Video Classification
Video Classification is the task of producing a label that is relevant to the video given its frames. A good video level classifier is one that not only provides accurate frame labels, but also best describes the entire video given the features and the annotations of the various frames in the video. For example, a video might contain a tree in some frame, but the label that is central to the video might be something else (e.g., “hiking”). The granularity of the labels that are needed to describe the frames and the video depends on the task. Typical tasks include assigning one or more global labels to the video, and assigning one or more labels for each frame inside the video.
Papers
Showing 51–75 of 455 papers
All datasetsBreakfastCOINMoBYouTube-8MHockey Fight Detection DatasetCharadesHome Action GenomeKineticsMultimodal PISASomething-Something V1Something-Something V2SRI-APPROVE Fine-Grained Video Classification
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | HERMES | Accuracy (%) | 95.2 | — | Unverified |
| 2 | MA-LMM | Accuracy (%) | 93 | — | Unverified |
| 3 | S5 | Accuracy (%) | 90.7 | — | Unverified |
| 4 | TranS4mer | Accuracy (%) | 90.27 | — | Unverified |
| 5 | D-Sprv. | Accuracy (%) | 89.9 | — | Unverified |
| 6 | ViS4mer | Accuracy (%) | 88.2 | — | Unverified |
| 7 | GHRM | Accuracy (%) | 75.5 | — | Unverified |
| 8 | Timeception | Accuracy (%) | 71.3 | — | Unverified |
| 9 | VideoGraph | Accuracy (%) | 69.5 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | DCGN (self-attention graph pooling) | Hit@1 | 87.7 | — | Unverified |
| 2 | Hierarchical LSTM with MoE | Hit@1 | 86.8 | — | Unverified |
| 3 | Mixture-of-2-Experts | Hit@1 | 70.1 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Structured Keypoint Pooling | Accuracy | 99.5 | — | Unverified |
| 2 | CNN+LSTM | 1:1 Accuracy | 98 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Multigrid | mAP | 38.2 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Cooperative Ours (3rd-person) | Accuracy (%) | 24.7 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Multigrid | Top-1 | 77.6 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Video | Accuracy (%) | 73.95 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | MSNet-R50En (ours) | Top-5 Accuracy | 84 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | MSNet-R50En (ours) | Top-5 Accuracy | 91 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Multi-Label Prototypes Contrastive Learning | AUPR | 88.4 | — | Unverified |