💬 LLM (Other)

🎞️ ECCV2026 · 13 paper notes

📌 Same area in other venues: 🧠 NeurIPS2026 (2) · 📷 CVPR2026 (3) · 🔬 ICLR2026 (55) · 💬 ACL2026 (61) · 🧪 ICML2026 (39) · 🤖 AAAI2026 (29)

🔥 Top topics: Diffusion Models ×2

ProAct: Agentic Lookahead in Interactive Environments

ProAct moves lookahead out of inference time and into data construction: it first probes the real environment with MCTS and compresses the resulting futures into observation-analysis-conclusion causal chains for SFT (GLAD), then stabilizes PPO/GRPO training with a parameter-free Monte-Carlo Critic (MC-Critic) that estimates values from cheap random-policy rollouts, letting a 4B model substantially outperform open-source baselines of the same and larger scale on 2048 and Sokoban.

Embedding Rotation Invariance for Provable Multi-Oriented Scene Text Recognition

To eliminate the error accumulation and inference overhead stemming from explicit orientation prediction and rotation data augmentation in multi-oriented scene text recognition, RISTER incorporates rotation equivariance into the encoder (via the RELG backbone) and rotation invariance into the decoder (via cross-attention in RITD), creating an end-to-end rotation-invariant recognition system with theoretical guarantees that outperforms prior SOTA by 4.0% on multi-oriented benchmarks.

ExpoMotion: A Large-Scale Benchmark and A Householder Projection Network for Multi-Exposure Fusion

To tackle the lack of high-fidelity dynamic benchmarks and the propensity of correlation-based fusion to admit ghosting artifacts, this paper presents ExpoMotion, a 1,738-sequence real-world dynamic MEF benchmark, and HOP, a decoupled architecture using Householder orthogonal projections to filter out motion artifacts geometrically while harmonizing global exposures.

GRE-Diff: Gaussian Room Embeddings for Structured Layout Diffusion

GRE-Diff models individual rooms as continuous 2D Gaussian distributions (Gaussian Room Embeddings, GRE) to guide a vector diffusion process, achieving an FID of 4.36 and a 99.21% F1BC-RC on RPLAN while enabling robust multi-modal and GUI-based interactive floor plan editing.

Learning Probabilistic Prompt for Continual Learning

To tackle the ubiquitous "prompt collapse" issue where deterministic prompt vectors become highly correlated and fail to represent diverse distributions across incremental tasks, this paper models prompts as Gaussian distributions, samples diverse prompt tokens from a query-conditioned mixture distribution, and employs a distribution regularization loss to stabilize continuous learning.

Prior-Conditioned Gaussian Discriminants for Generalizable AI-generated Image Detection

Formalizes AI-generated image (AIGI) detection as a tripartite transfer system of training prior, frozen representation, and decision rule, establishing a closed-form Gaussian discriminant ladder with nested covariance assumptions as a diagnostic baseline that reveals learned classifier heads often fail to surpass frozen low-order feature geometry.

SAM2Matting: Generalized Image and Video Matting

SAM2Matting proposes a decoupled "tracker-to-matting" framework that divides video matting into high-level tracking (where a frozen VOS tracker like SAM2/SAM3 ensures temporal consistency) and low-level matting (where a trainable ROI detector + progressive Alpha predictor estimates fine-grained transparency). Trained solely on image matting data, it achieves SOTA in a zero-shot manner on video matting benchmarks. It also supports multiple prompt types, maintains strong temporal consistency, and generalizes well to both human and open-world scenarios.

Debiased Textual Prompt Tuning for Enhancing Unknown Class Discovery

Addressing known-class model bias and cross-modal graph sparsity in text-guided open-world semi-supervised learning, this paper proposes an optimal-transport-debiased prompt tuning scheme paired with an asymmetric bimodal graph label propagation framework, achieving a 20.7% relative accuracy improvement on unknown classes across large-scale benchmarks.

Direct Preference Optimization for Perceptual Alignment via Vision-Language Consistency

VLC-DPO is an unsupervised multimodal alignment framework that constructs preference pairs using complementary image- and description-based responses guided by a joint vision-language consistency score, incorporating consistency discrepancies as adaptive penalties in DPO to effectively suppress hallucinations and advance zero-shot VQA performance.

Leveraging Cross-Modal Knowledge Transfer for Knowledge-Aware Concept Customization

Addressing the instability and lack of semantic reasoning in conventional rare-token concept customization, this paper introduces the knowledge-aware concept customization task alongside MoKus, a two-stage framework that leverages cross-modal knowledge transfer to project diverse natural language knowledge onto a visual anchor via closed-form model editing, achieving efficient and high-fidelity customized generation.

Semantic Line Diffusion: Character-Consistent Line Art from text-annotated Storyboards

To tackle rough geometry and severe cross-panel character identity drift in webtoon storyboard sketches (conti), this paper introduces PanelDiff, a diffusion transformer framework combining multi-reference static pooling, dynamic memory bank retrieval with a temporal recency kernel, and panel-adaptive gating to generate clean, structure-aligned, and identity-consistent line-art sequences.

Synthetic Sub-Aperture Phase Augmentation for Demosaicing 2×2 Shared Microlens Sensors

Addressing grid-aligned checkerboard and chromatic artifacts caused by sub-aperture phase shifts under defocus in 2×2 shared microlens sensors, this paper proposes Synthetic Sub-Aperture Phase Augmentation (SPA)—a calibration-free physical data synthesis pipeline—which, paired with a lightweight network, suppresses periodic artifacts while preserving natural optical defocus blur.

TriNLOS: Triplane Representations for Neural Non-Line-of-Sight Imaging

Addressing the cubic computational complexity \(O(N^3)\) and heavy diffuse background noise in dense 3D non-line-of-sight (NLOS) neural inversion, TriNLOS combines a differentiable Enhanced Light-Cone Transform (ELCT) initialization with an orthogonal triplane representation, achieving state-of-the-art volumetric and surface reconstruction in \(O(N^2)\) complexity with only 8.0 GB VRAM.