Taking Modality-free Human Identification as Zero-shot Learning
Human identification is an important topic in event detection, person tracking, and public security. There have been numerous methods proposed for human identification, such as face identification, person re-identification, and gait identification. Typically, existing methods predominantly classify a queried image to a specific identity in an image gallery set (I2I). This is seriously limited for the scenario where only a textual description of the query or an attribute gallery set is available in a wide range of video surveillance applications (A2I or I2A). However, very few efforts have been devoted towards modality-free identification, i.e., identifying a query in a gallery set in a scalable way. In this work, we take an initial attempt, and formulate such a novel Modality-Free Human Identification (named MFHI) task as a generic zero-shot learning model in a scalable way. Meanwhile, it is capable of bridging the visual and semantic modalities by learning a discriminative prototype of each identity. In addition, the semantics-guided spatial attention is enforced on visual modality to obtain representations with both high global category-level and local attribute-level discrimination. Finally, we design and conduct an extensive group of experiments on two common challenging identification tasks, including face identification and person re-identification, demonstrating that our method outperforms a wide variety of state-of-the-art methods on modality-free human identification.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeEvent DetectionFace IdentificationGait IdentificationPerson Re-IdentificationZero-Shot LearningSimilar Papers 제목 키워드 기반
DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training
Large-scale pre-trained multi-modal models (e.g., CLIP) demonstrate strong zero-shot transfer capability in many discriminative tasks. Their adaptation to zero-shot image-conditioned text generation tasks has drawn incre…
DecoderImage CaptioningText GenerationRGB-Infrared Cross-Modality Person Re-Identification
Person re-identification (Re-ID) is an important problem in video surveillance, aiming to match pedestrian images across camera views. Currently, most works focus on RGB-based Re-ID. However, in some applications, RGB im…
Cross-Modality Person Re-identificationCross-Modal Person Re-IdentificationPerson Re-IdentificationDual-level Modality Debiasing Learning for Unsupervised Visible-Infrared Person Re-Identification
Two-stage learning pipeline has achieved promising results in unsupervised visible-infrared person re-identification (USL-VI-ReID). It first performs single-modality learning and then operates cross-modality learning to …
Person Re-IdentificationHPILN: A feature learning framework for cross-modality person re-identification
Most video surveillance systems use both RGB and infrared cameras, making it a vital technique to re-identify a person cross the RGB and infrared modalities. This task can be challenging due to both the cross-modality va…
Cross-Modality Person Re-identificationPerson Re-IdentificationDMPT: Decoupled Modality-aware Prompt Tuning for Multi-modal Object Re-identification
Current multi-modal object re-identification approaches based on large-scale pre-trained backbones (i.e., ViT) have displayed remarkable progress and achieved excellent performance. However, these methods usually adopt t…