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Found 17 papers, 8 papers with code
Hierarchical Prompts for Rehearsal-free Continual Learning
Nonetheless, these learnable prompts tend to concentrate on the discriminatory knowledge of the current task while ignoring past task knowledge, leading to that learnable prompts still suffering from catastrophic forgetting.
Hierarchical Augmentation and Distillation for Class Incremental Audio-Visual Video Recognition
We introduce Hierarchical Augmentation and Distillation (HAD), which comprises the Hierarchical Augmentation Module (HAM) and Hierarchical Distillation Module (HDM) to efficiently utilize the hierarchical structure of data and models, respectively.
TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model
However, those textual tokens have a limited generalization ability regarding unseen domains, as they cannot dynamically adjust to the distribution of testing classes.
Camera-Incremental Object Re-Identification with Identity Knowledge Evolution
In this paper, we propose a novel Identity Knowledge Evolution (IKE) framework for CIOR, consisting of the Identity Knowledge Association (IKA), Identity Knowledge Distillation (IKD), and Identity Knowledge Update (IKU).
Visual-Language Prompt Tuning with Knowledge-guided Context Optimization
Representative CoOp-based work combines the learnable textual tokens with the class tokens to obtain specific textual knowledge.
UTM: A Unified Multiple Object Tracking Model With Identity-Aware Feature Enhancement
Recently, Multiple Object Tracking has achieved great success, which consists of object detection, feature embedding, and identity association.
Dual Cluster Contrastive learning for Object Re-Identification
Unlike the individual-based updating mechanism, the centroid-based updating mechanism that applies the mean feature of each cluster to update the cluster memory can reduce the impact of individual samples.
Ranked #50 on
Person Re-Identification
on Market-1501
Hierarchical Granularity Transfer Learning
In this paper, we introduce a new task, named Hierarchical Granularity Transfer Learning (HGTL), to recognize sub-level categories with basic-level annotations and semantic descriptions for hierarchical categories.
Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning
Then, an attentional graph attribute embedding is proposed to reduce the semantic bias between seen and unseen categories, which utilizes the graph operation to capture the semantic relationship between categories.
Joint Person Objectness and Repulsion for Person Search
Based on this repulsion constraint, the repulsion term is proposed to reduce the similarity of distractor images that are not most similar to the probe person.
Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning
Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the effect of semantic-free visual representation in alleviating the biased recognition problem.
Multi-Objective Matrix Normalization for Fine-grained Visual Recognition
In this paper, we propose an efficient Multi-Objective Matrix Normalization (MOMN) method that can simultaneously normalize a bilinear representation in terms of square-root, low-rank, and sparsity.
Domain-Specific Embedding Network for Zero-Shot Recognition
In contrast to previous methods, the DSEN decomposes the domain-shared projection function into one domain-invariant and two domain-specific sub-functions to explore the similarities and differences between two domains.
GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
Targeting to solve these problems, this work proposes a Global-Local-Alignment Descriptor (GLAD) and an efficient indexing and retrieval framework, respectively.
Ranked #93 on
Person Re-Identification
on Market-1501
Deep Representation Learning with Part Loss for Person Re-Identification
The representation learning risk is evaluated by the proposed part loss, which automatically generates several parts for an image, and computes the person classification loss on each part separately.
Ranked #97 on
Person Re-Identification
on Market-1501
One-Shot Fine-Grained Instance Retrieval
Aiming to conquer this issue, we propose a retrieval task named One-Shot Fine-Grained Instance Retrieval (OSFGIR).
DR2-Net: Deep Residual Reconstruction Network for Image Compressive Sensing
Accordingly, DR$^{2}$-Net consists of two components, \emph{i. e.,} linear mapping network and residual network, respectively.
