Efficient Video Intelligence in 2026

Video intelligence has now moved beyond short clip classification into a stage that must jointly handle hour-long footage, on-device tracking, and multi-modal reasoning. A 1-minute, 30 FPS, 224x224 video produces 352,000 visual tokens under ViT-B/16, and at 1 hour that reaches 21 million tokens. Since most frames look similar while important events are rare, video must inevitably be compressed and adaptively sampled somewhere in the pipeline.

Frame encoders now have to handle multiple roles with a single backbone. CLIP/SigLIP/SigLIP 2 are strong at semantic retrieval but weak at dense prediction, while DINOv2/DINOv3 excel at segmentation, depth, and correspondence but have weak language alignment. SAM/SAM 2 specialize in segmentation and tracking, and DepthAnything/DepthPro/DepthLM specialize in depth. The trend trying to close this gap is the family of agglomerative encoders like AM-RADIO, Theia, and DUNE, and EUPE adds one more proxy teacher on top of this. It first blends DINOv2/v3, the SAM family, and CLIP/SigLIP/SigLIP-SO400M into a large proxy teacher, then transfers the result to a student model with fewer than 100M parameters. Because conflicts between different teachers are resolved at the proxy stage before compression, this approach loses less signal than direct multi-teacher distillation.

When dealing with long videos, the standard O(n²) cost of self-attention immediately becomes a bottleneck. Responses have converged into three directions.

• Sliding-window / sparse attention: focuses only on local or selected tokens, as in LongLLaMA, Mistral, and DeepSeek Native Sparse Attention.
• Linear attention: handles length with linear complexity, as in Performer, Linformer, Nyströmformer, and Qwen3-Next's Gated DeltaNet.
• Hybrid Mamba-Transformer: uses attention for short-term relations and SSM for long-term dependencies, as in Jamba and Nvidia Nemotron Nano 2.

In video, this structure is organized as factorized spatial-temporal attention. Spatial attention is kept within each frame, while temporal attention across frames is reduced via sparse or linear methods, a direction that Qwen3-VL and LLaVA-Video also converge on.

Segmentation and tracking form the core execution layer for on-device video. SAM 2 extends into video by adding FIFO memory that separates recent/prompted frames along with object pointers, while XMem's multi-store memory, DEVA's decoupled temporal propagation, and Cutie's query-based object transformer each show advantages in long-term memory, reusability, and memory efficiency respectively.

Advances in the compression family have actually changed real-world deployment. EfficientSAM distills SAM's image encoder into SAMI, letting smaller ViT-T/S models achieve similar performance, and Efficient Track Anything uses a plain ViT instead of a hierarchical encoder along with an efficient memory module, achieving about 2x speed on A100, a 2.4x reduction in parameters, and about 10 FPS on iPhone 15 Pro Max. EdgeTAM compresses memory further with a 2D Spatial Perceiver, recording 87.7 / 70.0 / 72.3 / 71.7 J&F on DAVIS 2017, MOSE, SA-V val/test, and achieving 16 FPS on iPhone 15 Pro Max. The center of these performance gains is not simply scaling up models, but reducing frame redundancy and making memory propagation more efficient.

In 3D and depth, video's parallax and motion provide strong cues, but in practice image-based approaches still dominate. DepthLM shows that a 3B VLM backbone with only standard SFT can match or exceed DepthPro and Metric3Dv2-level metric depth. The key elements are visual prompts, intrinsic-conditioned augmentation that normalizes focal length, and sparse supervision with just one labeled pixel per image.

Dedicated specialists such as DepthAnything, DepthAnything V2, DepthPro, and Metric3D v2 remain strong on many benchmarks, and Marigold has an edge in zero-shot generalization but is slow. Adding reconstruction-based approaches like DUSt3R, MASt3R, and VGGT into the mix, depth now shapes up as a contest among specialists, diffusion priors, reconstruction methods, and VLMs.

Ultimately, in 2026, video intelligence is decided not by a single giant model but by designs that compress the encoder, attention, memory, and task-specific heads at every stage and bring them down to consumer devices.