Action Recognition In Videos
Action recognition is a task in the field of computer vision that aims to identify and classify human behaviors through videos or images. Its goal is to categorize the actions being performed in videos or images into predefined action categories, thereby achieving accurate action detection and understanding. This task holds significant value for applications such as video surveillance, human-computer interaction, and sports analysis. However, the challenge of building large-scale video datasets has led to most existing action recognition benchmarks being relatively small, typically containing only around 10k videos.
ActionNet-VE
ActivityNet
Text4Vis (w/ ViT-L)
Animal Kingdom
AVA v2.1
AVA v2.2
LART (Hiera-H, K700 PT+FT)
BAR
Charades
Charades-Ego
LaViLa (Finetuned, TimeSformer-L)
Diving-48
Drone-Action
DVS128 Gesture
EgoGesture
EPIC-KITCHENS-55
EPIC-KITCHENS-100
Avion (ViT-L)
H2O (2 Hands and Objects)
HandFormer-B/21x8
HAA500
HACS
UniFormerV2-L
HMDB-51
VideoMAE V2-g
HMDB51
MSQNet
Hockey
ICVL-4
IndustReal
IRD
Jester (Gesture Recognition)
DirecFormer
KTH
CNN-GRU
MECCANO
SlowFast
Mimetics
JMRN
miniSports
MTL-AQA
C3D-AVG
N-UCLA
DVANet
NEC Drone
NTU RGB+D
PoseC3D (RGB + Pose)
NTU RGB+D 120
PoseC3D (RGB + Pose)
Okutama-Action
Penn Action
RareAct
Real Life Violence Situations Dataset
DeVTr
RoCoG-v2
Skeleton-Mimetics
SL-Animals
SEW-Resnet18 (3sets)
Something-Something V1
InternVideo
Something-Something V2
MVD (Kinetics400 pretrain, ViT-H, 16 frame)
Sports-1M
ip-CSN-152 (RGB)
THUMOS’14
BMN
THUMOS14
UAV-Human
PMI Sampler
UAV Human
FAR
UCF-101
R3D-18
UCF 101
R2+1D-BERT
UCF101
VideoMAE V2-g
UCFSports
UTD-MHAD
VIRAT Ground 2.0
Volleyball
PoseC3D (Pose Only)
Win-Fail Action Understanding
2DCNN+TRN