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Found 15 papers, 8 papers with code
Multi-view Information Integration and Propagation for Occluded Person Re-identification
Moreover, as multiple images with the same identity are not accessible in the testing stage, we devise an Information Propagation (IP) mechanism to distill knowledge from the comprehensive representation to that of a single occluded image.
Learning Comprehensive Representations with Richer Self for Text-to-Image Person Re-Identification
Since the support set is unavailable during inference, we propose to distill the knowledge learned by the "richer" model into a lightweight model for inference with a single image/text as input.
Prototype-guided Cross-modal Completion and Alignment for Incomplete Text-based Person Re-identification
Therefore, we propose the cross-modal nearest neighbor construction strategy for missing data by computing the cross-modal similarity between existing images and texts, which provides key guidance for the completion of missing modal features.
Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification
Occlusion perturbation presents a significant challenge in person re-identification (re-ID), and existing methods that rely on external visual cues require additional computational resources and only consider the issue of missing information caused by occlusion.
Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation
Specifically, a flexible context aggregation module is proposed to capture the global object context in different granular spaces.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-identification
To address this problem, in this paper, we propose a simple Triplet Contrastive Representation Learning (TCRL) framework which leverages cluster features to bridge the part features and global features for unsupervised vehicle re-identification.
CLIP-Driven Fine-grained Text-Image Person Re-identification
Secondly, cross-grained feature refinement (CFR) and fine-grained correspondence discovery (FCD) modules are proposed to establish the cross-grained and fine-grained interactions between modalities, which can filter out non-modality-shared image patches/words and mine cross-modal correspondences from coarse to fine.
Centralized Feature Pyramid for Object Detection
To address this problem, in this paper, we propose a Centralized Feature Pyramid (CFP) for object detection, which is based on a globally explicit centralized feature regulation.
Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search
Moreover, existing methods seldom consider the information inequality problem between modalities caused by image-specific information.
Semantically Contrastive Learning for Low-light Image Enhancement
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.
Learning Calibrated-Guidance for Object Detection in Aerial Images
Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance.
Graph Attention Tracking
We propose to establish part-to-part correspondence between the target and the search region with a complete bipartite graph, and apply the graph attention mechanism to propagate target information from the template feature to the search feature.
Spatiotemporal Co-attention Recurrent Neural Networks for Human-Skeleton Motion Prediction
To this end, we propose a novel Skeleton-joint Co-attention Recurrent Neural Networks (SC-RNN) to capture the spatial coherence among joints, and the temporal evolution among skeletons simultaneously on a skeleton-joint co-attention feature map in spatiotemporal space.
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.
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
To this end, we propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to model the long-term inter-related dynamics between two interacting people on the bounding boxes covering people.
Ranked #2 on
Human Interaction Recognition
on BIT
