CVPR 2026 Papers

Parameter-efficient fine-tuning (PEFT) methods have recently gained popularity for applying deep neural networks on small datasets as they reduce overfitting, simplify deployment, and enable fast training. We demonstrate that for dense image prediction tasks, a well-designed and lightweight dense readout on top of a frozen large backbone can surpass state-of-the-art PEFT methods in both efficiency and accuracy. Our parameter-efficient readout module combines interpolation and attention for fine-grained dense prediction. It integrates seamlessly with a wide range of pretrained vision backbones such as DINOv3 and can be combined with low-rank adaptation (LoRA). We achieve competitive or superior performance in semantic segmentation, object detection, pose estimation and semantic contour prediction, offering an efficient alternative to current PEFT techniques.

The escalating parameter counts in modern deep learning models pose a fundamental challenge to efficient training and resolution of overfitting. We address this by introducing the Mapping Networks which replace the high dimensional weight space by a compact, trainable latent vector based on the hypothesis that the trained parameters of large networks reside on smooth, low-dimensional manifolds. Henceforth, the Mapping Theorem enforced by a dedicated Mapping Loss, shows the existence of a mapping from this latent space to the target weight space both theoretically and in practice. Mapping Networks significantly reduce overfitting and achieve comparable to better performance than target network across complex vision and sequence tasks, including Image Classification, Deepfake Detection etc., with 99.5%, i.e., around 500x reduction in trainable parameters.

Tensor-based methods have attracted growing interest due to their ability to reduce trainable parameters and offer advantages over matrix-based approaches in parameter-efficient fine-tuning (e.g., LoRA and PiSSA), particularly in capturing inter-layer correlations. However, directly applying tensor methods requires repeated reconstruction of model weights during training, introducing substantial overhead and becoming a key bottleneck for deployment. To address this limitation, we propose Reconstruction-Free Tensor-Based Adaptation (ReFTA), which offers four advantages: (1) eliminating weight tensor reconstruction via tensor algebraic properties; (2) reducing initialization quantization error by fine-tuning only the principal tensor components; (3) providing a rigorous generalization guarantee rooted in tensor product algebra; and (4) enabling a unified design controlled by a single tensor rank configuration. Extensive experiments on both image classification (IC) and natural language understanding (NLU) tasks demonstrate that ReFTA achieves the best accuracy-efficiency trade-off among all evaluated methods. Across most cases, ReFTA attains the highest average accuracy with the fewest trainable parameters. On IC tasks with the ViT-Large model, ReFTA surpasses LoRA by 5.6% in average accuracy, while reducing the number of trainable parameters by up to 96% compared to LoRA. On NLU tasks with RoBERTa-Large, ReFTA improves the average accuracy by approximately 5% over most existing methods while using only 86.4% fewer parameters than LoRA (r=1) and 97.5% fewer than PiSSA. The code is available at https://github.com/jzheng20/ReFTA.

Recent advancements in deep neural networks (DNNs) have significantly improved visual quality of camera captures under low-light (<10lx) conditions, yet visual quality in extreme low-light (<1lx) remains inadequate. Existing DNN models are computationally intensive and suffer from large processing times, making them impractical for real-time enhancement of high-resolution video. Consequently, Ultra HD (UHD) videos (4K/8K) captured in extreme low-light environments exhibit elevated noise and diminished detail. Developing DNN-based solutions for UHD video enhancement faces challenges including paired dataset creation, temporal consistency, and efficient deployment under strict latency (<33ms) and power constraints (<250mA for 30fps video).We present a comprehensive methodology for developing a real-time raw to raw denoising solution for UHD video in extreme low-light, designed for seamless integration into existing ISP pipelines. Unlike ISP-replacement approaches, our solution enhances commercial camera stacks across sensor platforms. Our framework comprises: (1) Diverse dataset creation methodology; (2) A low-complexity model architecture optimized for mobile compute elements; (3) Efficient training and post-training optimizations (reparameterization, restructuring, quantization) to meet latency constraints while ensuring high-quality output. The result is a power-efficient real-time raw to raw video denoiser that improves extreme low-light video quality while preserving downstream ISP behavior.

In this work, we investigate the potential of weights to serve as effective representations, focusing on neural fields. Our key insight is that constraining the optimization space through a pre-trained base model and low-rank adaptation (LoRA) can induce structure in weight space. Across reconstruction, generation, and analysis tasks on 2D and 3D data, we find that multiplicative LoRA weights achieve high representation quality while exhibiting distinctiveness and semantic structure. When used with latent diffusion models, multiplicative LoRA weights enable higher-quality generation than existing weight-space methods.

Test-Time Adaptation (TTA) is essential for enabling deep learning models to handle real-world data distribution shifts. However, current approaches face significant limitations: backpropagation-based methods are not suitable for low-end deployment devices, due to their high computation and memory requirements, as well as their tendency to modify model weights during adaptation; while traditional backpropagation-free techniques exhibit constrained adaptation capabilities. In this work, we propose Forward-Only Zeroth-Order Optimization (FOZO), a novel and practical backpropagation-free paradigm for TTA. FOZO leverages a memory-efficient zeroth-order prompt optimization, which is led by objectives optimizing both intermediate feature statistics and prediction entropy. To ensure efficient and stable adaptation over the out-of-distribution data stream, we introduce a dynamically decaying perturbation scale during zeroth-order gradient estimation and theoretically prove its convergence under the TTA data stream assumption. Extensive continual adaptation experiments on ImageNet-C, ImageNet-R, and ImageNet-Sketch demonstrate FOZO's superior performance, achieving 59.52% Top-1 accuracy on ImageNet-C (5K, level 5) and outperforming main gradient-based methods and SOTA forward-only FOA (58.13%). Furthermore, FOZO exhibits strong generalization on quantized (INT8) models. These findings demonstrate that FOZO is a highly competitive solution for TTA deployment in resource-limited scenarios. Code: https://github.com/eVI-group-SCU/FOZO

Diffusion-based video motion customization facilitates the acquisition of human motion representations from a few video samples, while achieving arbitrary subjects transfer through precise textual conditioning. Existing approaches often rely on semantic-level alignment, expecting the model to learn new motion concepts and combine them with other entities (e.g., cats or dogs) to produce visually appealing results. However, video data involve complex spatio-temporal patterns, and focusing solely on semantics cause the model to overlook the visual complexity of motion. Conversely, tuning only the visual representation leads to semantic confusion in representing the intended action. To address these limitations, we propose SynMotion, a new motion-customized video generation model that jointly leverages semantic guidance and visual adaptation. At the semantic level, we introduce the dual-embedding semantic comprehension mechanism which disentangles subject and motion representations, allowing the model to learn customized motion features while preserving its generative capabilities for diverse subjects. At the visual level, we integrate parameter-efficient motion adapters into a pre-trained video generation model to enhance motion fidelity and temporal coherence. Furthermore, we introduce a new embedding-specific training strategy which alternately optimizes subject and motion embeddings, supported by the manually constructed Subject Prior Video (SPV) training dataset. This strategy promotes motion specificity while preserving generalization across diverse subjects. Lastly, we introduce MotionBench, a newly curated benchmark with diverse motion patterns. Experimental results across both T2V and I2V settings demonstrate that SynMotion outperforms existing baselines.

Recent breakthroughs in 3D Gaussian Splatting (3DGS) have advanced neural rendering with high fidelity and speed. However, its performance degrades significantly in large-scale scenes due to the computational burden of tile-based rasterization. Existing optimization efforts either require costly scene re-training or focus on narrow aspects of the pipeline, overlooking critical inefficiencies in real-world deployments. Through a comprehensive analysis, we identify three primary sources of redundancy and low GPU utilization: redundant inter-frame pre-processing, viewpoint-based occlusion redundancy, and severe tile-level load imbalance. To address these issues, we propose CaT-GS, a novel and efficient 3DGS rendering pipeline. CaT-GS introduces a speculative multi-frame preprocessing method to eliminate redundant computations across consecutive frames, and an inter-frame caching mechanism to eliminate viewpoint redundant rendering stages. Furthermore, it refactors rasterization tasks with a dedicated kernel to mitigate tile load imbalance, significantly boosting GPU utilization. Extensive experiments demonstrate that CaT-GS achieves a speedup of up to 10 times over the original 3DGS and up to 70% over previous state-of-the-art methods, establishing a new benchmark for high-fidelity, real-time rendering of large-scale scenes.

RAW images preserve superior fidelity and rich scene information compared to RGB, making them essential for tasks in challenging imaging conditions. To alleviate the high cost of data collection, recent RGB-to-RAW conversion methods aim to synthesize RAW images from RGB. However, they overlook two key challenges: (i) the reconstruction difficulty varies with pixel intensity, and (ii) multi-camera conversion requires camera-specific adaptation. To address these issues, we propose SpiralDiff, a diffusion based framework tailored for RGB-to-RAW conversion with a signal-dependent noise weighting strategy that adapts reconstruction fidelity across intensity levels. In addition, we introduce CamLoRA, a camera-aware lightweight adaptation module that enables a unified model to adapt to different camera-specific ISP characteristics. Extensive experiments on four benchmark datasets demonstrate the superiority of SpiralDiff in RGB-to-RAW conversion quality and its downstream benefits in RAW-based object detection. Our code is available at https://github.com/Chuancy-TJU/SpiralDiff.

Federated learning (FL) in post-deployment settings must adapt to non-stationary data streams across heterogeneous clients without access to ground-truth labels. A major challenge is learning rate selection under client-specific, time-varying distribution shifts, where fixed learning rates often lead to underfitting or divergence. We propose Fed-ADE (Federated Adaptation with Distribution Shift Estimation), an unsupervised federated adaptation framework that leverages lightweight estimators of distribution dynamics. Specifically, Fed-ADE employs uncertainty dynamics estimation to capture changes in predictive uncertainty and representation dynamics estimation to detect covariate-level feature drift, combining them into a per-client, per-timestep adaptive learning rate. We provide theoretical analyses showing that our dynamics estimation approximates the underlying distribution shift and yields dynamic regret and convergence guarantees. Experiments on image and text benchmarks under diverse distribution shifts (label, covariate, and concept) demonstrate consistent improvements over strong baselines. These results highlight that distribution shift-aware adaptation enables effective and robust federated post-adaptation under real-world non-stationarity.

Visual place recognition (VPR) faces critical challenges in handling extreme environmental variations while meeting the computational constraints of practical applications. Current methods predominantly address these challenges by either scaling up model capacity or employing computationally intensive reranking stages, creating a significant efficiency bottleneck. To overcome this limitation, we propose EfficientVPR, a lightweight one-stage framework that achieves speed-accuracy trade-offs through two key innovations: i) a scene-aware visual prompt tuning method which adapts pretrained features with less parameters while dynamically adjusting to sample-specific characteristics, and ii) an instance-dependent key local feature enhancement module that further reinforces discriminative regions. Comprehensive evaluations on Pitts250k, MSLS, Eynsham, AmsterTime and SVOX demonstrate that our method establishes a new SOTA for DINOv2-small models by outperforming all same-scale competitors, and delivers a 73x speedup with 60% lower-dimensional features while maintaining competitive (within 2.5% average R@1 gap) against the SOTA DINOv2-large-based two-stage method. Our code will be available at https://github.com/WiniTang/EfficientVPR.

With the rapid advancement of diffusion models, talking face generation has made remarkable progress. However, existing diffusion-based methods still require task-specific fine-tuning and large-scale audiovisual datasets, resulting in high computational costs that hinder accessibility for resource-constrained researchers and small teams. To address this, we propose a fine-tuning-free paradigm that directly performs talking face generation using the pretrained weights of Stable Diffusion and IP-Adapter. This backbone leverages the visual embedding capability of IP-Adapter to mine lip-related semantics from the pretrained Stable Diffusion. To address the challenges of identity drift, synchronization errors, and temporal instability, we also design three trainable-parameter-free components: 1) the Structurist, which explicitly disentangles and reassembles lip and appearance features to mitigate identity drift and appearance distortion; 2) the Structure Controller, which adaptively refines embeddings based on quasi-monotonic motion trends for precise lip synchronization; and 3) the Noise Sensor, which introduces a Gaussian prior to detect and suppress flicker and jitter artifacts and enhance temporal consistency. Experimental results show that our method outperforms existing SOTA approaches in both lip-sync accuracy (at least 0.16 gain in PCLD) and visual fidelity (at least 0.7 improvement in FID), establishing a novel fine-tuning-free diffusion framework for talking face generation.

Vision-language models (VLMs) have demonstrated remarkable generalization across diverse tasks, yet their performance remains constrained by the quality and geometry of the textual prototypes used to represent classes. Standard zero-shot classifiers, derived from frozen text encoders and handcrafted prompts, may yield correlated or weakly separated embeddings that limit task-specific discriminability. We introduce ORION, a text encoder fine-tuning framework that improves pretrained VLMs using only class names. Our method optimizes, via low-rank adaptation, a novel loss integrating two terms, one promoting pairwise orthogonality between the textual representations of the classes of a given task and the other penalizing deviations from the initial class prototypes. Furthermore, we provide a probabilistic interpretation of our orthogonality penalty, connecting it to the general maximum likelihood estimation (MLE) principle via Huygens' theorem. We report extensive experiments on 11 benchmarks and three large VLM backbones, showing that the refined textual embeddings yield powerful replacements for the standard CLIP prototypes. Added as plug-and-play module on top of various state-of-the-art methods, and across different prediction settings (zero-shot, few-shot and test-time adaptation), ORION improves the performance consistently and significantly.

Synthesizing novel spatiotemporal views of dynamic scenes is challenging due to object and camera motion, and sparse observations. While recent Neural Radiance Field (NeRF) and Gaussian Splatting (GS) methods enable 4D dynamic scene reconstruction, they predominantly assume well-lit inputs. Existing low-light reconstruction approaches are limited to static scenes and mainly focus on brightness enhancement while overlooking underlying scene structure. Reconstructing well-lit dynamic scenes from low-light inputs is particularly challenging due to motion-induced shadows, occlusions, and disocclusions, making the problem highly ambiguous. We propose L^ 2 DGS (Low-Light Dynamic Gaussian Splatting), a self-supervised 4D GS framework that directly reconstructs well-lit dynamic scenes from low-light videos. The method decomposes the scene into view- and time-dependent illumination and view-time-invariant reflectance components. We introduce an Occlusion-Disocclusion Network (OCD-Net) to model temporal intensity variations and Brightness Attenuation Features (BAFs) with a BAF Enhancement Network (BAFE-Net) to enable geometry- and photometry-aware transformation between well-lit and low-light observations for self-supervision. L^ 2 DGS operates on standard sRGB inputs without requiring camera metadata. Experiments on simulated and proposed real Low-Light Dynamic Video (L^ 2 DyV) datasets demonstrate superior qualitative and quantitative performance over prior methods. The dataset is available at: \href https://github.com/akumar005/L2DGS https://github.com/akumar005/L2DGS .

Parameter Recombination (PR) methods aim to efficiently compose the weights of a neural network, and encompasses tasks like Parameter-Efficient FineTuning (PEFT) and Model Compression (MC), among others. Most methods typically focus on one application of PR, which can make composing them challenging. For example, when deploying a large model you may wish to compress the model and also quickly adapt to new settings. However, PEFT methods often can still contain millions of parameters. This may be small compared to the original model size, but can be problematic in resource constrained deployments like edge devices, where they take a larger portion of the compressed model's parameters. To address this, we present Coefficient-gated weight Recombination by Interpolated Shared basis Projections (\method ), a general approach that can address multiple PR tasks within the same framework, which can enable seamless integration. It accomplishes this by using a factorization process that decomposes pretrained weights into basis matrices and their component projections. Sharing these basis matrices across layers and adjusting its size enables us to perform MC, whereas the small size of the projection weights (fewer than 200 in some experiments) enables \method support PEFT. Experiments on ViT models show \method outperforms methods from prior work capable of dual-task applications by 4-5% while also outperforming the state-of-the-art in PEFT by 1.5% and PEFT+MC combinations by almost 1%.

Test-time training (TTT) enhances the robustness of pretrained models to out-of-distribution (OOD) data through auxiliary self-supervised tasks, without requiring labeled samples. However, existing TTT methods predominantly rely on decoder-based auxiliary objectives, which suffer from inefficient adaptation and weak coupling with the primary task. To solve these limitations, we revisit the mechanism of test-time training by analyzing masking-based pretrained models to uncover the fundamental source of their OOD robustness. Our investigation reveals that their generalization capability stems from a latent feature-level structural invariance, the consistency of encoded representations under masked perturbations. Building on this insight, we introduce LoTT-PC, a lightweight LoRA-based framework that operationalizes this invariance-preserving principle for 3D point cloud classification. LoTT-PC consists of two main components: (1) low-rank modulation units for parameter-efficient adaptation, and (2) a permutation-invariant alignment mechanism that enforces representation consistency through masked feature alignment. Extensive experiments on multiple benchmarks demonstrate that this unified design enables pretrained point cloud models to self-tune rapidly and reliably across diverse OOD scenarios, outperforming state-of-the-art methods by an average of 2.7% in accuracy under various corruption types.

Thermal infrared (TIR) imaging enables robust perception in adverse conditions. However, it often suffers from complex degradations (e.g., fixed-pattern noise and low-resolution) due to sensor limitations and environmental dynamics. Existing methods, whether traditional or learning-based, easily fail under composite and varying degradation. Pre-trained generative models showcase powerful capabilities for alleviating degradations but lack effective tools to adapt visible generative priors to TIR-specific characteristics. To overcome these challenges, we propose a Hierarchical Degradation Representation and Adaptation (HiDRA) framework to decompose the enhancement procedure into degradation representation estimation and generative model fine-tuning. The degradation representation estimation aims to disentangle TIR degradation patterns, which then guide the parameter adaptation for thermal image enhancement. Additionally, we introduce a hierarchical adaptation solution that aggregates learning across varying degradation levels, further improving robustness under various scenarios. Experiments across diverse types and degrees demonstrate the robustness of our approach and further validate its effectiveness on downstream tasks. Code is available at https://github.com/Zihang-Chen/HiDRA.

On-device AI systems increasingly adopt a single foundation model equipped with task-specific Low-Rank Adaptation (LoRA) modules, forming a multi-LoRA LLM that supports multiple tasks.We study how to adapt such a model to a new task on memory-constrainted devices.Although LoRA reduces trainable parameters, fine-tuning a full set of modules remains memory-intensive.To improve efficiency, we apply sparse updating, training a subset of LoRA modules within the memory budget.However, existing sparse updating methods assume all candidate parameters are instantiated and cannot estimate the importance of modules that do not yet exist, while prior memory models designed for sequential networks fail to capture the blockwise parallel structure of Transformers.We propose TaskIT, a framework for memory-efficient fine-tuning via cross-task importance transfer. TaskIT predicts pre-insertion module importance by transferring from previously tuned tasks and employs a block-based memory predictor that captures parallel and sequential dependencies of Transformer blocks. A dynamic programming scheduler then selects module locations, numbers, and ranks to maximize accuracy within the memory budget.Experiments on uni-modal and cross-modal benchmarks show that TaskIT achieves superior accuracy-memory tradeoffs compared with Zero-FT, non-LoRA, and LoRA-based fine-tuning methods.

2D Gaussian Splatting has emerged as a novel image representation technique that can support efficient rendering on low-end devices. However, scaling to high-resolution images requires optimizing and storing millions of unstructured Gaussian primitives independently, leading to slow convergence and redundant parameters. To address this, we propose Structured Gaussian Image (SGI), a compact and efficient framework for representing high-resolution images. SGI decomposes a complex image into multi-scale local spaces defined by a set of seeds. Each seed corresponds to a spatially coherent region and, together with lightweight multi-layer perceptrons (MLPs), generates structured implicit 2D neural Gaussians. This seed-based formulation imposes structural regularity on otherwise unstructured Gaussian primitives, which facilitates entropy-based compression at the seed level to reduce the total storage. However, optimizing seed parameters directly on high-resolution images is a challenging and non-trivial task. Therefore, we designed a multi-scale fitting strategy that refines the seed representation in a coarse-to-fine manner, substantially accelerating convergence. Quantitative and qualitative evaluations demonstrate that SGI achieves up to 7.5xcompression over prior non-quantized 2D Gaussian methods and 1.6xover quantized ones, while also delivering 1.6xand 6.5xfaster optimization, respectively, without degrading, and often improving, image fidelity. Code is available at https://github.com/zx-pan/SGI.