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model
Diffusion-based adversarial purification focuses on using the diffusion model to generate a clean image against such adversarial attacks.
Target to Source: Guidance-Based Diffusion Model for Test-Time Adaptation
However, 1) the semantic information loss from test data to the source domain and 2) the model shift between the source classifier and diffusion model would prevent the diffusion model from mapping the test data back to the source domain correctly.
