LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models | Paper Citation Record

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This paper cites Flamingo: a visual language model for few-shot learning.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Flamingo: a visual language model for few-shot learning

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Ae: A domain-agnostic platform for adaptive experimentation

Reference 2

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Food-101--mining discriminative components with random forests

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 4

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This paper cites An empirical study and analysis of generalized zero-shot learning for object recognition in the wild.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models An empirical study and analysis of generalized zero-shot learning for object recognition in the wild

Reference 5

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This paper cites Pixart- : Fast training of diffusion transformer for photorealistic text-to-image synthesis, 2023.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Pixart- : Fast training of diffusion transformer for photorealistic text-to-image synthesis, 2023

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This paper cites Robustsam: Segment anything robustly on degraded images.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Robustsam: Segment anything robustly on degraded images

Reference 7

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This paper cites PaLI: A Jointly-Scaled Multilingual Language-Image Model.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models PaLI: A Jointly-Scaled Multilingual Language-Image Model

Reference 8

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Learning resolution-invariant deep representations for person re-identification

Reference 9

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Low-resolution face recognition

Reference 10

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Describing textures in the wild

Reference 11

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Mevid: Multi-view extended videos with identities for video person re-identification

Reference 12

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Imagenet: A large-scale hierarchical image database

Reference 13

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Bert: Pre-training of deep bidirectional transformers for language understanding

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models An image is worth 16x16 words: Transformers for image recognition at scale

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Data Filtering Networks

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models DataComp: In search of the next generation of multimodal datasets

Reference 17

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Implicit diffusion models for continuous super-resolution

Reference 18

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This paper cites Efficient low-resolution face recognition via bridge distillation.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Efficient low-resolution face recognition via bridge distillation

Reference 19

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Imagebind: One embedding space to bind them all

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Caltech-256 object category dataset

Reference 21

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining

Reference 22

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Audioclip: Extending clip to image, text and audio

Reference 23

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Deep residual learning for image recognition

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models The many faces of robustness: A critical analysis of out-of-distribution generalization

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Natural adversarial examples

Reference 27

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Openclip, July 2021

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Visual prompt tuning

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Resolution invariant face recognition using a distillation approach

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Segment anything

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Similarity of neural network representations revisited

Reference 32

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Lsun-stanford car dataset: enhancing large-scale car image datasets using deep learning for usage in gan training

Reference 33

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Visual genome: Connecting language and vision using crowdsourced dense image annotations

Reference 34

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Elevater: A benchmark and toolkit for evaluating language-augmented visual models

Reference 35

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Selvaraju, Akhilesh Deepak Gotmare, Shafiq Joty, Caiming Xiong, and Steven Hoi

Reference 36

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 37

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Face Recognition in Low Quality Images: A Survey

Reference 38

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Unresolved cited work

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models RECLIP : Resource-efficient CLIP by training with small images

Reference 40

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models An inverse scaling law for clip training

Reference 41

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This paper cites CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a \$10,000 Budget; An Extra \$4,000 Unlocks 81.8% Accuracy.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a \$10,000 Budget; An Extra \$4,000 Unlocks 81.8% Accuracy

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Swinir: Image restoration using swin transformer

Reference 43

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Microsoft coco: Common objects in context

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Frozen clip models are efficient video learners

Reference 45

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Few-shot Adaptation of Multi-modal Foundation Models: A Survey

Reference 46

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Flower classification via convolutional neural network

Reference 47

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This paper cites Luevano, Leonardo Chang, Heydi Méndez-Vázquez, Yoanna Martínez-Díaz, and Miguel González-Mendoza.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Luevano, Leonardo Chang, Heydi Méndez-Vázquez, Yoanna Martínez-Díaz, and Miguel González-Mendoza

Reference 48

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This paper cites Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning

Reference 49

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Fine-Grained Visual Classification of Aircraft

Reference 50

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This paper cites End-to-end learning of visual representations from uncurated instructional videos.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models End-to-end learning of visual representations from uncurated instructional videos

Reference 51

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This paper cites Rethinking Image Super-Resolution from Training Data Perspectives.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Rethinking Image Super-Resolution from Training Data Perspectives

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This paper cites Im2text: Describing images using 1 million captioned photographs.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Im2text: Describing images using 1 million captioned photographs

Reference 53

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Cats and dogs

Reference 54

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This paper cites Patil, R.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Patil, R

Reference 55

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This paper cites UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images

Reference 56

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This paper cites Language models are unsupervised multitask learners.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Language models are unsupervised multitask learners

Reference 57

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This paper cites Learning transferable visual models from natural language supervision.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Learning transferable visual models from natural language supervision

Reference 58

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This paper cites Do imagenet classifiers generalize to imagenet? In International conference on machine learning, pp.\ 5389--5400.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Do imagenet classifiers generalize to imagenet? In International conference on machine learning, pp.\ 5389--5400

Reference 59

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Probing Conceptual Understanding of Large Visual-Language Models

Reference 60

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Observation 26c73f49-f8c9-4627-bfe5-ecdef180b976 · outbound

This paper cites Robustness analysis on foundational segmentation models.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Robustness analysis on foundational segmentation models

Reference 61

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Observation 7a6914b8-a951-4be5-a78c-17d46fe5a410 · outbound

This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 62

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 63

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Dafnis, Zhixing Zhang, Shiyu Zhao, and Dimitris Metaxas

Reference 64

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 65

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Test-time prompt tuning for zero-shot generalization in vision-language models

Reference 66

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 67

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 68

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Reference 69

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models LLaMA: Open and Efficient Foundation Language Models

Reference 70

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This paper cites Attention is all you need.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Attention is all you need

Reference 71

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This paper cites Learning robust global representations by penalizing local predictive power.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Learning robust global representations by penalizing local predictive power

Reference 72

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This paper cites Magneto: A foundation transformer.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Magneto: A foundation transformer

Reference 73

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Esrgan: Enhanced super-resolution generative adversarial networks

Reference 74

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision

Reference 75

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Zero-shot learning-the good, the bad and the ugly

Reference 76

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation

Reference 77

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Demystifying CLIP data

Reference 78

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This paper cites Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models

Reference 79

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This paper cites What is Next in Multimodal Foundation Models?.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models What is Next in Multimodal Foundation Models?

Reference 80

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This paper cites Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion Transformer.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion Transformer

Reference 81

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Coca: Contrastive captioners are image-text foundation models

Reference 82

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Observation cac0fc08-1e60-4162-9005-3ea0511a46bd · outbound

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Sigmoid loss for language image pre-training

Reference 83

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This paper cites Designing a practical degradation model for deep blind image super-resolution.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Designing a practical degradation model for deep blind image super-resolution

Reference 84

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This paper cites A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene

Reference 85

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Regionclip: Region-based language-image pretraining

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:12:38.359831Z

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This paper cites Learning deep features for scene recognition using places database.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Learning deep features for scene recognition using places database

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:12:38.340421Z

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This paper cites Privacy-sensitive objects pixelation for live video streaming.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Privacy-sensitive objects pixelation for live video streaming

Reference 88

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Observation 0d81ccaf-12b9-480b-a4e8-e5da3fdafc33 · outbound

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models write newline

Reference 89

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This paper cites @esa (Ref.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models @esa (Ref

Reference 90

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LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Unresolved cited work

Reference 91

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This paper cites Text Encoders are mostly modified vanilla transformers (Tran.) NIPS2017_3f5ee243.

LR0.FM: Low-Res Benchmark and Improving Robustness for Zero-Shot Classification in Foundation Models Text Encoders are mostly modified vanilla transformers (Tran.) NIPS2017_3f5ee243

Reference 92

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