https://github.com/insomnia94/FTMU
공식 코드Fast Template Matching and Update for Video Object Tracking and Segmentation
Papers with Code 아카이브
Scene Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 3
- 보존된 평가 기록
- 5
Scene understanding involves interpreting the visual information of a scene, including objects, their spatial relationships, and the overall layout. It goes beyond simple object recognition by considering the context and how objects relate to each other and the environment.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/instance-based-loc/instance-based-loc
공식 코드Towards Global Localization using Multi-Modal Object-Instance Re-Identification
https://github.com/intellisensing/rs-agent
공식 코드RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent
https://github.com/isee-laboratory/referdino-plus
공식 코드ReferDINO-Plus: 2nd Solution for 4th PVUW MeViS Challenge at CVPR 2025
https://github.com/isunchy/3d_instance_segmentation
공식 코드Semantic Segmentation-Assisted Instance Feature Fusion for Multi-Level 3D Part Instance Segmentation
https://github.com/ivanDonadello/Visual-Relationship-Detection-LTN
공식 코드Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation
https://github.com/ivy-lvlm/counterfactual-inception
공식 코드https://github.com/jamalilab/sam4em
공식 코드https://github.com/jamycheung/trans4pass
공식 코드Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation
https://github.com/jayparanjape/f-vita
공식 코드F-ViTA: Foundation Model Guided Visible to Thermal Translation
https://github.com/jeeseung-park/viplo
공식 코드https://github.com/jeremy-chopin/apacosi
공식 코드Model-based inexact graph matching on top of CNNs for semantic scene understanding
https://github.com/jerrysunning/drrnet
공식 코드DRRNet: Macro-Micro Feature Fusion and Dual Reverse Refinement for Camouflaged Object Detection
https://github.com/jerryx1110/robust-video-object-segmentation
공식 코드Towards Robust Video Object Segmentation with Adaptive Object Calibration
https://github.com/jerryx1110/rpcmvos
공식 코드Reliable Propagation-Correction Modulation for Video Object Segmentation
https://github.com/jesusbermudezcameo/noncentralindoordataset
공식 코드https://github.com/jfzhuang/DAVSS
공식 코드Video Semantic Segmentation with Distortion-Aware Feature Correction
https://github.com/jfzhuang/st_memory
공식 코드Video Semantic Segmentation with Inter-Frame Feature Fusion and Inner-Frame Feature Refinement
https://github.com/jhcho99/coformer
공식 코드Collaborative Transformers for Grounded Situation Recognition
https://github.com/jhcho99/gsrtr
공식 코드https://github.com/jhl-det/rgbd_video_sod
공식 코드ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection
https://github.com/jianzongwu/awesome-open-vocabulary
공식 코드https://github.com/jiawen-zhu/hqtrack
공식 코드https://github.com/jiawen-zhu/trackgpt
공식 코드https://github.com/jieqianyu/ssc-rs
공식 코드SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion
https://github.com/jieqin-ai/amr
공식 코드Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation
https://github.com/jimmy-dq/dropmae
공식 코드DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks
https://github.com/jingkang50/openpvsg
공식 코드https://github.com/jingkang50/psg4d
공식 코드https://github.com/jinglinglingling/x4d
공식 코드https://github.com/jinlab-imvr/surgical-sam-2
공식 코드Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning
https://github.com/jinyangyuan/acquired-deep-impressions
공식 코드Knowledge-Guided Object Discovery with Acquired Deep Impressions
https://github.com/jokester-zzz/ssa-sc
공식 코드Semantic Segmentation-assisted Scene Completion for LiDAR Point Clouds
https://github.com/juanb09111/semantic_depth
공식 코드SemSegDepth: A Combined Model for Semantic Segmentation and Depth Completion
https://github.com/julilien/PLDepth
공식 코드Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model
https://github.com/juntang-zhuang/ShelfNet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.