https://github.com/alice01010101/tasu
공식 코드In-the-wild Audio Spatialization with Flexible Text-guided Localization
Papers with Code 아카이브
Visual Question Answering (VQA)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.
Image Source: visualqa.org
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/alignmentresearch/vlmrm
공식 코드Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning
https://github.com/alirezasalemi7/dedr-mm-fid
공식 코드https://github.com/alirezasalemi7/pretraining-multimodal-dense-retriever-for-okvqa
공식 코드Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering
https://github.com/allenai/ai2thor
공식 코드https://github.com/allenai/aokvqa
공식 코드A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge
https://github.com/allenai/close
공식 코드I Can't Believe There's No Images! Learning Visual Tasks Using only Language Supervision
https://github.com/allenai/dqa-net
공식 코드https://github.com/allenai/gpv-1
공식 코드https://github.com/allenai/grit_official
공식 코드https://github.com/allenai/reclip
공식 코드ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension
https://github.com/allenai/visual-reasoning-rationalization
공식 코드https://github.com/allenai/x-lxmert
공식 코드X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers
https://github.com/alpha-vllm/llama2-accessory
공식 코드https://github.com/alpha-vllm/lumina-mgpt
공식 코드https://github.com/amazon-research/mix-generation
공식 코드https://github.com/amazon-science/camml
공식 코드https://github.com/amd-aig-aima/amd-hummingbird-t2v
공식 코드https://github.com/amiroor/d3questiongenerationclevr
공식 코드D3: Data Diversity Design for Systematic Generalization in Visual Question Answering
https://github.com/amitakamath/whatsup_vlms
공식 코드What's "up" with vision-language models? Investigating their struggle with spatial reasoning
https://github.com/andersonstra/mukea
공식 코드https://github.com/andst/text_guided_cl
공식 코드https://github.com/ankeshanand/blindfold-baselines-eqa
공식 코드https://github.com/anonymousanoy/fohe
공식 코드https://github.com/anranwu-richpo/dcqa
공식 코드https://github.com/anthonyhuo/sam-dam-for-compositional-reasoning
공식 코드Composition Vision-Language Understanding via Segment and Depth Anything Model
https://github.com/antoyang/FrozenBiLM
공식 코드Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
https://github.com/antoyang/just-ask
공식 코드Just Ask: Learning to Answer Questions from Millions of Narrated Videos
https://github.com/antresearchnlp/vilasr
공식 코드Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing
https://github.com/apple/ml-ferret
공식 코드Ferret: Refer and Ground Anything Anywhere at Any Granularity
https://github.com/apple/ml-slowfast-llava
공식 코드SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
https://github.com/appletea233/al-ref-sam2
공식 코드https://github.com/applicaai/multi-property-extraction
공식 코드From Dataset Recycling to Multi-Property Extraction and Beyond
https://github.com/archiki/repare
공식 코드Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models
https://github.com/arctanxarc/mc-llava
공식 코드https://github.com/art-programmer/FloorNet
공식 코드FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.