LSTCN is a dual-branch CNN that extracts temporal gait features by pooling spatial data into strips and applying local spatiotemporal convolutions with asymmetric kernels.
Prolonged reason- ing is not all you need: Certainty-based adaptive rout- ing for efficient LLM/MLLM reasoning
7 Pith papers cite this work. Polarity classification is still indexing.
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DAIN reframes multimodal fusion as dynamic agent collaboration with sparse activation, claiming SOTA results including 2.6% accuracy gain on ADNI across five benchmarks.
SAME-Net adds a differentiable soft attention mask embedding module to achieve rectification-free end-to-end scene text spotting with 84.02% H-mean on Total-Text.
SPEX delivers 1.2-3x speedup on ToT algorithms via speculative path selection, dynamic budget allocation, and adaptive early termination, reaching up to 4.1x when combined with token-level speculative decoding.
ProWAFT proposes a workload-aware dynamic fault-tolerance method for FPGA CNN accelerators via selective TMR and partial reconfiguration, reporting lower composite cost than static TMR or reactive approaches on ResNet/MobileNet traces under SEU injection.
A multilevel perceptual CRF model using Swin Transformer, HPF fusion, HA adapters, and dynamic scaling attention achieves state-of-the-art monocular depth estimation on NYU Depth v2, KITTI, and MatterPort3D with reduced error and fast inference.
A survey that taxonomizes efficiency methods for LVLMs across the full inference pipeline, decouples the problem into information density, long-context attention, and memory limits, and outlines four future research frontiers with pilot insights.
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Breaking the Reward Barrier: Accelerating Tree-of-Thought Reasoning via Speculative Exploration
SPEX delivers 1.2-3x speedup on ToT algorithms via speculative path selection, dynamic budget allocation, and adaptive early termination, reaching up to 4.1x when combined with token-level speculative decoding.
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Hierarchical Awareness Adapters with Hybrid Pyramid Feature Fusion for Dense Depth Prediction
A multilevel perceptual CRF model using Swin Transformer, HPF fusion, HA adapters, and dynamic scaling attention achieves state-of-the-art monocular depth estimation on NYU Depth v2, KITTI, and MatterPort3D with reduced error and fast inference.