Results for “vision-language”
28 skills
jiachen-t-wang longva-long-context-transfer-from-language-to-vision-arxiv-2
LongVA: Long Context Transfer from Language to Vision
6
tianhao909 llava
Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.
1 · bundle
qcmuu llava
Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.
0 · bundle
jackychenlu llava
Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.
0 · bundle
More results
qcmuu blip-2-vision-language
Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.
0 · bundle
tianhao909 blip-2-vision-language
Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.
1 · bundle
thedixitjain llava
Vision-language chat: VQA, captioning, image dialogue.
2 · bundle
orchestra-research blip-2-vision-language
Generate image captions, answer visual questions, and perform image-text retrieval using BLIP-2's Q-Former architecture with frozen vision encoders and LLMs.
10.4k · bundle
jiachen-t-wang nlvr2-a-visual-reasoning-benchmark-for-natural-language-arxi
NLVR2: A Visual Reasoning Benchmark for Natural Language
6
orchestra-research llava
Enables visual instruction tuning and image-based conversations using open-source vision-language models. Supports multi-turn image chat, visual question answering, and image understanding tasks.
10.4k · bundle
lord1egypt llava
Runs the open-source LLaVA vision-language model for image understanding, captioning, visual question answering, and multi-turn image conversations, including setup, inference, and training guidance.
2
jiachen-t-wang glip-grounded-language-image-pre-training-arxiv-2112-03857v2
GLIP: Grounded Language-Image Pre-training
6
jiachen-t-wang vila-on-pre-training-for-visual-language-models-arxiv-2312-0
VILA: On Pre-training for Visual Language Models
6
jiachen-t-wang cogvlm-visual-expert-for-pretrained-language-models-arxiv-23
CogVLM: Visual Expert for Pretrained Language Models
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jiachen-t-wang coco-microsoft-coco-common-objects-in-context-arxiv-1405-031
COCO: Microsoft COCO: Common Objects in Context
6
jiachen-t-wang dreamlip-language-image-pre-training-with-long-captions-arxi
DreamLIP: Language-Image Pre-training with Long Captions
6
jiachen-t-wang nocaps-novel-object-captioning-at-scale-arxiv-1812-08658v2
Nocaps: Novel Object Captioning at Scale
6
lionelndong visual-package
Build a visual sequence that proves, explains, and supports decisions.
0
jiachen-t-wang textvqa-towards-reasoning-about-text-in-images-arxiv-1904-08
TextVQA: Towards Reasoning about Text in Images
6
jiachen-t-wang open-vocabulary-object-detection-using-captions-arxiv-2011-1
Open-Vocabulary Object Detection Using Captions
6
jiachen-t-wang multimodal-few-shot-learning-with-frozen-language-models-arx
Multimodal Few-Shot Learning with Frozen Language Models
6
jiachen-t-wang improved-baselines-with-visual-instruction-tuning-arxiv-2310
Improved Baselines with Visual Instruction Tuning
6
lionelndong visuals-adversarial
Skeptical pushback on visual placement — both density and quality. Reads the annotated outline plus the visuals manifest and asks (a) whether the article hits the density target from editorial-principles-visuals.md, (b) whether each [VISUAL:...] earns its place, (c) whether sections without one would benefit. One revision pass on FAIL (BLOG_AGENT_VISUALS_REVISION_BUDGET, default 1).
0
jiachen-t-wang sigmoid-loss-for-language-image-pre-training-arxiv-2303-1534
Sigmoid Loss for Language Image Pre-Training
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jiachen-t-wang visual-instruction-tuning-arxiv-2304-08485v2
Visual Instruction Tuning
6
jiachen-t-wang mantis-interleaved-multi-image-instruction-tuning-arxiv-2405
Mantis: Interleaved Multi-Image Instruction Tuning
6
jiachen-t-wang towards-open-world-segmentation-of-parts-arxiv-2305-06914v3
Towards Open-World Segmentation of Parts
6
jiachen-t-wang imagenet-a-large-scale-hierarchical-image-database-crossref-
ImageNet: A Large-Scale Hierarchical Image Database
6