A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets | Paper Citation Record

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This paper cites GPT-4 Technical Report.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets GPT-4 Technical Report

Reference 1

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This paper cites Vismin: Visual minimal-change understanding.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Vismin: Visual minimal-change understanding

Reference 2

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This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 3

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This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 4

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This paper cites Clip2scene: Towards label-efficient 3d scene understanding by clip.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Clip2scene: Towards label-efficient 3d scene understanding by clip

Reference 5

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This paper cites A simple framework for contrastive learning of visual representations.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets A simple framework for contrastive learning of visual representations

Reference 6

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This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 7

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This paper cites InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Reference 8

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This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 9

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This paper cites Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 10

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This paper cites InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model

Reference 11

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This paper cites Dense and aligned captions (dac) promote compositional reasoning in vl models.Advances in Neural Information Processing Systems, 36:76137–76150, 2023.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Dense and aligned captions (dac) promote compositional reasoning in vl models.Advances in Neural Information Processing Systems, 36:76137–76150, 2023

Reference 12

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This paper cites Teaching structured vision & language concepts to vision & language models.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Teaching structured vision & language concepts to vision & language models

Reference 13

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This paper cites Dense and aligned captions (dac) promote compositional reasoning in vl models.Advances in Neural Information Processing Systems, 36, 2024.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Dense and aligned captions (dac) promote compositional reasoning in vl models.Advances in Neural Information Processing Systems, 36, 2024

Reference 14

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This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 15

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This paper cites Advances in deep concealed scene understanding.Visual Intelligence, 1(1):16,.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Advances in deep concealed scene understanding.Visual Intelligence, 1(1):16,

Reference 16

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This paper cites Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance

Reference 17

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This paper cites Visual program- ming: Compositional visual reasoning without training.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Visual program- ming: Compositional visual reasoning without training

Reference 18

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This paper cites Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality.Advances in neural information processing systems, 36, 2024.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality.Advances in neural information processing systems, 36, 2024

Reference 19

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets LoRA: Low-Rank Adaptation of Large Language Models

Reference 20

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This paper cites Semantic to Structure: Learning Structural Representations for Infringement Detection.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Semantic to Structure: Learning Structural Representations for Infringement Detection

Reference 21

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This paper cites Secret lies in color: Enhancing ai-generated images detection with color distribution anal- ysis.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Secret lies in color: Enhancing ai-generated images detection with color distribution anal- ysis

Reference 22

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This paper cites Elevater: A benchmark and toolkit for evaluating language-augmented visual models.Advances in Neural Information Processing Systems, 35:9287–9301,.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Elevater: A benchmark and toolkit for evaluating language-augmented visual models.Advances in Neural Information Processing Systems, 35:9287–9301,

Reference 23

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This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 24

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This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 25

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This paper cites Vila: On pre-training for vi- sual language models.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Vila: On pre-training for vi- sual language models

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This paper cites Visual instruction tuning.Advances in neural information processing systems, 36, 2024.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Visual instruction tuning.Advances in neural information processing systems, 36, 2024

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This paper cites MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

Reference 28

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This paper cites Synthesize diagnose and optimize: Towards fine- grained vision-language understanding.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Synthesize diagnose and optimize: Towards fine- grained vision-language understanding

Reference 29

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This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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This paper cites Learning transferable visual models from natural language supervi- sion.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Learning transferable visual models from natural language supervi- sion

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

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 32

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This paper cites Flava: A foundational language and vision alignment model.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Flava: A foundational language and vision alignment model

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This paper cites Yfcc100m: The new data in multimedia research.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Yfcc100m: The new data in multimedia research

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This paper cites Winoground: Probing vision and language models for visio- linguistic compositionality.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Winoground: Probing vision and language models for visio- linguistic compositionality

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This paper cites A picture is worth more than 77 text tokens: Evaluating clip-style models on dense captions.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets A picture is worth more than 77 text tokens: Evaluating clip-style models on dense captions

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This paper cites xgen-mm (blip-3): A family of open large multimodal models.arXiv preprint arXiv:2408.08872, 2024.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets xgen-mm (blip-3): A family of open large multimodal models.arXiv preprint arXiv:2408.08872, 2024

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This paper cites What you see is what you read? improving text- image alignment evaluation.Advances in Neural Informa- tion Processing Systems, 36, 2024.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets What you see is what you read? improving text- image alignment evaluation.Advances in Neural Informa- tion Processing Systems, 36, 2024

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This paper cites When and why vision- language models behave like bags-of-words, and what to do about it? InThe Eleventh International Conference on Learning Representations, 2023.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets When and why vision- language models behave like bags-of-words, and what to do about it? InThe Eleventh International Conference on Learning Representations, 2023

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This paper cites Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts

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This paper cites Investigating compositional chal- lenges in vision-language models for visual grounding.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Investigating compositional chal- lenges in vision-language models for visual grounding

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This paper cites Sigmoid loss for language image pre-training.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets Sigmoid loss for language image pre-training

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This paper cites VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations

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This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

A Visual Leap in CLIP Compositionality Reasoning through Generation of Counterfactual Sets MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

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