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Found 33 papers, 17 papers with code
View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network
Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5. 0%/2. 7% on CARGO and 3. 7%/5. 2% on AG-ReID, keeping the same magnitude of computational complexity.
Tackling the Singularities at the Endpoints of Time Intervals in Diffusion Models
Most diffusion models assume that the reverse process adheres to a Gaussian distribution.
Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis
While existing methods simply align the person appearance to the target pose, they are prone to overfitting due to the lack of a high-level semantic understanding on the source person image.
Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability
Recently, many studies utilized adversarial examples (AEs) to raise the cost of malicious image editing and copyright violation powered by latent diffusion models (LDMs).
Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion
We introduce a two-stage pipeline to effectively train our network: Stage I utilizes inter-speech contrastive learning to model fine-grained emotion and intra-speech disentanglement learning to better separate emotion and content.
MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation
Universal domain adaptation (UniDA) is a practical but challenging problem, in which information about the relation between the source and the target domains is not given for knowledge transfer.
Ranked #2 on
Universal Domain Adaptation
on Office-Home
ASAG: Building Strong One-Decoder-Layer Sparse Detectors via Adaptive Sparse Anchor Generation
We find that the architecture discrepancy between dense and sparse detectors leads to feature conflict, hampering the performance of one-decoder-layer detectors.
APRF: Anti-Aliasing Projection Representation Field for Inverse Problem in Imaging
However, these methods have not considered the correlation between adjacent projection views, resulting in aliasing artifacts on SV sinograms.
Releasing Inequality Phenomena in $L_{\infty}$-Adversarial Training via Input Gradient Distillation
However, a recent work showed the inequality phenomena in $l_{\infty}$-adversarial training and revealed that the $l_{\infty}$-adversarially trained model is vulnerable when a few important pixels are perturbed by i. i. d.
Hard Nominal Example-aware Template Mutual Matching for Industrial Anomaly Detection
Additionally, to meet the speed-accuracy demands, we further propose \textbf{P}ixel-level \textbf{T}emplate \textbf{S}election (PTS) to streamline the original template set.
Ranked #12 on
Anomaly Detection
on MVTec LOCO AD
CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution
Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread attention, aiming to super sample medical volumes at arbitrary scales via a single model.
Adversarial Attack and Defense for Medical Image Analysis: Methods and Applications
In this exposition, we present a comprehensive survey on recent advances in adversarial attack and defense for medical image analysis with a novel taxonomy in terms of the application scenario.
The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training
To circumvent this issue, we propose a novel adversarial training scheme that encourages the model to produce similar outputs for an adversarial example and its ``inverse adversarial'' counterpart.
SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
Another advantage of the module is that most of the operators are selected based on the solution to the defined energy function, avoiding too many efforts for structure tuning.
Texture-guided Saliency Distilling for Unsupervised Salient Object Detection
In this paper, we propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples.
SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks
The SNN2ANN consists of 2 components: a) a weight sharing architecture between ANN and SNN and b) spiking mapping units.
3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds
3D deep models consuming point clouds have achieved sound application effects in computer vision.
Cross-Camera Trajectories Help Person Retrieval in a Camera Network
To address this issue, we propose a pedestrian retrieval framework based on cross-camera trajectory generation, which integrates both temporal and spatial information.
Restricted Black-box Adversarial Attack Against DeepFake Face Swapping
DeepFake face swapping presents a significant threat to online security and social media, which can replace the source face in an arbitrary photo/video with the target face of an entirely different person.
Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels has attracted much attention due to low annotation costs.
Ranked #20 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Exploring Dual-task Correlation for Pose Guided Person Image Generation
Pose Guided Person Image Generation (PGPIG) is the task of transforming a person image from the source pose to a given target pose.
Benchmarking Deep Models for Salient Object Detection
In recent years, deep network-based methods have continuously refreshed state-of-the-art performance on Salient Object Detection (SOD) task.
Modeling 3D Layout for Group Re-Identification
To the best of our knowledge, 3DT is the first work to address GReID with 3D perspective, and the City1M is the currently largest dataset.
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations
Extensive experiments show that our method can significantly outperform state-of-the-art adversarially robust FSIC methods on two standard benchmarks.
Activation to Saliency: Forming High-Quality Labels for Completely Unsupervised Salient Object Detection
Moreover, a new loss function is proposed to facilitate the generation of high-quality pseudo labels.
Edge Prior Augmented Networks for Motion Deblurring on Naturally Blurry Images
Motion deblurring has witnessed rapid development in recent years, and most of the recent methods address it by using deep learning techniques, with the help of different kinds of prior knowledge.
Batch Face Alignment using a Low-rank GAN
This paper studies the problem of aligning a set of face images of the same individual into a normalized image while removing the outliers like partial occlusion, extreme facial expression as well as significant illumination variation.
Contour Loss: Boundary-Aware Learning for Salient Object Segmentation
Yet the Contour Loss emphasizes on the local saliency.
Discovering Underlying Person Structure Pattern with Relative Local Distance for Person Re-identification
With the discovered underlying person structure, the RLD method builds a bridge between the global and local feature representation and thus improves the capacity of feature representation for person re-ID.
Spatial-Temporal Person Re-identification
In this paper, we propose a novel two-stream spatial-temporal person ReID (st-ReID) framework that mines both visual semantic information and spatial-temporal information.
Ranked #4 on
Person Re-Identification
on Market-1501
Learning View-Specific Deep Networks for Person Re-Identification
In recent years, a growing body of research has focused on the problem of person re-identification (re-id).
Deep Growing Learning
A bottleneck of SSL is the overfitting problem when training over the limited labeled data, especially on a complex model like a deep neural network.
Motion-Appearance Interactive Encoding for Object Segmentation in Unconstrained Videos
We present a novel method of integrating motion and appearance cues for foreground object segmentation in unconstrained videos.
