https://github.com/b-faye/lightmdetr
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
Papers with Code 아카이브
Visual Question Answering (VQA)
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Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.
Image Source: visualqa.org
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/baai-dcai/segvol
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
https://github.com/baaivision/emu3
https://github.com/baaivision/eva
https://github.com/baichuan-inc/Baichuan-Omni-1.5
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/baidu/DuReader
https://github.com/baidut/PatchVQ
https://github.com/bareblackfoot/faster-rcnn.selection
https://github.com/bareblackfoot/lddp-tf-faster-rcnn
https://github.com/bashivanlab/iwisdm
A Dataset and Architecture for Visual Reasoning with a Working Memory
https://github.com/batra-mlp-lab/visdial-challenge-starter-pytorch
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/batra-mlp-lab/visdial-rl
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
https://github.com/batra-mlp-lab/vln-chasing-ghosts
https://github.com/batra-mlp-lab/vln-sim2real
https://github.com/betashort/ClassActivationMappings
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/bhanu77prakash/EDA-project
https://github.com/bill86416/trojan_attack
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/birkhoffkiki/gpfm
Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation
https://github.com/blaze1522/Image-Segmentation-FloodNet
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/bletib/artquest
https://github.com/brandon3964/multimodal-task-vector
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
https://github.com/brandontrabucco/up_down_cell
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/brandontrabucco/up_down_rnn_cell
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/brightmart/roberta_zh
https://github.com/buaaYYC/faster-rcnn-tensorflow
https://github.com/byahn2/clevr_ref
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
https://github.com/caffeinism/film-pytorch
https://github.com/caggursoy/Cross-Age-Effect
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/caggursoy/crossageeffect
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/camenduru/llava-colab
https://github.com/cattidea/VinVL-Paddle
VinVL: Revisiting Visual Representations in Vision-Language Models
https://github.com/cbluebenchmark/cblue
https://github.com/ceyzaguirre4/DACT-MAC
https://github.com/ceyzaguirre4/NSM
https://github.com/ceyzaguirre4/mac-network-pytorch
https://github.com/chaitanyadwivedii/3D-Attention-is-All-You-Need
TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.