Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:52:20.893254Z
Paper Citation Record · LEDGER
As of 10 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2507.07262.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:52:20.893254Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
64 of 64 outbound references displayed
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Observation e48df077-2342-46aa-9616-093405f55e55 · outbound
DisenQ: Disentangling Q-Former for Activity-Biometrics Past, present, and future of face recognition: A review
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DisenQ: Disentangling Q-Former for Activity-Biometrics Flamingo: A visual language model for few-shot learning
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DisenQ: Disentangling Q-Former for Activity-Biometrics Activity- biometrics: Person identification from daily activities
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DisenQ: Disentangling Q-Former for Activity-Biometrics Hierarq: Task-aware hierarchical q-former for enhanced video understanding
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DisenQ: Disentangling Q-Former for Activity-Biometrics Salient-to-broad transition for video person re- identification
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DisenQ: Disentangling Q-Former for Activity-Biometrics Unveiling the power of clip in unsupervised visible-infrared person re-identification
Reference 10
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DisenQ: Disentangling Q-Former for Activity-Biometrics InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
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DisenQ: Disentangling Q-Former for Activity-Biometrics Mevid: Multi-view extended videos with identities for video person re-identification
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DisenQ: Disentangling Q-Former for Activity-Biometrics BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Reference 13
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DisenQ: Disentangling Q-Former for Activity-Biometrics Learning disentangled rep- resentation for robust person re-identification
Reference 14
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DisenQ: Disentangling Q-Former for Activity-Biometrics Video-based person re-identification with spatial and tempo- ral memory networks
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DisenQ: Disentangling Q-Former for Activity-Biometrics Gaitpart: Temporal part-based model for gait recognition
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DisenQ: Disentangling Q-Former for Activity-Biometrics Opengait: Revisiting gait recognition towards better practicality
Reference 17
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DisenQ: Disentangling Q-Former for Activity-Biometrics Appearance-preserving 3d convolution for video-based person re-identification
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Reference 20
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Reference 21
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Reference 22
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Reference 23
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DisenQ: Disentangling Q-Former for Activity-Biometrics Bicnet-tks: Learning efficient spatial- temporal representation for video person re-identification
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DisenQ: Disentangling Q-Former for Activity-Biometrics Empowering visible- infrared person re-identification with large foundation mod- els
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DisenQ: Disentangling Q-Former for Activity-Biometrics Celebrities-reid: A benchmark for clothes variation in long- term person re-identification
Reference 27
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DisenQ: Disentangling Q-Former for Activity-Biometrics Cloth-changing person re- identification from a single image with gait prediction and regularization
Reference 29
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DisenQ: Disentangling Q-Former for Activity-Biometrics LLaVA-OneVision: Easy Visual Task Transfer
Reference 30
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DisenQ: Disentangling Q-Former for Activity-Biometrics Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Reference 31
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DisenQ: Disentangling Q-Former for Activity-Biometrics Clip-reid: exploiting vision-language model for image re-identification without concrete text labels
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DisenQ: Disentangling Q-Former for Activity-Biometrics Gaitedge: Beyond plain end-to-end gait recognition for better practicality
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DisenQ: Disentangling Q-Former for Activity-Biometrics Differ: Disentangling iden- tity features via semantic cues for clothes-changing person re-id
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DisenQ: Disentangling Q-Former for Activity-Biometrics Gait recognition via effective global-local feature representation and local tempo- ral aggregation
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DisenQ: Disentangling Q-Former for Activity-Biometrics Deeply coupled convolution–transformer with spatial– temporal complementary learning for video-based person re- identification
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DisenQ: Disentangling Q-Former for Activity-Biometrics Magface: A universal representation for face recognition and quality assessment
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DisenQ: Disentangling Q-Former for Activity-Biometrics GPT-4 Technical Report
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DisenQ: Disentangling Q-Former for Activity-Biometrics Fine-Grained Re-Identification
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Reference 2024
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