CoDAT: Collaborative Dual-Attention Transformer with Low-Cost Temporal Modeling for Efficient Edge Action Recognition

• URL: http://arxiv.org/abs/2608.06691v1
• Date: Fri, 07 Aug 2026 01:40:20 GMT
• Title: CoDAT: Collaborative Dual-Attention Transformer with Low-Cost Temporal Modeling for Efficient Edge Action Recognition
• Abstract summary: Current 3D CNNs, video transformers, and shift-based ViT deliver high accuracy but come at computational costs that preclude edge IoT deployment.<n>This paper proposes a Collaborative Dual-Attention Transformer that replaces conventional multi-head attention with a lightweight dual-branch module.<n>To enable temporal communication across frames, a parameter-free TShift module is embedded in each block.
• Score: 10.191006117283782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time human action recognition on Internet-of-Things (IoT) edge devices requires models that capture rich spatio-temporal cues within strict latency, memory, and power envelopes. Current 3D CNNs, video transformers, and shift-based ViT deliver high accuracy but come at computational costs that preclude edge IoT deployment. This paper proposes CoDAT, a Collaborative Dual-Attention Transformer that replaces conventional multi-head attention with a lightweight dual-branch module: Spatial Convolutional Attention (SCA) for local aggregation and Strided Single-Head Attention (SSHA) for global context. SSHA jointly compresses the spatial resolution and channel dimensions of the query, key, and value tensors via stride-based sparse projection, then fuses the resulting global and local features at a markedly reduced cost. To enable temporal communication across frames, a parameter-free TShift module is embedded in each block. Extensive experiments on Jetson AGX Orin and Raspberry Pi 5 demonstrate that CoDAT achieves an energy-accuracy balance in both image and action recognition. On ImageNet-1K, CoDAT-M runs 2x faster than EfficientViT384 and FastViT-S12 at comparable accuracy, and CoDAT-L matches ViT-S with 3x fewer parameters at 2x higher throughput. On Kinetics-400 and MA-52, CoDAT achieves competitive Top-1 accuracy against state-of-the-art CNN, transformer, and hybrid baselines while running up to 2.9x faster than VSwin-T, 2x faster than ViT-Temporal-Shift variants, and 5x faster than UniFormer-B. On UCF-101, CoDAT-S384 matches TokShift and LAPS while being 6x faster and requiring up to 13x fewer FLOPs, establishing an efficiency-accuracy balance for real-time action recognition in edge IoT perception systems. Code is available at https://github.com/novendrastywn/CoDAT .

Related papers

• Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders [123.58723804218151]
  Vision foundation models are bottlenecked by the quadratic cost of self-attention.<n>We present C-GSPN based on 2D spatial propagation propagation.
  arXiv  Detail & Related papers  (2026-05-30T14:29:43Z)
• BWTA: Accurate and Efficient Binarized Transformer by Algorithm-Hardware Co-design [71.97035034203275]
  We analyze zero-point distortion in binarization and propose a Binary Weights & Ternary Activations (BWTA) quantization scheme.<n>For training, we propose Smooth Multi-Stage Quantization, combining a Levelwise Degradation Strategy and a MagnitudeAlignment Projection Factor.<n>Experiments show that BWTA approaches full-precision performance for Transformer-based models, with an average 3.5% drop on GLUE and less than 2% drop on five tasks.
  arXiv  Detail & Related papers  (2026-04-05T04:25:07Z)
• Linear Differential Vision Transformer: Learning Visual Contrasts via Pairwise Differentials [34.77694214755808]
  We introduce Visual-Contrast Attention (VCA), a drop-in replacement for Multi-Head Self-Attention (MHSA)<n>VCA injects an explicit notion of discrimination while reducing the theoretical complexity from O(N N C) to O(N n C) with n N.<n>The module adds fewer than 0.3M parameters to a DeiT-Tiny backbone, requires no extra FLOPs, and is wholly architecture-agnostic.
  arXiv  Detail & Related papers  (2025-11-02T07:04:12Z)
• Speedy MASt3R [68.47052557089631]
  MASt3R redefines image matching as a 3D task by leveraging DUSt3R and introducing a fast reciprocal matching scheme.<n>Fast MASt3R achieves a 54% reduction in inference time (198 ms to 91 ms per image pair) without sacrificing accuracy.<n>This advancement enables real-time 3D understanding, benefiting applications like mixed reality navigation and large-scale 3D scene reconstruction.
  arXiv  Detail & Related papers  (2025-03-13T03:56:22Z)
• ParFormer: A Vision Transformer with Parallel Mixer and Sparse Channel Attention Patch Embedding [9.144813021145039]
  This paper introduces ParFormer, a vision transformer that incorporates a Parallel Mixer and a Sparse Channel Attention Patch Embedding (SCAPE) ParFormer improves feature extraction by combining convolutional and attention mechanisms. For edge device deployment, ParFormer-T excels with a throughput of 278.1 images/sec, which is 1.38 $times$ higher than EdgeNeXt-S. The larger variant, ParFormer-L, reaches 83.5% Top-1 accuracy, offering a balanced trade-off between accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-03-22T07:32:21Z)
• SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design [5.962184741057505]
  This paper aims to address computational redundancy at all design levels in a memory-efficient manner. We discover that using larger-stride patchify stem not only reduces memory access costs but also achieves competitive performance. We introduce SHViT, a Single-Head Vision Transformer that obtains the state-of-the-art speed-accuracy tradeoff.
  arXiv  Detail & Related papers  (2024-01-29T09:12:23Z)
• Edge-MoE: Memory-Efficient Multi-Task Vision Transformer Architecture with Task-level Sparsity via Mixture-of-Experts [60.1586169973792]
  M$3$ViT is the latest multi-task ViT model that introduces mixture-of-experts (MoE) MoE achieves better accuracy and over 80% reduction computation but leaves challenges for efficient deployment on FPGA. Our work, dubbed Edge-MoE, solves the challenges to introduce the first end-to-end FPGA accelerator for multi-task ViT with a collection of architectural innovations.
  arXiv  Detail & Related papers  (2023-05-30T02:24:03Z)
• Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition [117.98023585449808]
  We propose a temporal attention-based autoencoder (STAE) architecture to evaluate the importance of frames and pixels in each frame. We develop a lightweight decoder that leverages a 3D-2D CNN combined to reconstruct missing information. Experimental results show that ViT_STAE can compress the video dataset H51 by 104x with only 5% accuracy loss.
  arXiv  Detail & Related papers  (2023-05-22T07:47:27Z)
• FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation [81.76975488010213]
  Dense optical flow estimation plays a key role in many robotic vision tasks. Current networks often occupy large number of parameters and require heavy computation costs. Our proposed FastFlowNet works in the well-known coarse-to-fine manner with following innovations.
  arXiv  Detail & Related papers  (2021-03-08T03:09:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.