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Video Understanding
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A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bytedance/valley
공식 코드Valley2: Exploring Multimodal Models with Scalable Vision-Language Design
https://github.com/bytedance/video-salmonn-2
공식 코드video-SALMONN 2: Captioning-Enhanced Audio-Visual Large Language Models
https://github.com/carolchenyx/mgfn
공식 코드MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
https://github.com/ceezh/llovi
공식 코드A Simple LLM Framework for Long-Range Video Question-Answering
https://github.com/ceezh/silvr
공식 코드https://github.com/cg1177/videollm
공식 코드VideoLLM: Modeling Video Sequence with Large Language Models
https://github.com/chaofengc/iqa-pytorch
공식 코드TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
https://github.com/chaoma99/sr-metric
공식 코드Learning a No-Reference Quality Metric for Single-Image Super-Resolution
https://github.com/chaoyuaw/lvu
공식 코드https://github.com/charlotte-charmlab/fibottention
공식 코드Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across Heads
https://github.com/chenhaoxing/DeMamba
공식 코드DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark
https://github.com/chihyaoma/Activity-Recognition-with-CNN-and-RNN
공식 코드TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
https://github.com/chuhanxx/helping_hand_for_egocentric_videos
공식 코드Helping Hands: An Object-Aware Ego-Centric Video Recognition Model
https://github.com/cioppaanthony/context-aware-loss
공식 코드A Context-Aware Loss Function for Action Spotting in Soccer Videos
https://github.com/clh124/LMM-PVQA
공식 코드https://github.com/codnjsqkr/FastAno_official
공식 코드FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation
https://github.com/congvvc/instructseg
공식 코드InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language Models
https://github.com/contrastive/freevideollm
공식 코드Free Video-LLM: Prompt-guided Visual Perception for Efficient Training-free Video LLMs
https://github.com/cool-xuan/bn-wvad
공식 코드https://github.com/coolbay/Dr2Net
공식 코드Dr$^2$Net: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning
https://github.com/csjihwanh/soccernet-mlv
공식 코드https://github.com/cti-vision/perr
공식 코드Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark
https://github.com/cvlab-columbia/hyperfuture
공식 코드https://github.com/cvlab-yonsei/MNAD
공식 코드https://github.com/damo-nlp-sg/video-llama
공식 코드Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/damo-nlp-sg/videollama3
공식 코드VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding
https://github.com/damo-nlp-sg/videorefer
공식 코드VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM
https://github.com/daooshee/hd-vg-130m
공식 코드Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation
https://github.com/dasongli1/snapugc_engagement
공식 코드https://github.com/datadrivenwheels/scvlm
공식 코드ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding
https://github.com/datamllab/autovideo
공식 코드https://github.com/daveishan/spact
공식 코드SPAct: Self-supervised Privacy Preservation for Action Recognition
https://github.com/daveishan/timebalance
공식 코드https://github.com/declare-lab/sas-vqa
공식 코드Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models
https://github.com/declare-lab/sealing
공식 코드Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models
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