https://github.com/pwc-1/Paper-9/tree/main/llava_next
LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models
Papers with Code 아카이브
Video Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/qirui-chen/MultiHop-EgoQA
https://github.com/qiujihao19/artemis
https://github.com/qq326823564/LSDNN
MVB: A Large-Scale Dataset for Baggage Re-Identification and Merged Siamese Networks
https://github.com/qwenlm/qwen2.5-vl
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/ruc-aimc-lab/nt2vr
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
https://github.com/saccharomycetes/text-based-traffic-understanding
https://github.com/sczwangxiao/tsgvs-mm2023
RTQ: Rethinking Video-language Understanding Based on Image-text Model
https://github.com/skunkworksai/bakllava
https://github.com/skyworkai/moe-plus-plus
https://github.com/skyworkai/moh
https://github.com/sshh12/multi_token
https://github.com/stanfordvl/atp-video-language
https://github.com/sunsmarterjie/chatterbox
https://github.com/tabtoyou/kollava
https://github.com/tanveer81/rgnet
https://github.com/towhee-io/towhee
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/tutujingyugang1/ChatVLA_public
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/tychen-SJTU/MECD-Benchmark
MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning
https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models
https://github.com/voxmenthe/OmniNet_1
OmniNet: A unified architecture for multi-modal multi-task learning
https://github.com/wayveai/driving-with-llms
https://github.com/wuyuejinxia/prcv2019-mvb-renet
MVB: A Large-Scale Dataset for Baggage Re-Identification and Merged Siamese Networks
https://github.com/wxh1996/VideoAgent
VideoAgent: Long-form Video Understanding with Large Language Model as Agent
https://github.com/xinding-sys/StreamMind
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/yangyucheng000/University/tree/main/model-3/qwen2_vl
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/yingsen1/unimd
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
https://github.com/yiyixuxu/TimeSformer-rolled-attention
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/yonseivnl/vlm-rlaif
ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPO
https://github.com/zengyan-97/x-vlm
X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
https://github.com/zyang1580/binllm
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://gitlab.com/grounded-sport-convai/goal-baselines
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.