https://github.com/ThiagoCF05/NeuralREG
공식 코드NeuralREG: An end-to-end approach to referring expression generation
Papers with Code 아카이브
Referring expression generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 2
- 보존된 평가 기록
- 34
Generate referring expressions
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ai4colonoscopy/intelliscope
공식 코드https://github.com/baai-dcai/bunny
공식 코드https://github.com/clause-bielefeld/reg-scene-context
공식 코드Resilience through Scene Context in Visual Referring Expression Generation
https://github.com/clp-research/pento-diaref
공식 코드https://github.com/dvlab-research/minigemini
공식 코드Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
https://github.com/haotian-liu/LLaVA
공식 코드https://github.com/hon-wong/elysium
공식 코드Elysium: Exploring Object-level Perception in Videos via MLLM
https://github.com/jwh97nn/anyref
공식 코드Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perception
https://github.com/lichengunc/refer
공식 코드https://github.com/mars-tin/pixrefer
공식 코드Vision-Language Models Are Not Pragmatically Competent in Referring Expression Generation
https://github.com/mbzuai-oryx/groundingLMM
공식 코드https://github.com/mcao610/ProfileREG
공식 코드https://github.com/meituan-automl/mobilevlm
공식 코드MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices
https://github.com/microsoft/unilm/tree/master/kosmos-2
공식 코드Kosmos-2: Grounding Multimodal Large Language Models to the World
https://github.com/superhero-7/ireg
공식 코드Whether you can locate or not? Interactive Referring Expression Generation
https://github.com/tsinghua-msiip/uni-med
공식 코드Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE
https://github.com/willemsenbram/a-game-of-sorts
공식 코드https://github.com/willemsenbram/reg-with-guiding
공식 코드https://github.com/zinengtang/MulAgentRef
공식 코드Grounding Language in Multi-Perspective Referential Communication
https://github.com/AmitMY/chimera
Improving Quality and Efficiency in Plan-based Neural Data-to-Text Generation
https://github.com/GuessWhatGame/referit
https://github.com/LLaVA-VL/LLaVA-NeXT
https://github.com/ZhangYiqun018/StickerConv
https://github.com/albertotestoni/ndq_visual_objects
https://github.com/camenduru/llava-colab
https://github.com/computer-vision-in-the-wild/cvinw_readings
https://github.com/dinhvietcuong1996/icme25-inova
https://github.com/dvlab-research/MGM
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
https://github.com/fuqianya/ViLBERT-Paddle
https://github.com/huggingface/transformers
https://github.com/linzhiqiu/clip-flant5
https://github.com/llava-annonymous/llava
https://github.com/microsoft/LLaVA-Med
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
https://github.com/qiujihao19/artemis
https://github.com/rabiulcste/vqazero
Kosmos-2: Grounding Multimodal Large Language Models to the World
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.