arXiv Computer Vision
TTTIR: Unlocking Instance-Specific State Evolution via Test-Time Training for Image Restoration
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arXiv Computer Vision
Aug 28
Loop-Mamba: A Loop Mamba with Degradation-Aware and Shared Memory for Old Photo Restoration
Loop‑Mamba is a lightweight, loop‑based state‑space framework designed for restoring old photographs that suffer from multiple degradations such as scratches, cracks, fading, blur, noise, and missing regions. It models restoration as progressive state evolution, using a Semantic‑Guided Degradation Estimator to predict local degradation maps and global scores, and a Shared Structural Memory Mamba to maintain a persistent restoration state across iterations. The method employs first‑order state recursion and a multi‑directional scanning strategy to reduce gradient dilution and computational overhead, and introduces the Old Photo Damage Recovery Score (ODRS) to evaluate both degradation recovery and structural reconstruction, achieving superior performance on the SynOld benchmark.
arXiv Computer Vision
Oct 12
State-Isolated Vision Mamba via Update-Tendency Routing for Underwater Image Enhancement
The paper introduces IsoMamba, a Vision Mamba variant that addresses state contention in underwater image enhancement by organizing recurrent states according to token compatibility. It employs a cluster–route–isolate paradigm to route update‑compatible tokens into independent state streams and adds a dynamic channel‑wise reweighting objective to handle wavelength‑dependent reconstruction challenges. Experiments show IsoMamba outperforms existing methods on multiple underwater enhancement benchmarks while producing cleaner hidden state responses.
arXiv Computer Vision
Sep 1
Test-Time Scaling for Video Diffusion Models via Diagnosis-Guided Candidate Recycling
arXiv:2608.29322v1 Announce Type: new Abstract: Recent video diffusion models have achieved remarkable generation quality, but high-fidelity results still largely depend on closed-source systems or c...
arXiv Computer Vision
Aug 24
Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions
The paper surveys Continual Test-Time Adaptation (CTTA), a framework that adapts pretrained computer‑vision models to non‑stationary target distributions without source data or labeled targets, while avoiding catastrophic forgetting and error accumulation. It formally defines the CTTA problem, categorizes existing methods into optimization‑based, parameter‑efficient, and architecture‑based families, and reviews representative techniques and benchmarks across standard evaluation settings. The survey also outlines current limitations and proposes future research directions, such as adapting foundation models and black‑box systems.
arXiv Computer Vision
Sep 1
GramLoop: Training-Free Gram-Gated Replay for Robust Dense Prediction
GramLoop is a training‑free framework that enhances frozen DINOv3 dense‑prediction models under distribution shift by adding inference computation within the visual backbone. It replays a short transformer window and uses final‑layer cosine‑Gram consistency to control each replay, propagating proposals through the frozen suffix and accepting them via a patchwise gate. Across object detection and semantic segmentation tasks, GramLoop improves performance on all five shifted benchmarks, notably raising COCO‑O mAP by +0.252 and Effective Robustness by +0.250 while maintaining clean ADE20K accuracy.
arXiv Computer Vision
Sep 11
LoopVAE: Recurrent Depth Across Scales for Visual Tokenization
LoopVAE introduces a recurrent depth architecture that reuses a scale‑ and loop‑conditioned core across different spatial scales while keeping resolution‑changing transitions separate. The four‑block core applies 28 block operations per encoder or decoder, enabling a 29M‑parameter convolutional model to achieve 0.28 rFID and 32.54 dB PSNR on ImageNet‑256 with roughly 65% fewer parameters than comparable VAEs. Experiments with both convolutional and Transformer operators, as well as ablations on parameter sharing, demonstrate competitive image quality metrics and reveal how targeted loop interventions and truncation affect reconstruction quality and computational trade‑offs.