On the rankability of visual embeddings | Paper Citation Record

Observation 72fcb7d1-1878-4a5b-987a-7a8956bbd1b7 · outbound

This paper cites Understanding intermediate layers using linear classifier probes.

On the rankability of visual embeddings Understanding intermediate layers using linear classifier probes

Reference 1

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Observation 72f8616e-862e-4547-92c6-4f8ae4d82f13 · outbound

This paper cites Conditioned and composed image retrieval combining and partially fine-tuning CLIP-based features.

On the rankability of visual embeddings Conditioned and composed image retrieval combining and partially fine-tuning CLIP-based features

Reference 2

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Observation 671d080d-4f91-48ef-b91b-bc3ff30dd0f1 · outbound

This paper cites Not Only Text: Exploring Compositionality of Visual Representations in Vision-Language Models.

On the rankability of visual embeddings Not Only Text: Exploring Compositionality of Visual Representations in Vision-Language Models

Reference 3

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Observation 82b92998-93e2-45a5-9a65-d480c670884b · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

On the rankability of visual embeddings A Simple Framework for Contrastive Learning of Visual Representations

Reference 4

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Observation 511cf3cc-d07f-4c12-a0ae-b1d11993093b · outbound

This paper cites Deep Learning for Instance Retrieval: A Survey.

On the rankability of visual embeddings Deep Learning for Instance Retrieval: A Survey

Reference 5

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This paper cites Deep learning for instance retrieval: A survey.

On the rankability of visual embeddings Deep learning for instance retrieval: A survey

Reference 6

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Observation af97d169-f7c6-4cc0-8ed5-efa72c98a39c · outbound

This paper cites Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds.

On the rankability of visual embeddings Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds

Reference 7

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Observation 94a871ac-e6e3-41c2-b3b3-06ddd5a78bde · outbound

This paper cites Hyperbolic Image-Text Representations.

On the rankability of visual embeddings Hyperbolic Image-Text Representations

Reference 8

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Observation 2d825a8c-c3ea-4a01-a78d-877669bf5ce3 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

On the rankability of visual embeddings An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

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Observation b6cc826c-934f-489b-94e1-f44b9422ddc1 · outbound

This paper cites Teach CLIP to Develop a Number Sense for Ordinal Regression.

On the rankability of visual embeddings Teach CLIP to Develop a Number Sense for Ordinal Regression

Reference 10

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Observation 8883acf4-b68b-4343-903e-8706086d6021 · outbound

This paper cites Age and Gender Estimation of Unfiltered Faces.

On the rankability of visual embeddings Age and Gender Estimation of Unfiltered Faces

Reference 11

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Observation 9cfaf071-0d3c-4582-83b4-b5dd8d280749 · outbound

This paper cites It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap.

On the rankability of visual embeddings It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap

Reference 12

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Observation 9dd7610a-3d37-4134-a268-e1343c54c0f3 · outbound

This paper cites Heterogeneous face attribute estimation: A deep multi-task learning approach.

On the rankability of visual embeddings Heterogeneous face attribute estimation: A deep multi-task learning approach

Reference 14

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Observation 477da1c2-50ff-4f16-8b1d-05c3201567d5 · outbound

This paper cites Deep Residual Learning for Image Recognition.

On the rankability of visual embeddings Deep Residual Learning for Image Recognition

Reference 15

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This paper cites Multilayer feedforward networks are universal approximators.

On the rankability of visual embeddings Multilayer feedforward networks are universal approximators

Reference 16

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Observation bb2b5fda-2abe-47f1-b5e9-2c4c8f7635f0 · outbound

This paper cites KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment.

On the rankability of visual embeddings KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment

Reference 17

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Observation 97fb22fc-96d4-4760-9b18-e002309f4483 · outbound

This paper cites Lp++: A surprisingly strong linear probe for few-shot clip.

On the rankability of visual embeddings Lp++: A surprisingly strong linear probe for few-shot clip

Reference 18

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Observation 9d9125c5-7393-4fdd-90fd-9213f230accf · outbound

This paper cites The Platonic Representation Hypothesis.

On the rankability of visual embeddings The Platonic Representation Hypothesis

Reference 19

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Observation 1e02910c-73b7-4adf-a889-8aa29d70c8f0 · outbound

This paper cites CLIP-Count: Towards Text-Guided Zero- Shot Object Counting.

On the rankability of visual embeddings CLIP-Count: Towards Text-Guided Zero- Shot Object Counting

Reference 20

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Observation 22adfc6d-f764-4110-8eea-9146e82b38b7 · outbound

This paper cites Hyperbolic Image Embeddings.

On the rankability of visual embeddings Hyperbolic Image Embeddings

Reference 21

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Observation 86d92a71-4aea-4f83-ad09-41ba0cd792a0 · outbound

This paper cites MLLM-CompBench: A Comparative Reasoning Benchmark for Multimodal LLMs.

On the rankability of visual embeddings MLLM-CompBench: A Comparative Reasoning Benchmark for Multimodal LLMs

Reference 22

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Observation 4c0d978c-48c2-461d-9110-e11b77b6153b · outbound

This paper cites Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav).

On the rankability of visual embeddings Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)

Reference 23

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Observation d74f666a-fe1f-4221-be11-5cb58b0a6fd4 · outbound

This paper cites CLIP Behaves like a Bag-of-Words Model Cross-modally but Not Uni-modally.

On the rankability of visual embeddings CLIP Behaves like a Bag-of-Words Model Cross-modally but Not Uni-modally

Reference 24

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Observation de4d0571-cc07-4331-a0c2-034a6b53f7b1 · outbound

This paper cites CLIP Behaves like a Bag-of-Words Model Cross-modally but not Uni-modally.

On the rankability of visual embeddings CLIP Behaves like a Bag-of-Words Model Cross-modally but not Uni-modally

Reference 25

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Observation d160e3ef-edc5-4886-b0e0-5c3704ab4ed2 · outbound

This paper cites Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning.

On the rankability of visual embeddings Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning

Reference 26

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Observation c5ada6c4-84e0-41ad-b510-1f10d8b09f98 · outbound

This paper cites MiVOLO: Multi-input Transformer for Age and Gender Estimation.

On the rankability of visual embeddings MiVOLO: Multi-input Transformer for Age and Gender Estimation

Reference 27

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This paper cites The Double-Ellipsoid Geometry of CLIP.

On the rankability of visual embeddings The Double-Ellipsoid Geometry of CLIP

Reference 28

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Observation 4d502446-6440-47de-b3a7-37f7735ac5f9 · outbound

This paper cites Does CLIP Bind Concepts? Probing Compositionality in Large Image Models.

On the rankability of visual embeddings Does CLIP Bind Concepts? Probing Compositionality in Large Image Models

Reference 29

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Observation 2624fd97-d6cf-4533-b4f8-02e1e764d3dd · outbound

This paper cites Align before fuse: Vision and language representation learning with mo- mentum distillation.

On the rankability of visual embeddings Align before fuse: Vision and language representation learning with mo- mentum distillation

Reference 30

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Observation bf413c30-217d-4123-95e4-6fe4efb58ffe · outbound

This paper cites BLIP: Bootstrapping Language-Image Pre-training for Unified Vision- Language Understanding and Generation.

On the rankability of visual embeddings BLIP: Bootstrapping Language-Image Pre-training for Unified Vision- Language Understanding and Generation

Reference 31

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This paper cites OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression.

On the rankability of visual embeddings OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression

Reference 32

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This paper cites CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model.

On the rankability of visual embeddings CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model

Reference 33

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This paper cites Beyond comparing image pairs: Setwise active learning for relative attributes.

On the rankability of visual embeddings Beyond comparing image pairs: Setwise active learning for relative attributes

Reference 34

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This paper cites CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification.

On the rankability of visual embeddings CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification

Reference 35

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Observation ca8ccce3-3a66-418b-96d1-4e4cd7f48a02 · outbound

This paper cites ClipCap: CLIP Prefix for Image Captioning.

On the rankability of visual embeddings ClipCap: CLIP Prefix for Image Captioning

Reference 36

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This paper cites A V A: A Large-Scale Database for Aesthetic Visual Analysis.

On the rankability of visual embeddings A V A: A Large-Scale Database for Aesthetic Visual Analysis

Reference 37

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This paper cites A metric learning reality check.

On the rankability of visual embeddings A metric learning reality check

Reference 38

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This paper cites Parts of Speech-Grounded Subspaces in Vision-Language Models.

On the rankability of visual embeddings Parts of Speech-Grounded Subspaces in Vision-Language Models

Reference 39

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Observation 18e7cfd6-4087-4ef9-beec-b45a1a211e6a · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

On the rankability of visual embeddings DINOv2: Learning Robust Visual Features without Supervision

Reference 40

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This paper cites Teaching CLIP to Count to Ten.

On the rankability of visual embeddings Teaching CLIP to Count to Ten

Reference 41

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This paper cites Dating Historical Color Images.

On the rankability of visual embeddings Dating Historical Color Images

Reference 42

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This paper cites Relative Attributes.

On the rankability of visual embeddings Relative Attributes

Reference 43

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This paper cites HYDEN: Hyperbolic Density Representations for Medical Images and Re- ports.

On the rankability of visual embeddings HYDEN: Hyperbolic Density Representations for Medical Images and Re- ports

Reference 44

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This paper cites Learning Transferable Visual Models From Natural Language Supervision.

On the rankability of visual embeddings Learning Transferable Visual Models From Natural Language Supervision

Reference 45

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This paper cites Learning Transferable Visual Models From Natural Language Super- vision.

On the rankability of visual embeddings Learning Transferable Visual Models From Natural Language Super- vision

Reference 46

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This paper cites Steering Llama 2 via Contrastive Activation Addition.

On the rankability of visual embeddings Steering Llama 2 via Contrastive Activation Addition

Reference 47

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This paper cites Finetuning CLIP to Reason about Pairwise Differences.

On the rankability of visual embeddings Finetuning CLIP to Reason about Pairwise Differences

Reference 48

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This paper cites Improving Image Encoders for General-Purpose Nearest Neighbor Search and Classification.

On the rankability of visual embeddings Improving Image Encoders for General-Purpose Nearest Neighbor Search and Classification

Reference 49

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This paper cites Linear Representations of Sentiment in Large Language Models.

On the rankability of visual embeddings Linear Representations of Sentiment in Large Language Models

Reference 50

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This paper cites Linear Spaces of Meanings: Compositional Structures in Vision- Language Models.

On the rankability of visual embeddings Linear Spaces of Meanings: Compositional Structures in Vision- Language Models

Reference 51

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This paper cites Deep image prior.

On the rankability of visual embeddings Deep image prior

Reference 52

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This paper cites BEYOND DECODABILITY: LIN- EAR FEATURE SPACES ENABLE VISUAL COMPOSITIONAL GENERALIZATION.

On the rankability of visual embeddings BEYOND DECODABILITY: LIN- EAR FEATURE SPACES ENABLE VISUAL COMPOSITIONAL GENERALIZATION

Reference 53

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This paper cites Intermediate Layer Classifiers for OOD Generalization.

On the rankability of visual embeddings Intermediate Layer Classifiers for OOD Generalization

Reference 54

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This paper cites Order-Embeddings of Images and Language.

On the rankability of visual embeddings Order-Embeddings of Images and Language

Reference 55

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This paper cites Exploring CLIP for Assessing the Look and Feel of Images.

On the rankability of visual embeddings Exploring CLIP for Assessing the Look and Feel of Images

Reference 56

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This paper cites Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification.

On the rankability of visual embeddings Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification

Reference 57

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Observation e4732314-04f8-4044-ba8f-e92acfb33c58 · outbound

This paper cites Learning-to-rank meets language: Boosting language-driven ordering align- ment for ordinal classification.

On the rankability of visual embeddings Learning-to-rank meets language: Boosting language-driven ordering align- ment for ordinal classification

Reference 58

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Observation be34882b-7800-4c43-bc56-e814d8e5511c · outbound

This paper cites Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere.

On the rankability of visual embeddings Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

Reference 59

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This paper cites Disentangled representation learning.

On the rankability of visual embeddings Disentangled representation learning

Reference 60

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This paper cites ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders.

On the rankability of visual embeddings ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 61

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This paper cites CLIP Brings Better Features to Visual Aesthetics Learners.

On the rankability of visual embeddings CLIP Brings Better Features to Visual Aesthetics Learners

Reference 62

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This paper cites FILIP: Fine-grained Interactive Language-Image Pre-Training.

On the rankability of visual embeddings FILIP: Fine-grained Interactive Language-Image Pre-Training

Reference 63

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This paper cites Just noticeable differences in visual attributes.

On the rankability of visual embeddings Just noticeable differences in visual attributes

Reference 64

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This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

On the rankability of visual embeddings CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 65

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This paper cites RANKING-AWARE ADAPTER FOR TEXT-DRIVEN IMAGE OR- DERING WITH CLIP.

On the rankability of visual embeddings RANKING-AWARE ADAPTER FOR TEXT-DRIVEN IMAGE OR- DERING WITH CLIP

Reference 66

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On the rankability of visual embeddings Ranking-aware adapter for text-driven image ordering with CLIP

Reference 67

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This paper cites When and why vision-language models behave like bags-of-words, and what to do about it?.

On the rankability of visual embeddings When and why vision-language models behave like bags-of-words, and what to do about it?

Reference 68

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This paper cites Single-Image Crowd Counting via Multi-Column Convolutional Neural Network.

On the rankability of visual embeddings Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

Reference 69

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On the rankability of visual embeddings Unresolved cited work

Reference 70

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This paper cites Learning Ordinal Relationships for Mid-Level Vision.

On the rankability of visual embeddings Learning Ordinal Relationships for Mid-Level Vision

Reference 71

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This paper cites Age Progression/Regression by Conditional Adversarial Autoencoder.

On the rankability of visual embeddings Age Progression/Regression by Conditional Adversarial Autoencoder

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:11:51.767240Z

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This paper cites Linear Representations of Sentiment in Large Language Models.

On the rankability of visual embeddings Linear Representations of Sentiment in Large Language Models

Reference 2023

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This paper cites DOI: 10.1109/TIP.2020.2967829.

On the rankability of visual embeddings DOI: 10.1109/TIP.2020.2967829

Reference 4056

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