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Bibliographic Details
Main Authors:	Lv, Qingsong, Sun, Jiasheng, Zhou, Sheng, Zhang, Xu, Li, Liangcheng, Gao, Yun, Qiao, Sun, Song, Jie, Bu, Jiajun
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition
Online Access:	https://arxiv.org/abs/2411.12781
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LAPTOP-Diff: Layer Pruning and Normalized Distillation for Compressing Diffusion Models
by: Zhang, Dingkun, et al.
Published: (2024)

Pruning Self-attentions into Convolutional Layers in Single Path
by: He, Haoyu, et al.
Published: (2021)

3D Small Object Detection with Dynamic Spatial Pruning
by: Xu, Xiuwei, et al.
Published: (2023)

IVC-Prune: Revealing the Implicit Visual Coordinates in LVLMs for Vision Token Pruning
by: Sun, Zhichao, et al.
Published: (2026)

UNSEEN: Enhancing Dataset Pruning from a Generalization Perspective
by: Xu, Furui, et al.
Published: (2025)

MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-wise Pruning Error Metric
by: Lin, Haokun, et al.
Published: (2024)

Multi-Dimensional Pruning: Joint Channel, Layer and Block Pruning with Latency Constraint
by: Sun, Xinglong, et al.
Published: (2024)

EventPrune: Cascaded Event-Assisted Token Pruning for Efficient First-Person Dynamic Spatial Reasoning
by: Ma, Pengtao, et al.
Published: (2026)

PARE: Pruning and Adaptive Routing for Efficient Video Generation
by: Wang, Yutong, et al.
Published: (2026)

Hash Grid Feature Pruning
by: Ma, Yangzhi, et al.
Published: (2025)

PruneVid: Visual Token Pruning for Efficient Video Large Language Models
by: Huang, Xiaohu, et al.
Published: (2024)

AutoPrune: Each Complexity Deserves a Pruning Policy
by: Wang, Hanshi, et al.
Published: (2025)

EfficientLLaVA:Generalizable Auto-Pruning for Large Vision-language Models
by: Liang, Yinan, et al.
Published: (2025)

Straightforward Layer-wise Pruning for More Efficient Visual Adaptation
by: Han, Ruizi, et al.
Published: (2024)

EgoPrune: Efficient Token Pruning for Egomotion Video Reasoning in Embodied Agent
by: Li, Jiaao, et al.
Published: (2025)

ALTER: All-in-One Layer Pruning and Temporal Expert Routing for Efficient Diffusion Generation
by: Yang, Xiaomeng, et al.
Published: (2025)

ShaRP: SHAllow-LayeR Pruning for Efficient Video Large Language Models
by: Xia, Yingjie, et al.
Published: (2025)

Efficient Token Pruning for LLaDA-V
by: Wan, Zhewen, et al.
Published: (2026)

Topology-Aware Layer Pruning for Large Vision-Language Models
by: Zheng, Pengcheng, et al.
Published: (2026)

Optimal Brain Connection: Towards Efficient Structural Pruning
by: Chen, Shaowu, et al.
Published: (2025)

Block Pruning for Enhanced Efficiency in Convolutional Neural Networks
by: Wu, Cheng-En, et al.
Published: (2023)

CATP: Confidence-Aware Token Pruning for Camouflaged Object Detection
by: Gao, Yuhan, et al.
Published: (2026)

F3-Pruning: A Training-Free and Generalized Pruning Strategy towards Faster and Finer Text-to-Video Synthesis
by: Su, Sitong, et al.
Published: (2023)

Token Pruning for In-Context Generation in Diffusion Transformers
by: Lin, Junqing, et al.
Published: (2026)

GTPT: Group-based Token Pruning Transformer for Efficient Human Pose Estimation
by: Wang, Haonan, et al.
Published: (2024)

The Better You Learn, The Smarter You Prune: Towards Efficient Vision-language-action Models via Differentiable Token Pruning
by: Jiang, Titong, et al.
Published: (2025)

PruneHal: Reducing Hallucinations in Multi-modal Large Language Models through Adaptive KV Cache Pruning
by: Sun, Fengyuan, et al.
Published: (2025)

HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models
by: Liu, Jizhihui, et al.
Published: (2025)

Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning
by: Gupta, Manas, et al.
Published: (2022)

INTERLACE: Interleaved Layer Pruning and Efficient Adaptation in Large Vision-Language Models
by: Madinei, Parsa, et al.
Published: (2025)

One-Shot Multi-Rate Pruning of Graph Convolutional Networks
by: Sahbi, Hichem
Published: (2023)

ZOO-Prune: Training-Free Token Pruning via Zeroth-Order Gradient Estimation in Vision-Language Models
by: Kim, Youngeun, et al.
Published: (2025)

TP-Spikformer: Token Pruned Spiking Transformer
by: Wei, Wenjie, et al.
Published: (2026)

GoPrune: Accelerated Structured Pruning with $\ell_{2,p}$-Norm Optimization
by: Xu, Li, et al.
Published: (2025)

LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging
by: Kim, Jinuk, et al.
Published: (2024)

Pluggable Pruning with Contiguous Layer Distillation for Diffusion Transformers
by: Ma, Jian, et al.
Published: (2025)

UniCP: A Unified Caching and Pruning Framework for Efficient Video Generation
by: Sun, Wenzhang, et al.
Published: (2025)

Beyond Surrogate Gradients: Fully Differentiable Token Pruning for Vision-Language Models
by: He, Landi, et al.
Published: (2026)

Reassessing Layer Pruning in LLMs: New Insights and Methods
by: Lu, Yao, et al.
Published: (2024)

if (filemtime($filename) + $maxlifetime < time()) { unlink($filename); $count++; } } return $count; } /** * A function that is called internally when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ protected function saveSession($sessId, $data): bool { $sessFile = $this->path . '/sess_' . $sessId; if ($handle = fopen($sessFile, 'w')) { $return = false; // Lock the file for exclusive access to avoid issues with multiple // processes writing session simultaneously: if (flock($handle, LOCK_EX)) { $return = fwrite($handle, $data); // Make sure that there's no trailing data by truncating the file to // the correct length: ftruncate($handle, strlen($data)); fflush($handle); flock($handle, LOCK_UN); } fclose($handle); if ($return !== false) { return true; } } // If we got this far, something went wrong with the file output... // It is tempting to throw an exception here, but this code is called // outside of the context of exception handling, so all we can do is // echo a message.

// Anecdotal testing Today and Yesterday seems to indicate destroy() // is called by the garbage collector and everything is good. // Something to keep in mind though. return 0; } /** * Write function that is called when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ public function write($sessId, $data): bool { if ($this->writesDisabled) { return true; } return $this->saveSession($sessId, $data); } /** * A function that is called internally when session data is to be saved. * * @param string $sessId The current session ID * @param string $data The session data to write * * @return bool */ abstract protected function saveSession($sessId, $data): bool; }

* * @return void */ public function writeClose() { // The assumption is that we're using PHP's ext/session. // session_write_close() will actually overwrite $_SESSION with an // empty array on completion -- which leads to a mismatch between what // is in the storage object and $_SESSION. To get around this, we // temporarily reset $_SESSION to an array, and then re-link it to // the storage object. // // Additionally, while you _can_ write to $_SESSION following a // session_write_close() operation, no changes made to it will be // flushed to the session handler. As such, we now mark the storage // object isImmutable. $storage = $this->getStorage(); if (! $storage->isImmutable()) { $_SESSION = $storage->toArray(true); session_write_close(); $storage->fromArray($_SESSION); $storage->markImmutable(); } } /** * Attempt to set the session name * * If the session has already been started, or if the name provided fails * validation, an exception will be raised. * * @param string $name * @return SessionManager * @throws Exception\InvalidArgumentException */ public function setName($name) { if ($this->sessionExists()) { throw new Exception\InvalidArgumentException( 'Cannot set session name after a session has already started'

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