SV-TAD: Native Sparse Convs for Efficient Temporal Action Detection

arXiv:2610.11579 · cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Code: https://github.com/pcr-upm/eccv26_tad

Terminology

• Transformer — 24× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Pruning — 13× in this paper · explained in 4 episodes
• Sparsity — 11× in this paper · explained in 4 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Parameter Efficiency — 2× in this paper · explained in 3 episodes
• Parameter-Efficient Fine-Tuning — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• European Conference on Computer Vision — 1× in this paper
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Parameter-Efficient Fine-Tuning (PEFT) — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Generating Long Sequences with Sparse Transformers
• Submanifold Sparse Convolutional Networks
• InternVideo-Next: Towards General Video Foundation Models without Video-Text Supervision

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