Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.36460

Full Collapse Is Suboptimal: Controlled Partial Neural Collapse for Long-Tailed Recognition

Hyeontaek Hwang·Cheonghwan Hur·Daeyoung Kim
View original source ↗Read source document ↗Export metadata ↓

Abstract

Recent long-tailed recognition (LTR) methods incorporate neural collapse (NC) and align features to a fixed Equiangular Tight Frame (ETF) as a uniform target for all classes. However, forcing tail classes to fully collapse over-contracts their samples and weakens generalization. To address this, we propose a controlled partial neural collapse, realized through two coordinated controls on a backbone: the Class-Adaptive Sparse Activation (CASA) classifier and the ETF-constrained adaptive margin (ETF-AM) loss. Rather than forcing a uniform full collapse, this regime allows head classes to collapse along the ETF while tail classes retain within-class diversity. This prevents the tail from being forced into full collapse, which is suboptimal under imbalance. To set the dimensionality of collapse, CASA induces class-related node specialization through feature-conditioned gating so that tail classes occupy their own feature directions. For the clearance axis, ETF-AM applies a frequency-adaptive margin, with smaller margins for head classes and larger for tail classes. On CIFAR100-LT, ImageNet-LT, Places-LT, and iNaturalist-2018, our method achieves the best overall accuracy over prior methods. In the empirical analysis, within-class spread increases monotonically from head to tail classes, consistent with tail classes retaining variability rather than fully collapsing.

Explore related subjects

cs.CVcs.AI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hyeontaek Hwang, Cheonghwan Hur, Daeyoung Kim. 2026-10-27. Full Collapse Is Suboptimal: Controlled Partial Neural Collapse for Long-Tailed Recognition. https://arxiv.org/abs/2610.36460

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

DART: Differentiable Dynamic Adaptive Region Tokenizer for Vision Foundation Models

The content-agnostic, fixed-grid tokenizers used by standard large-scale vision models like Vision Transformer (ViT) and Vision Mamba (Vim) represent a fundamental performance bottleneck, creating a trade-off between capturing fine-grained detail and suffering from redundant computation. To resolve this dilemma, we introduce DART, a fully differentiable Dynamic Adaptive Region Tokenizer. DART employs learnable region scores and quantile-based partitioning to create content-aware patches of varying sizes, intelligently allocating a higher token density to information-rich regions. The impact of this approach is profound: it unlocks a more intelligent scaling paradigm, where a DART-equipped DeiT-Small (22M parameters) matches the performance of a DeiT-Base (86M) with nearly double the inference speed by efficiently capturing high-resolution details in key regions. Furthermore, the principle of adaptive tokenization proves its generality with clear benefits in dense prediction and spatiotemporal video tasks. We argue that by resolving the tokenizer bottleneck at its source, adaptive tokenization is a key component for building the next generation of more efficient and capable foundation models for multimodal AI, robotics, and content generation. Code is available at https://github.com/HCPLab-SYSU/DART.

cs.CV↗
arXiv2026

PIS3R: Very Large Parallax Image Stitching via Deep 3D Reconstruction

Image stitching aim to align two images taken from different viewpoints into one seamless, wider image. However, when the 3D scene contains depth variations and the camera baseline is significant, noticeable parallax occurs-meaning the relative positions of scene elements differ substantially between views. Most existing stitching methods struggle to handle such images with large parallax effectively. To address this challenge, in this paper, we propose an image stitching solution called PIS3R that is robust to very large parallax based on the novel concept of deep 3D reconstruction. First, we apply visual geometry grounded transformer to two input images with very large parallax to obtain both intrinsic and extrinsic parameters, as well as the dense 3D scene reconstruction. Subsequently, we reproject reconstructed dense point cloud onto a designated reference view using the recovered camera parameters, achieving pixel-wise alignment and generating an initial stitched image. Finally, to further address potential artifacts such as holes or noise in the initial stitching, we propose a point-conditioned image diffusion module to obtain the refined result.Compared with existing methods, our solution is very large parallax tolerant and also provides results that fully preserve the geometric integrity of all pixels in the 3D photogrammetric context, enabling direct applicability to downstream 3D vision tasks such as SfM. Experimental results demonstrate that the proposed algorithm provides accurate stitching results for images with very large parallax, and outperforms the existing methods qualitatively and quantitatively.

cs.CV↗
arXiv2026

Real-Time Object Detection Meets DINOv3

Driven by the simple and effective Dense O2O, DEIM demonstrates faster convergence and enhanced performance. In this work, we extend it with DINOv3 features, resulting in DEIMv2. DEIMv2 spans eight model sizes from X to Atto, covering GPU, edge, and mobile deployment. For the X, L, M, and S variants, we adopt DINOv3-pretrained or distilled backbones and introduce a Spatial Tuning Adapter (STA), which efficiently converts DINOv3's single-scale output into multi-scale features and complements strong semantics with fine-grained details to enhance detection. For ultra-lightweight models (Nano, Pico, Femto, and Atto), we employ HGNetv2 with depth and width pruning to meet strict resource budgets. Together with a simplified decoder and an upgraded Dense O2O, this unified design enables DEIMv2 to achieve a superior performance-cost trade-off across diverse scenarios, establishing new state-of-the-art results. Notably, our largest model, DEIMv2-X, achieves 57.8 AP with only 50.3 million parameters, surpassing prior X-scale models that require over 60 million parameters for just 56.5 AP. On the compact side, DEIMv2-S is the first sub-10 million model (9.71 million) to exceed the 50 AP milestone on COCO, reaching 50.9 AP. Even the ultra-lightweight DEIMv2-Pico, with just 1.5 million parameters, delivers 38.5 AP, matching YOLOv10-Nano (2.3 million) with around 50 percent fewer parameters. Our code and pre-trained models are available at https://github.com/Intellindust-AI-Lab/DEIMv2

cs.CV↗