Search Results for author:
Found 13 papers, 5 papers with code
A survey of Transformer applications for histopathological image analysis: New developments and future directions
Transformers have been widely used in many computer vision challenges and have shown the capability of producing better results than convolutional neural networks (CNNs).
BiGSeT: Binary Mask-Guided Separation Training for DNN-based Hyperspectral Anomaly Detection
Our method introduces a separation training loss based on a latent binary mask to separately constrain the background and anomalies in the estimated image.
MDL-NAS: A Joint Multi-Domain Learning Framework for Vision Transformer
Technically, MDL-NAS constructs a coarse-to-fine search space, where the coarse search space offers various optimal architectures for different tasks while the fine search space provides fine-grained parameter sharing to tackle the inherent obstacles of multi-domain learning.
IntraLoss: Further Margin via Gradient-Enhancing Term for Deep Face Recognition
This method, named IntraLoss, explicitly performs gradient enhancement in the anisotropic region so that the intra-class distribution continues to shrink, resulting in isotropic and more compact intra-class distribution and further margin between identities.
Strong but Simple Baseline with Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification
In this letter, we propose a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID).
Ranked #2 on
Cross-Modal Person Re-Identification
on RegDB
Parameter Sharing Exploration and Hetero-Center based Triplet Loss for Visible-Thermal Person Re-Identification
By well splitting the ResNet50 model to construct the modality-specific feature extracting network and modality-sharing feature embedding network, we experimentally demonstrate the effect of parameters sharing of two-stream network for VT Re-ID.
Ranked #1 on
Cross-Modal Person Re-Identification
on RegDB
Cross-Modality Person Re-identification
Cross-Modal Person Re-Identification
Neural Network Activation Quantization with Bitwise Information Bottlenecks
Recent researches on information bottleneck shed new light on the continuous attempts to open the black box of neural signal encoding.
Enhancing the Discriminative Feature Learning for Visible-Thermal Cross-Modality Person Re-Identification
To address these two issues, we propose focusing on enhancing the discriminative feature learning (EDFL) with two extreme simple means from two core aspects, (1) skip-connection for mid-level features incorporation to improve the person features with more discriminability and robustness, and (2) dual-modality triplet loss to guide the training procedures by simultaneously considering the cross-modality discrepancy and intra-modality variations.
Cross-Modality Person Re-identification
Person Re-Identification
Attention: A Big Surprise for Cross-Domain Person Re-Identification
Unlike existing cross-domain Re-ID methods, leveraging the auxiliary information of those unlabeled target-domain data, we aim at enhancing the model generalization and adaptation by discriminative feature learning, and directly exploiting a pre-trained model to new domains (datasets) without any utilization of the information from target domains.
The General Pair-based Weighting Loss for Deep Metric Learning
A large amount of loss functions based on pair distances have been presented in the literature for guiding the training of deep metric learning.
Temporal Action Detection by Joint Identification-Verification
Temporal action detection aims at not only recognizing action category but also detecting start time and end time for each action instance in an untrimmed video.
PCN: Part and Context Information for Pedestrian Detection with CNNs
To take advantage of the body parts and context information for pedestrian detection, we propose the part and context network (PCN) in this work.
Additive Margin Softmax for Face Verification
In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works.
Ranked #2 on
Face Identification
on Trillion Pairs Dataset
