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Enhancing Vision Transformers for Object Detection via Context-Aware Token Selection and Packing

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
vision transformerobject detection
Abstract:

In recent years, the long-range attention mechanism of vision transformers has driven significant performance breakthroughs across various computer vision tasks. However, these advancements come at the cost of inefficiency and substantial computational expense, especially when dealing with sparse data. While sparse attention mechanisms have been introduced to mitigate these issues by pruning tokens involved in attention, they often lack context-awareness and intelligence, frequently limiting the number of selected tokens uniformly across different inputs. To address these challenges, we propose a novel algorithm: Select and Pack Attention (SPA). SPA dynamically selects informative tokens using a low-cost gating layer and packs these selected tokens into new batches, allowing for a variable number of tokens to be used in GPU batch training and inference. Through extensive experiments on diverse datasets and multiple computer vision tasks, our method demonstrates superior performance and efficiency, including a 0.5-2.7 AP improvement in object detection and a 10.9%-24.9% reduction in computation.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes Select and Pack Attention (SPA), a dynamic token selection mechanism for efficient vision transformer object detection. It resides in the 'Dynamic Token Selection and Pruning' leaf, which contains five papers including the original work. This leaf sits within the broader 'Sparse Attention Mechanisms for Vision Transformers' branch, indicating a moderately populated research direction focused on adaptive sparsity. The taxonomy shows this is an active area with clear boundaries: dynamic selection methods are distinguished from fixed sparse patterns and hierarchical approaches, suggesting the paper targets a well-defined but competitive niche.

The taxonomy reveals neighboring research directions that contextualize this work. Adjacent leaves include 'Hierarchical and Multi-Scale Sparse Attention' (four papers) and 'Fixed Sparse Patterns and Window-Based Attention' (four papers), both addressing computational efficiency through different sparsity strategies. The 'Learnable Sparsity and Attention Transformation' leaf (three papers) explores alternative attention formulations rather than token pruning. The scope notes clarify that SPA's dynamic, content-aware selection distinguishes it from fixed-pattern methods, while its focus on token-level decisions separates it from hierarchical aggregation approaches. This positioning suggests the work bridges efficiency concerns with adaptive modeling.

Among twenty-one candidates examined, none clearly refute the three identified contributions. The 'Select and Pack Attention mechanism' and 'Select and Pack Transformer backbone' each faced ten candidates with zero refutable overlaps, while the 'multi-scale supervised token selection strategy' examined one candidate without refutation. This limited search scope—drawn from top-K semantic matches—indicates that within the examined subset, no prior work directly anticipates the specific combination of dynamic selection, packing, and variable batch processing. However, the sibling papers in the same taxonomy leaf (four works on dynamic token selection) represent the most relevant prior art and warrant careful comparison.

Based on the limited literature search covering twenty-one candidates, the contributions appear distinct within the examined scope. The absence of refutable overlaps suggests novelty in the specific mechanism design, though the crowded 'Dynamic Token Selection' leaf and active neighboring research directions indicate incremental positioning within a well-explored efficiency paradigm. The analysis does not cover exhaustive prior work beyond top-K semantic matches, leaving open the possibility of related methods in broader transformer efficiency literature.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
21
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: efficient vision transformer object detection via sparse attention. The field has organized itself around several complementary strategies for reducing the computational burden of vision transformers in detection tasks. The main branches include Sparse Attention Mechanisms for Vision Transformers, which explores fundamental techniques like dynamic token selection, pruning, and learnable routing patterns (e.g., BiFormer[1], SparseViT[29]); Domain-Specific Sparse Transformer Object Detection, which tailors sparse designs to particular application areas such as medical imaging, autonomous driving, or industrial inspection; Temporal and Video-Based Sparse Detection, addressing the unique challenges of processing sequential visual data; Specialized Detection Tasks with Sparse Transformers, focusing on niche problems like small object detection or multi-scale scenarios; and Hybrid Architectures and Attention Fusion, which combine sparse transformers with convolutional or other complementary modules to balance efficiency and accuracy. Within the Sparse Attention Mechanisms branch, a particularly active line of work centers on dynamic token selection and pruning, where methods adaptively decide which spatial regions or feature tokens merit full attention computation. Context Token Selection[0] falls squarely into this cluster, emphasizing learned strategies for identifying informative context during inference. Nearby approaches such as Dynamic Spatial Sparsification[23] and Token Compression ViT[36] share a similar philosophy of runtime adaptivity, though they differ in whether they prune tokens entirely or compress them into compact representations. In contrast, works like Sparse Scan Prior[3] incorporate structured priors to guide sparsity patterns, while Scene Adaptive Sparse[2] adjusts attention based on scene complexity. The central trade-off across these methods involves balancing the overhead of selection mechanisms against the savings from reduced attention, with open questions remaining about generalization across diverse object scales and dataset characteristics.

Claimed Contributions

Select and Pack Attention (SPA) mechanism

SPA is a sparse attention mechanism that uses a linear gating layer to dynamically select informative tokens from input images and packs them into fixed-size containers for efficient GPU batch training and inference. It employs multi-scale selection labels derived from object annotations to supervise token selection, improving both efficiency and performance in vision transformers.

10 retrieved papers
Select and Pack Transformer (SPT) backbone network

SPT is a hierarchical backbone network that integrates the SPA mechanism with Swin Transformer blocks. It generates multi-scale image representations across four stages, applying SPA blocks in the last two stages to balance efficiency and performance while avoiding early-stage information loss.

10 retrieved papers
Multi-scale supervised token selection strategy

The method introduces selection labels derived from object-level annotations (bounding boxes or segmentation masks) at multiple scales. By combining scores from different feature scales via max-pooling, it guides the gating layer to select informative tokens more accurately, preventing excessive information loss in complex vision tasks.

1 retrieved paper

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Select and Pack Attention (SPA) mechanism

SPA is a sparse attention mechanism that uses a linear gating layer to dynamically select informative tokens from input images and packs them into fixed-size containers for efficient GPU batch training and inference. It employs multi-scale selection labels derived from object annotations to supervise token selection, improving both efficiency and performance in vision transformers.

Contribution

Select and Pack Transformer (SPT) backbone network

SPT is a hierarchical backbone network that integrates the SPA mechanism with Swin Transformer blocks. It generates multi-scale image representations across four stages, applying SPA blocks in the last two stages to balance efficiency and performance while avoiding early-stage information loss.

Contribution

Multi-scale supervised token selection strategy

The method introduces selection labels derived from object-level annotations (bounding boxes or segmentation masks) at multiple scales. By combining scores from different feature scales via max-pooling, it guides the gating layer to select informative tokens more accurately, preventing excessive information loss in complex vision tasks.

Table of Contents