https://github.com/txh-mercury/vast
공식 코드VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ty-97/seletive-volume-mix
공식 코드https://github.com/tychen-SJTU/MECD-Benchmark
공식 코드MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning
https://github.com/typistchen/spikmamba
공식 코드SpikMamba: When SNN meets Mamba in Event-based Human Action Recognition
https://github.com/tzhhhh123/HC-STVG
공식 코드Human-centric Spatio-Temporal Video Grounding With Visual Transformers
https://github.com/uark-aicv/aoe-net
공식 코드https://github.com/uark-aicv/video_representation
공식 코드Contextual Explainable Video Representation: Human Perception-based Understanding
https://github.com/uark-cviu/DyGLIP
공식 코드https://github.com/uark-cviu/direcformer
공식 코드DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition
https://github.com/ubisoftinc/Ubisoft-LaForge-Animation-Dataset
공식 코드https://github.com/ucf-crcv/tf-covr
공식 코드From Play to Replay: Composed Video Retrieval for Temporally Fine-Grained Videos
https://github.com/ucf-sst-lab/aicity2024cvprw
공식 코드Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis
https://github.com/uchiyama33/aosa
공식 코드Adaptive occlusion sensitivity analysis for visually explaining video recognition networks
https://github.com/ucsc-vlaa/image-pretraining-for-video
공식 코드In Defense of Image Pre-Training for Spatiotemporal Recognition
https://github.com/uiuctml/TaskVectorBasis
공식 코드Efficient Model Editing with Task Vector Bases: A Theoretical Framework and Scalable Approach
https://github.com/umarkhalidai/cefhri-efficient-federated-learning
공식 코드https://github.com/una-dinosauria/human-motion-prediction
공식 코드https://github.com/unabletousegit/videox22l
공식 코드Task-Aware KV Compression For Cost-Effective Long Video Understanding
https://github.com/uncbiag/livos
공식 코드LiVOS: Light Video Object Segmentation with Gated Linear Matching
https://github.com/uniadapter/uniadapter
공식 코드UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
https://github.com/uniqzheng/hfr-bvqa
공식 코드FAVER: Blind Quality Prediction of Variable Frame Rate Videos
https://github.com/universome/stylegan-v
공식 코드StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2
https://github.com/uolmm/tpqi-vqa
공식 코드Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment
https://github.com/uppaal/detox-edit
공식 코드Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity
https://github.com/usaywook/gin
공식 코드GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Driving
https://github.com/ustc-imcl/envc
공식 코드Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression
https://github.com/ut-vision/actionvos
공식 코드https://github.com/utaustin-swarmlab/neuro-symbolic-agent-challenge
공식 코드https://github.com/utaustin-swarmlab/temporal-logic-video-dataset
공식 코드https://github.com/uzh-rpg/rpg_timelens
공식 코드https://github.com/v-iashin/BMT
공식 코드A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer
https://github.com/v0lta/Complex-gated-recurrent-neural-networks
공식 코드https://github.com/v7labs/xmem_icl
공식 코드Flexible visual prompts for in-context learning in computer vision
https://github.com/vaibhavsaxena11/cwvae
공식 코드https://github.com/vaidehi99/infodeletionattacks
공식 코드Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks
https://github.com/vaishnavihimakunthala/vip
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.