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Found 22 papers, 10 papers with code
Which Model to Transfer? A Survey on Transferability Estimation
In this survey, we present the first review of existing advances in this area and categorize them into two separate realms: source-free model transferability estimation and source-dependent model transferability estimation.
Unleashing the power of Neural Collapse for Transferability Estimation
Transferability estimation aims to provide heuristics for quantifying how suitable a pre-trained model is for a specific downstream task, without fine-tuning them all.
Multi-query Vehicle Re-identification: Viewpoint-conditioned Network, Unified Dataset and New Metric
Existing vehicle re-identification methods mainly rely on the single query, which has limited information for vehicle representation and thus significantly hinders the performance of vehicle Re-ID in complicated surveillance networks.
Dynamic Enhancement Network for Partial Multi-modality Person Re-identification
Many existing multi-modality studies are based on the assumption of modality integrity.
Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification
Finally, to evaluate the proposed FACENet in handling intense flare, we introduce a new multi-spectral vehicle re-ID dataset, called WMVEID863, with additional challenges such as motion blur, significant background changes, and particularly intense flare degradation.
MODIFY: Model-driven Face Stylization without Style Images
Briefly, MODIFY first trains a generative model in the target domain and then translates a source input to the target domain via the provided style model.
MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection
Existing cross-domain keypoint detection methods always require accessing the source data during adaptation, which may violate the data privacy law and pose serious security concerns.
Parallel Augmentation and Dual Enhancement for Occluded Person Re-identification
To alleviate these two issues, we propose a simple yet effective method with Parallel Augmentation and Dual Enhancement (PADE), which is robust on both occluded and non-occluded data and does not require any auxiliary clues.
Multi-spectral Vehicle Re-identification with Cross-directional Consistency Network and a High-quality Benchmark
In particular, we design a new cross-directional center loss to pull the modality centers of each identity close to mitigate cross-modality discrepancy, while the sample centers of each identity close to alleviate the sample discrepancy.
Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset
License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few.
ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation
First of all, to avoid additional parameters and explore the information in the source model, ProxyMix defines the weights of the classifier as the class prototypes and then constructs a class-balanced proxy source domain by the nearest neighbors of the prototypes to bridge the unseen source domain and the target domain.
Adversarial-Metric Learning for Audio-Visual Cross-Modal Matching
AML aims to generate a modality-independent representation for each person in each modality via adversarial learning, while simultaneously learns a robust similarity measure for cross-modality matching via metric learning.
Viewpoint-aware Progressive Clustering for Unsupervised Vehicle Re-identification
Comprehensive experiments against the state-of-the-art methods on two multi-viewpoint benchmark datasets VeRi and VeRi-Wild validate the promising performance of the proposed method in both with and without domain adaption scenarios while handling unsupervised vehicle Re-ID.
Unsupervised Contrastive Photo-to-Caricature Translation based on Auto-distortion
Considering the intuitive artifacts in the existing methods, we propose a contrastive style loss for style rendering to enforce the similarity between the style of rendered photo and the caricature, and simultaneously enhance its discrepancy to the photos.
Lets Play Music: Audio-driven Performance Video Generation
We propose a new task named Audio-driven Per-formance Video Generation (APVG), which aims to synthesizethe video of a person playing a certain instrument guided bya given music audio clip.
STADB: A Self-Thresholding Attention Guided ADB Network for Person Re-identification
Specifically, STADB first obtains an attention map by channel-wise pooling and returns a drop mask by thresholding the attention map.
Deep Audio-Visual Learning: A Survey
Audio-visual learning, aimed at exploiting the relationship between audio and visual modalities, has drawn considerable attention since deep learning started to be used successfully.
Multi-Adapter RGBT Tracking
In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object.
Attributes Guided Feature Learning for Vehicle Re-identification
In particular, our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes (i. e., camera view, vehicle type and vehicle color).
Pedestrian Attribute Recognition: A Survey
We also review some popular network architectures which have been widely applied in the deep learning community.
Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning
Talking face generation aims to synthesize a face video with precise lip synchronization as well as a smooth transition of facial motion over the entire video via the given speech clip and facial image.
A Unified RGB-T Saliency Detection Benchmark: Dataset, Baselines, Analysis and A Novel Approach
In particular, we introduce a weight for each modality to describe the reliability, and integrate them into the graph-based manifold ranking algorithm to achieve adaptive fusion of different source data.
