https://github.com/thufdd/multi-mode-fault-diagnosis-datasets-with-te-process
공식 코드An Evidential Real-Time Multi-Mode Fault Diagnosis Approach Based on Broad Learning System
Papers with Code 아카이브
Image Captioning
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- 하위 태스크
- 8
- 평가표
- 34
- 보존된 평가 기록
- 419
Image Captioning is the task of describing the content of an image in words. This task lies at the intersection of computer vision and natural language processing. Most image captioning systems use an encoder-decoder framework, where an input image is encoded into an intermediate representation of the information in the image, and then decoded into a descriptive text sequence. The most popular benchmarks are nocaps and COCO, and models are typically evaluated according to a BLEU or CIDER metric.
( Image credit: Reflective Decoding Network for Image Captioning, ICCV'19)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/thunlp/SICOG
공식 코드https://github.com/tianhuilab/falcon
공식 코드https://github.com/tingxueronghua/dpke
공식 코드Dual-Perspective Knowledge Enrichment for Semi-Supervised 3D Object Detection
https://github.com/tingyaohsu/scicap
공식 코드https://github.com/tingyu215/vacnic
공식 코드https://github.com/tjy1423317192/PLF
공식 코드Less is More: Pseudo-Label Filtering for Continual Test-Time Adaptation
https://github.com/togethercomputer/dragonfly
공식 코드Dragonfly: Multi-Resolution Zoom-In Encoding Enhances Vision-Language Models
https://github.com/tohinz/semantic-object-accuracy-for-generative-text-to-image-synthesis
공식 코드Semantic Object Accuracy for Generative Text-to-Image Synthesis
https://github.com/tokkiwa/imagetextcoding
공식 코드LMM-driven Semantic Image-Text Coding for Ultra Low-bitrate Learned Image Compression
https://github.com/tong-zeng/dvbench
공식 코드https://github.com/tpzou/hgl
공식 코드HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud Segmentation
https://github.com/tree985/treble-counterfactual-vlms
공식 코드Treble Counterfactual VLMs: A Causal Approach to Hallucination
https://github.com/trquhuytin/TOT-CVPR22
공식 코드Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering
https://github.com/trumpetertimes/sparse-generation
공식 코드https://github.com/tsenghungchen/show-adapt-and-tell
공식 코드Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner
https://github.com/tsosea2/marginmatch
공식 코드MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins
https://github.com/ttengwang/caption-anything
공식 코드Caption Anything: Interactive Image Description with Diverse Multimodal Controls
https://github.com/tuyunbin/nct
공식 코드https://github.com/txh-mercury/vast
공식 코드VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
https://github.com/tzjtatata/myriad
공식 코드Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection
https://github.com/uark-aicv/fg-cxr
공식 코드https://github.com/uber/ludwig
공식 코드https://github.com/uc-cdis/label-boosted-RAG-for-RRG
공식 코드LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report Generation
https://github.com/ucas-vg/groupsampling
공식 코드Rethinking Sampling Strategies for Unsupervised Person Re-identification
https://github.com/ucdvision/cmsf
공식 코드Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning
https://github.com/ucsc-real/disparate-ssl
공식 코드The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
https://github.com/uestc-nnlab/gat
공식 코드Geometry Attention Transformer with Position-aware LSTMs for Image Captioning
https://github.com/ukyh/RemovingSpuriousAlignment
공식 코드https://github.com/ukyh/switch_disc_caption
공식 코드Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement Learning
https://github.com/usr922/fst
공식 코드Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
https://github.com/utsavgarg/cnn-fixations
공식 코드CNN Fixations: An unraveling approach to visualize the discriminative image regions
https://github.com/utsjiyaoli/adversarial-image-captioning-attack
공식 코드AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization
https://github.com/val-iisc/SFDA-Seg
공식 코드Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation
https://github.com/val-iisc/cnn-fixations
공식 코드CNN Fixations: An unraveling approach to visualize the discriminative image regions
https://github.com/varunakk/imaginator
공식 코드IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.