🔄 Self-Supervised Learning

🎞️ ECCV2026 · 21 paper notes

📌 Same area in other venues: 🧠 NeurIPS2026 (1) · 📷 CVPR2026 (92) · 🔬 ICLR2026 (81) · 💬 ACL2026 (1) · 🧪 ICML2026 (28) · 🤖 AAAI2026 (16)

🔥 Top topics: Self-Supervised Learning ×5

V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning

V-JEPA 2.1 extends video latent-prediction supervision to visible positions and multiple network depths, improving local spatiotemporal structure while recovering global semantic capabilities; its ViT-G reaches NYUv2 depth RMSE of 0.307 and SSv2 action recognition accuracy of 77.7% in frozen-feature evaluations.

The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding

UAE separates pretrained visual representations into a low-frequency base that preserves semantics and high-frequency residuals that can learn pixel detail, using selective distillation and masked reconstruction to combine both capabilities; Table 1 reports ImageNet-1K PSNR 32.17, SSIM 0.92, and rFID 0.35 for its DINOv2-B configuration.

DSeq-JEPA: Discriminative Sequential Joint-Embedding Predictive Architecture

DSeq-JEPA adds two inductive biases on top of I-JEPA's latent prediction objective — an attention-derived saliency map that selects and ranks regions by discriminativeness (where to predict), and a predictor that then predicts the embedding of the next region along that order (in what order to predict) — improving ImageNet linear probing by 1.1/0.8/1.3 points over I-JEPA (ViT-B/L/H) with consistent gains on fine-grained recognition, COCO/ADE20K, and CLEVR, at zero inference overhead.

GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure

GhostPoint observes that the learning signal in LiDAR self-supervised pretraining is almost exclusively defined on returns from visible surfaces and leaves occluded and no-return regions unconstrained; it therefore uses instance voxel dilation to generate neighborhood voxel sets around pseudo-instances that contain visible, masked, and no-return voxels, trains a lightweight predictor to hallucinate features at those positions from observed context, and supervises them with asymmetric teacher targets (occupied voxels match the teacher encoder, no-return voxels match the teacher predictor), unifying pseudo-occlusion and true occlusion under one self-distillation objective — reaching state-of-the-art downstream 3D detection on nuScenes and Waymo (67.5 mAP / 71.2 NDS on nuScenes after full fine-tuning).

Human-like Object Grouping in Self-supervised Vision Transformers

This paper scales the classical two-dot same/different object judgment paradigm into a human behavioral benchmark of 255 natural COCO scenes and 1,020 trials, reads out same/different decisions and trial-wise reaction times from a simple MLP over two patch features, finds that DINO-family self-supervised ViTs align best (up to 91.9% grouping accuracy), proposes an object-centricity AUC based on ROC analysis of patch affinity maps that correlates strongly with behavioral alignment across nine models (Spearman r=0.950), and finally raises supervised models' grouping accuracy by roughly 8–18 percentage points through Gram matrix distillation.

Implicit Neural Representation Facilitates Unified Universal Vision Encoding

The paper trains an image implicit neural representation (INR) hyper-network into a unified vision encoder, HUVR: a single ViT backbone emits standard tokens, compressed tokens (TinToks, up to 96x smaller), and per-patch INR modulation quantities, and is pretrained self-supervised with pixel reconstruction plus teacher distillation, making one representation usable simultaneously for classification, semantic segmentation, depth estimation, and image reconstruction.

Vision-TTT: Efficient and Expressive Visual Representation Learning with Test-Time Training

The paper brings Test-Time Training (TTT) from NLP into vision: it treats an image's token sequence as a dataset and updates a per-head hidden state online by gradient descent on a self-supervised reconstruction loss, then adds a dual-dataset strategy and Conv2d dataset preprocessing to turn the originally unidirectional TTT into a 2D-aware vision backbone — Vittt-T/S/B reach 77.7/81.8/82.7 Top-1 on ImageNet and, at 1280×1280, save 79.4% FLOPs, run 4.72× faster and use 88.9% less memory than DeiT-T.

Bottom-up modeling of repeated elements via single image analysis-by-synthesis

A shared two-dimensional prototype with adjustable appearance, single-image crop-based curriculum optimization, and full-image selection and refinement turn repeated-element discovery into interpretable reconstruction; on 116 curated FSC-147 images, the method achieves an image PSNR of 23.76 and a counting MAE of 14.11, without implying that the entire system is free of pretraining or scale priors.

DP-BOA: Dirichlet-Process Birth-or-Assign for On-the-Fly Category Discovery

DP-BOA reformulates per-sample online category discovery as a Bayesian comparison between existing-category assignment evidence and new-category birth evidence; support-set prior calibration, full-covariance prediction, and online statistical updates yield 53.4% overall and 51.6% novel-class clustering accuracy on CUB, without leading on every dataset or known-class metric.

ExPLoRe: Expert Patch-Level Loss Routing for Multi-Objective Masked Image Modeling

ExPLoRe repurposes the dispatch weights of Soft-MoE as patch-level, learnable loss coefficients in multi-objective masked image modeling. Through a loss-coupling mechanism, the router dynamically adjusts the target weights based on the semantic characteristics of different regions, achieving 80.6% linear probe and 85.3% finetuning accuracy on ImageNet-1K with a lower inference computation cost (11.93 GFLOPs vs. 17.45). It also proposes three downstream transfer strategies (Freeze Routing / Expert Dropout / Freeze Attention) to completely bridge the 2.5–2.9 mIoU gap on semantic segmentation.

From Phase to Phenomenon: Self-Supervised Learning of Subsurface Scattering with Minimal Phase-shift Inputs

This work proposes a self-supervised pre-training framework that learns general subsurface scattering (SSS) representations using a projector-camera system from only 8 high-frequency phase-shift profilometry (PSP) images. It then predicts anisotropic scattering footprints via a decoder to achieve zero-shot relighting, reducing the data acquisition requirements for new objects from thousands of images to just 8.

InstrAct: Towards Action-Centric Understanding in Instructional Videos

Addressing noisy web supervision and severe static shortcut bias where models rely on static objects rather than motion dynamics in instructional videos, InstrAct introduces a pretraining framework combining LLM-assisted action-centric hard negatives, an Action Perceiver with verb-guided distillation, Soft-DTW temporal alignment, and Masked Action Modeling (MAM), significantly enhancing fine-grained action discrimination and procedural temporal reasoning.

LoT-Pass: Long-term-robust Image Watermarking for Image to Video Generation

LoT-Pass proposes a bidirectional robustness strategy: simulating temporal evolution during training to actively expand the watermarking robustness distance, and employing optical flow inversion during inference to "pull back" severely drifted frames into an extractable state. This achieves a video-level watermark extraction accuracy of over 95% for the first time in image-to-video (I2V) generation scenarios.

Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions

Addressing catastrophic performance drops of recursive least squares (RLS) analytic continual learning under long-tailed distributions caused by autocorrelation matrix spectral collapse, this paper proposes Geometry-Spectral Rectification (GSR), which employs hyperspherical geodesic interpolation to inject structured anisotropic spectral perturbations, provably improving Gram matrix stable rank with \(O(D)\) linear complexity.

Virtual Category-Guided Continual Generalized Category Discovery

To tackle ambiguous unlabeled samples that induce noisy pseudo-labels and familiar-class bias in non-rehearsal Continual Generalized Category Discovery (C-GCD), this paper introduces Virtual Category Learning to establish a safe optimization buffer and combines it with Expanded Neighborhood Contrastive Learning to reinforce novel-class discovery while suppressing catastrophic forgetting.

WildProp: Visual Estimation of Wildlife Body Proportions at Scale

WildProp turns wildlife morphometrics into a retrieval-driven correspondence problem: the user provides a single annotated query image in a canonical pose, and the system uses DINOv3 for pose-aware retrieval, SAM2 to suppress background, RANSAC to filter geometrically inconsistent matches, and iterative keypoint-descriptor refinement to estimate population-level part-length-ratio distributions from iNaturalist-scale image collections. Across three physically measured datasets (AVONET birds, Shorebirds, Frogs) the median relative error is roughly 11-18%, and adapting to a new species or new body parts requires only one annotated query image, with no species-specific training.

Φeat: Physically-Grounded Material Feature Representation

Addressing the limitation of semantic foundation models that overlook physical material reflectance and mesostructure, Φeat fine-tunes DINOv3 using large-scale physically based renderings under controlled geometry and illumination variations, yielding representations invariant to extrinsic factors while highly sensitive to intrinsic material identity.

HVGCD: Rethinking Generalized Category Discovery through Hypothesis–Verification

HVGCD reformulates generalized category discovery from a cognitive perspective by establishing a tripartite representation system—global hypothesis, hypothesis-conditioned verification evidence, and maximum-entropy statistical grounding—resolving confident misclassification of novel classes and consistently boosting clustering accuracy across multiple benchmarks.

Local-to-global Cross-modal Coordination for Self-supervised RGB-T Tracking

To tackle error accumulation and modality drift caused by dynamic reliability discrepancies between visible and thermal modalities in forward-backward self-supervised loops, LGCTrack introduces hierarchical local coordination (HLC) and turning-phase global coordination (TGC) to achieve state-of-the-art self-supervised RGB-T tracking while rivaling fully supervised methods.

One Slide, Many Views: Unifying Complementary Foundation Model Perspectives for WSI Analysis

Reformulates whole slide image (WSI) foundation model ensembling as a multi-view learning problem, using an information-theoretic bottleneck module to disentangle shared consensus and view-specific representations coupled with a multi-branch MIL prediction head for robust downstream performance.

Simple Filtering Improves Masked Autoencoders

To resolve the pretext task difficulty imbalance caused by high spatial redundancy in masked autoencoders (MAE), this paper quantifies the geometric difficulty of mask patterns via mean nearest neighbor distance and introduces pooling-based masking to appropriately increase masking difficulty, combined with Gaussian target smoothing to soften uninformative high-frequency reconstruction noise, boosting representation learning across ViTs at negligible extra computational cost.