Papers with Code 아카이브
Video Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
- 254
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dvlab-research/llama-vid
공식 코드LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models
https://github.com/efficient-large-model/vila
공식 코드https://github.com/egoschema/egoschema
공식 코드EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding
https://github.com/elad-amrani/ssml
공식 코드Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning
https://github.com/engindeniz/vitis
공식 코드Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts
https://github.com/evolvinglmms-lab/lmms-eval
공식 코드https://github.com/evolvinglmms-lab/longva
공식 코드https://github.com/facebookresearch/TimeSformer
공식 코드Is Space-Time Attention All You Need for Video Understanding?
https://github.com/facebookresearch/causalvqa
공식 코드CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models
https://github.com/facebookresearch/egotom
공식 코드EgoToM: Benchmarking Theory of Mind Reasoning from Egocentric Videos
https://github.com/facebookresearch/perception_models
공식 코드PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding
https://github.com/facebookresearch/vjepa2
공식 코드V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
https://github.com/fanchenyou/HME-VideoQA
공식 코드Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering
https://github.com/fansunqi/akeys
공식 코드https://github.com/farewellthree/BT-Adapter
공식 코드BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning
https://github.com/farewellthree/ppllava
공식 코드PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance
https://github.com/georg3tom/RoadTextVQA
공식 코드https://github.com/gls0425/linvt
공식 코드LinVT: Empower Your Image-level Large Language Model to Understand Videos
https://github.com/google-research-datasets/egotempo
공식 코드Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos
https://github.com/google-research/scenic/tree/main/scenic/projects/verbs_in_action
공식 코드Verbs in Action: Improving verb understanding in video-language models
https://github.com/google/video-localized-narratives
공식 코드Connecting Vision and Language with Video Localized Narratives
https://github.com/gxym/stgt
공식 코드Video-Language Alignment via Spatio-Temporal Graph Transformer
https://github.com/haotian-liu/LLaVA
공식 코드https://github.com/henryhzy/vl-pet
공식 코드VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control
https://github.com/hoanganhpham1006/cost
공식 코드Video Dialog as Conversation about Objects Living in Space-Time
https://github.com/hon-wong/elysium
공식 코드Elysium: Exploring Object-level Perception in Videos via MLLM
https://github.com/hoplee6/sports-qa
공식 코드Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports
https://github.com/hritikbansal/videocon
공식 코드VideoCon: Robust Video-Language Alignment via Contrast Captions
https://github.com/hshjerry/videoespresso
공식 코드https://github.com/htsucml/VQAG
공식 코드End-to-End Video Question-Answer Generation with Generator-Pretester Network
https://github.com/huangb23/vtimellm
공식 코드https://github.com/hucvl/craft
공식 코드CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions
https://github.com/hyounghk/VideoQADenseCapFrameGate-ACL2020
공식 코드Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA
https://github.com/hyu-zhang/hcqa
공식 코드https://github.com/ictnlp/llava-mini
공식 코드LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.