Language-conditioned robot manipulation in the continuous spectrum presents a persistent challenge due to the difficult of mapping states to target actions. Previous methods face limitations in effectively modeling object states, primarily due to their reliance on executing ambiguous instructions devoid of explicit state information. In response, we present SD^2Actor, a zero-shot robotic manipulation framework that possesses the capability to generate precise actions in continuous states. Specifically, given the novel instructions, we aim to generate instruction-following and accurate robot manipulation actions. Instead of time-consuming optimization and finetuning, our zero-shot method generalizes to any object state with a wide range of translations and versatile rotations. At its core, we quantify multiple base states in the training set and utilize their combination to refine the target action generated by the diffusion model. To obtain novel state representations, we initially employ LLMs to extract the novel state from the instruction and decompose it into multiple learned base states. We then employ the linear combination of base state embeddings to produce novel state features. Moreover, we introduce the orthogonalization loss to constrain the state embedding space, which ensures the validity of linear interpolation. Experiments demonstrate that SD^2Actor outperforms state-of-the-art methods across a diverse range of manipulation tasks in ARNOLD Benchmark. Moreover, SD^2Actor can effectively learn generalizable policies from a limited number of human demonstrations, achieving promising accuracy in a variety of real-world manipulation tasks.
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While deep models are effectively trained based on a softmax cross-entropy loss, a cosine-based softmax loss also works for producing favorable feature embedding.In the cosine-based softmax, temperature plays a crucial role in properly scaling the logits of cosine similarities, though being manually tuned in ad-hoc ways as there is less prior knowledge about the temperature.In this paper, we address the challenging problem to adaptively estimate the temperature of cosine-based softmax in the framework of supervised image classification.By analyzing the cosine-based softmax representation from a geometrical viewpoint regarding features and classifiers, we construct a criterion in a least-square fashion which enables us to optimize the temperature at each sample via simple greedy search.Besides, our thorough analysis about temperature clarifies that feature embedding by the cosine-based softmax loss is endowed with diverse characteristics which are controllable by the temperature in an explainable way.The experimental results demonstrate that our optimized temperature contributes to determine a feasible range of temperature to control the feature characteristics and produces favorable performance on various image classification tasks.
Unleashing High-Quality Image Generation in Diffusion Sampling Using Second-Order Levenberg-Marquardt-Langevin
PDF ↗The emerging diffusion models (DMs) have demonstrated the remarkable capability of generating images via learning the noised score function of the data distribution. Current DM sampling techniques typically rely on first-order Langevin dynamics at each noise level, with efforts concentrated on refining inter-level denoising strategies. While leveraging additional second-order Hessian geometry to enhance the sampling quality of Langevin is a common practice in Markov chain Monte Carlo (MCMC), the naive attempts to utilize Hessian geometry in high-dimensional DMs lead to quadratic-complexity computational costs, rendering them non-scalable. In this work, we introduce a novel Levenberg-Marquardt-Langevin (LML) method that approximates the diffusion Hessian geometry in a training-free manner, drawing inspiration from the celebrated Levenberg-Marquardt optimization algorithm. Our approach introduces two key innovations: (1) A low-rank approximation of the diffusion Hessian, leveraging the DMs' inherent structure and circumventing explicit quadratic-complexity computations; (2) A damping mechanism to stabilize the approximated Hessian. This LML approximated Hessian geometry enables the diffusion sampling to execute more accurate steps and improve the image generation quality. We further conduct a theoretical analysis to substantiate the approximation error bound of low-rank approximation and the convergence property of the damping mechanism. Extensive experiments across multiple pretrained DMs validate that the LML method significantly improves image generation quality, with negligible computational overhead.
In visual generation tasks, the responses and combinations of complex concepts often lack stability and are error-prone, which remains an under-explored area. In this paper, we attempt to explore the causal factors for poor concept responses through elaborately designed experiments. We also design a concept-wise equalization loss function (IMBA loss) to address this issue. Our proposed method is online, eliminating the need for offline dataset processing, and requires minimal code changes. In our newly proposed complex concept benchmark Inert-CompBench and two other public test sets, our method significantly enhances the concept response capability of baseline models and yields highly competitive results with only a few codes.
Detection transformers have been applied to human-object interaction (HOI) detection, enhancing the localization and recognition of human-action-object triplets in images. Despite remarkable progress, this study identifies a critical issue--"Toxic Siblings" bias--which hinders the interaction decoder's learning, as numerous similar yet distinct HOI triplets interfere with and even compete against each other both input side and output side to the interaction decoder. This bias arises from high confusion among sibling triplets/categories, where increased similarity paradoxically reduces precision, as one's gain comes at the expense of its toxic sibling's decline. To address this, we propose two novel debiasing learning objectives--"contrastive-then-calibration" and "merge-then-split"--targeting the input and output perspectives, respectively. The former samples sibling-like incorrect HOI triplets and reconstructs them into correct ones, guided by strong positional priors. The latter first learns shared features among sibling categories to distinguish them from other groups, then explicitly refines intra-group differentiation to preserve uniqueness. Experiments show that we significantly outperform both the baseline (+9.18% mAP on HICO-Det) and the state-of-the-art (+3.59% mAP) across various settings. The source code will be made public.
Temporal Overlapping Prediction: A Self-supervised Pre-training Method for LiDAR Moving Object Segmentation
PDF ↗Moving object segmentation (MOS) on LiDAR point clouds is crucial for autonomous systems such as self-driving vehicles. While previous supervised approaches rely on costly manual annotations, LiDAR sequences naturally capture temporal motion cues that can be leveraged for self-supervised learning. In this paper, we propose Temporal Overlapping Prediction (TOP), a self-supervised pre-training method designed to alleviate this annotation burden. TOP learns powerful spatiotemporal representations by predicting the occupancy states of temporal overlapping points that are commonly observed in current and adjacent scans. To further ground these representations in the current scene's geometry, we introduce an auxiliary pre-training objective of reconstructing the occupancy of the current scan. Extensive experiments on the nuScenes and SemanticKITTI datasets validate our method's effectiveness. TOP consistently outperforms existing supervised and self-supervised pre-training baselines across both point-level Intersection-over-Union (IoU) and object-level Recall metrics. Notably, it achieves a relative improvement of up to 28.77% over a training-from-scratch baseline and demonstrates strong transferability across LiDAR setups. Our code is publicly available at https://github.com/ZiliangMiao/TOP.
Breaking Rectangular Shackles: Cross-View Object Segmentation for Fine-Grained Object Geo-Localization
PDF ↗This paper addresses the limitations of existing cross-view object geo-localization schemes, which rely on rectangular proposals to localize irregular objects in satellite imagery. These "rectangular shackles" inherently struggle to precisely define objects with complex geometries, leading to incomplete coverage or erroneous localization. We propose a novel scheme, cross-view object segmentation (CVOS), which achieves fine-grained geo-localization by predicting pixel-level segmentation masks of query objects. CVOS enables accurate extraction of object shapes, sizes, and areas--critical for applications like urban planning and agricultural monitoring. We introduce the CVOGL-Seg dataset specifically to support and evaluate the new CVOS scheme. To tackle CVOS challenges, we propose Transformer Object Geo-localization (TROGeo), a two-stage framework. First, the Heterogeneous Task Training Stage (HTTS) employs a single transformer encoder with a Cross-View Object Perception Module (CVOPM) and is trained by learning a heterogeneous task. Second, the SAM Prompt Stage (SPS) utilizes SAM's zero-shot segmentation capability, guided by HTTS outputs, to generate precise masks. Extensive experiments on both CVOGL and CVOGL-Seg datasets demonstrate that our approach achieves state-of-the-art performance, effectively breaking the rectangular shackles and unlocking new possibilities for fine-grained object geo-localization. Our project page: https://zqwlearning.github.io/CVOS.
Current video models fail as world model as they lack fine-graiend control. General-purpose household robots require real-time fine motor control to handle delicate tasks and urgent situations. In this work, we introduce fine-grained multimodal actions to capture such precise control. We consider senses of proprioception, kinesthesia, force haptics, and muscle activation. Such multimodal senses naturally enables fine-grained interactions that are difficult to simulate with text-conditioned generative models. To effectively simulate fine-grained multisensory actions, we develop a feature learning paradigm that aligns these modalities while preserving the unique information each modality provides. We further propose a regularization scheme to enhance causality of the action trajectory features in representing intricate interaction dynamics. Experiments show that incorporating multimodal senses improves simulation accuracy and reduces temporal drift. Extensive ablation studies and downstream applications demonstrate the effectiveness and practicality of our work.
We present Style Matching Score (SMS), a novel optimization method for image stylization with diffusion models. Balancing effective style transfer with content preservation is a long-standing challenge. Unlike existing efforts, our method reframes image stylization as a style distribution matching problem. The target style distribution is estimated from off-the-shelf style-dependent LoRAs via carefully designed score functions. To preserve content information adaptively, we propose Progressive Spectrum Regularization, which operates in the frequency domain to guide stylization progressively from low-frequency layouts to high-frequency details. In addition, we devise a Semantic-Aware Gradient Refinement technique that leverages relevance maps derived from diffusion semantic priors to selectively stylize semantically important regions. The proposed optimization formulation extends stylization from pixel space to parameter space, readily applicable to lightweight feedforward generators for efficient one-step stylization. SMS effectively balances style alignment and content preservation, outperforming state-of-the-art approaches, verified by extensive experiments.
Image degradation synthesis is highly desirable in a wide variety of applications ranging from image restoration to simulating artistic effects. Existing models are designed to generate one specific or a narrow set of degradations, which often require user-provided degradation parameters. As a result, they lack the generalizability to synthesize degradations beyond their initial design or adapt to other applications. Here we propose the first universal degradation model that can synthesize a broad spectrum of complex and realistic degradations containing both homogeneous (global) and inhomogeneous (spatially varying) components. Our model automatically extracts and disentangles homogeneous and inhomogeneous degradation features, which are later used for degradation synthesis without user intervention. A disentangle-by-compression method is proposed to separate degradation information from images. Two novel modules for extracting and incorporating inhomogeneous degradations are created to model inhomogeneous components in complex degradations. We demonstrate the model's accuracy and adaptability in film-grain simulation and blind image restoration tasks. The demo video, code, and dataset of this project will be released at Yang's GitHub account.
Scene coordinate regression (SCR) models have proven to be powerful implicit scene representations for 3D vision, enabling visual relocalization and structure-from-motion. SCR models are trained specifically for one scene. If training images imply insufficient multi-view constraints to recover the scene geometry, SCR models degenerate. We present a probabilistic reinterpretation of training SCR models, which allows us to infuse high-level reconstruction priors. We investigate multiple such priors, ranging from simple priors over the distribution of reconstructed depth values to learned priors over plausible scene coordinate configurations. For the latter, we train a 3D point cloud diffusion model on a large corpus of indoor scans. Our priors push predicted 3D scene points towards a more plausible geometry at each training step to increase their likelihood. On three indoor datasets our priors help learning better scene representations, resulting in more coherent scene point clouds, higher registration rates and better camera poses, with a positive effect on down-stream tasks such as novel view synthesis and camera relocalization.
Despite the rapid advancements in video generation technology, creating high-quality videos that precisely align with user intentions remains a significant challenge. Existing methods often fail to achieve fine-grained control over video details, limiting their practical applicability. We introduce AnyPortal, a novel zero-shot framework for video background replacement that leverages pre-trained diffusion models. Our framework collaboratively integrates the temporal prior of video diffusion models with the relighting capabilities of image diffusion models in a zero-shot setting. To address the critical challenge of foreground consistency, we propose a Refinement Projection Algorithm, which enables pixel-level detail manipulation to ensure precise foreground preservation. AnyPortal is training-free and overcomes the challenges of achieving foreground consistency and temporally coherent relighting. Experimental results demonstrate that AnyPortal achieves high-quality results on consumer-grade GPUs, offering a practical and efficient solution for video content creation and editing.
Diffusion models have demonstrated remarkable success in image restoration tasks. However, their multi-step denoising process introduces significant computational overhead, limiting their practical deployment. Furthermore, existing methods struggle to effectively remove severe JPEG artifact, especially in highly compressed images. To address these challenges, we propose CODiff, a compression-aware one-step diffusion model for JPEG artifact removal. The core of CODiff is the compression-aware visual embedder (CaVE), which extracts and leverages JPEG compression priors to guide the diffusion model. We propose a dual learning strategy that combines explicit and implicit learning. Specifically, explicit learning enforces a quality prediction objective to differentiate low-quality images with different compression levels. Implicit learning employs a reconstruction objective that enhances the model's generalization. This dual learning allows for a deeper and more comprehensive understanding of JPEG compression. Experimental results demonstrate that CODiff surpasses recent leading methods in both quantitative and visual quality metrics. The code is released at https://github.com/jp-guo/CODiff.
Recent advances in vision-language models (VLMs) have significantly improved performance in embodied tasks such as goal decomposition and visual comprehension. However, providing accurate rewards for robotic manipulation without fine-tuning VLMs remains challenging due to the absence of domain-specific robotic knowledge in pre-trained datasets and high computational costs that hinder real-time applicability. To address this, we propose T2-VLM, a novel training-free, temporally consistent framework that generates accurate rewards through tracking the status changes in VLM-derived subgoals. Specifically, our method first queries the VLM to establish spatially aware subgoals and an initial completion estimate before each round of interaction. We then employ a Bayesian tracking algorithm to update the goal completion status dynamically, using subgoal hidden states to generate structured rewards for reinforcement learning (RL) agents. This approach enhances long-horizon decision-making and improves failure recovery capabilities with RL. Extensive experiments indicate that T2-VLM achieves state-of-the-art performance in two robot manipulation benchmarks, demonstrating superior reward accuracy with reduced computation consumption. We believe our approach not only advances reward generation techniques but also contributes to the broader field of embodied AI. Project website: https://t2-vlm.github.io/.
Predicting the motion of other agents in a scene is highly relevant for autonomous driving, as it allows a self-driving car to anticipate. Inspired by the success of decoder-only models for language modeling, we propose DONUT, a Decoder-Only Network for Unrolling Trajectories. Unlike existing encoder-decoder forecasting models, we encode historical trajectories and predict future trajectories with a single autoregressive model. This allows the model to make iterative predictions in a consistent manner, and ensures that the model is always provided with up-to-date information, thereby enhancing performance. Furthermore, inspired by multi-token prediction for language modeling, we introduce an 'overprediction' strategy that gives the model the auxiliary task of predicting trajectories at longer temporal horizons. This allows the model to better anticipate the future and further improves performance. Through experiments, we demonstrate that our decoder-only approach outperforms the encoder-decoder baseline, and achieves new state-of-the-art results on the Argoverse 2 single-agent motion forecasting benchmark.
Foundation models that bridge vision and language have made significant progress. While they have inspired many life-enriching applications, their potential for abuse in creating new threats remains largely unexplored. In this paper, we reveal that vision-language models (VLMs) can be weaponized to enhance gradient inversion attacks (GIAs) in federated learning (FL), where an FL server attempts to reconstruct private data samples from gradients shared by victim clients. Despite recent advances, existing GIAs struggle to reconstruct high-resolution images when the victim has a large local data batch. One promising direction is to focus reconstruction on valuable samples rather than the entire batch, but current methods lack the flexibility to target specific data of interest. To address this gap, we propose Geminio, the first approach to transform GIAs into semantically meaningful, targeted attacks. It enables a brand new privacy attack experience: attackers can describe, in natural language, the data they consider valuable, and Geminio will prioritize reconstruction to focus on those high-value samples. This is achieved by leveraging a pretrained VLM to guide the optimization of a malicious global model that, when shared with and optimized by a victim, retains only gradients of samples that match the attacker-specified query. Geminio can be launched at any FL round and has no impact on normal training (i.e., the FL server can steal clients' data while still producing a high-utility ML model as in benign scenarios). Extensive experiments demonstrate its effectiveness in pinpointing and reconstructing targeted samples, with high success rates across complex datasets and large batch sizes with resilience against defenses.
Lightweight and Fast Real-time Image Enhancement via Decomposition of the Spatial-aware Lookup Tables
PDF ↗The image enhancement methods based on 3D lookup tables (3D LUTs) efficiently reduce both model size and runtime by interpolating pre-calculated values at the vertices. However, the 3D LUT methods have a limitation due to their lack of spatial information, as they convert color values on a point-by-point basis. Although spatial-aware 3D LUT methods address this limitation, they introduce additional modules that require a substantial number of parameters, leading to increased runtime as image resolution increases. To address this issue, we propose a method for generating image-adaptive LUTs by focusing on the redundant parts of the tables. Our efficient framework decomposes a 3D LUT into a linear sum of low-dimensional LUTs and employs singular value decomposition (SVD). Furthermore, we enhance the modules for spatial feature fusion to be more cache-efficient. Extensive experimental results demonstrate that our model effectively decreases both the number of parameters and runtime while maintaining spatial awareness and performance. The code is available at https://github.com/WontaeaeKim/SVDLUT.
Text-to-image latent diffusion models (LDMs) have recently emerged as powerful generative models with great potential for solving inverse problems in imaging. However, leveraging such models in a Plug & Play (PnP), zero-shot manner remains challenging because it requires identifying a suitable text prompt for the unknown image of interest. Also, existing text-to-image PnP approaches are highly computationally expensive. We herein address these challenges by proposing a novel PnP inference paradigm specifically designed for embedding generative models within stochastic inverse solvers, with special attention to Latent Consistency Models (LCMs), which distill LDMs into fast generators. We leverage our framework to propose LAtent consisTency INverse sOlver (LATINO), the first zero-shot PnP framework to solve inverse problems with priors encoded by LCMs. Our conditioning mechanism avoids automatic differentiation and reaches SOTA quality in as little as 8 neural function evaluations. As a result, LATINO delivers remarkably accurate solutions and is significantly more memory and computationally efficient than previous approaches. We then embed LATINO within an empirical Bayesian framework that automatically calibrates the text prompt from the observed measurements by marginal maximum likelihood estimation. Extensive experiments show that prompt self-calibration greatly improves estimation, allowing LATINO with PRompt Optimization to define new SOTAs in image reconstruction quality and computational efficiency. The code is available at latino-pro.github.io
Generative AI (GenAI), which revolutionized both computer vision and natural language processing, has drawn continuous attention recently. Benefits from GenAI with the evolution of large language models (LLMs), the image generation task evolved from prompt-based to dialogue-based, which takes the real-world human intent expressed through conversations. When breaking this task into multiple steps, the best pathway of analyzing the dialogues is not determined, such as whether the objects or prompted template should be focused on the first step of dialogues analyzing. Thus, a multi-chain reasoning is requested to decompose this application beyond a pure chain-of-thought structure. After the divergent process, the question comes to how to converge the thinking chain that leads to the best matched image, which requires a new evaluation method to lead the thinking process. To address these challenges, we propose the LLM Thought Divergence and Convergence (LTDC) framework, which simulates human cognitive processes through three phases: (1) The Step-by-Step Thought process decomposes dialogue-based image generation tasks into sequential thinking chains using LLMs; (2) The Image Generation process creates image prompts following these thought instructions and produces corresponding images; (3) The Evaluation process aligns the coherence between generated images and dialogues through a multi-modal LLM, guiding the selection of optimal thinking chains. Evaluated on VisDial, our LTE framework achieves a 4.87% improvement in CLIP similarity, demonstrating the effectiveness in generating images with higher semantic fidelity.
TimeFormer: Capturing Temporal Relationships of Deformable 3D Gaussians for Robust Reconstruction
PDF ↗Dynamic scene reconstruction is a long-term challenge in 3D vision. Recent methods extend 3D Gaussian Splatting to dynamic scenes via additional deformation fields and apply explicit constraints like motion flow to guide the deformation. However, they learn motion changes from individual timestamps independently, making it challenging to reconstruct complex scenes, particularly when dealing with violent movement, extreme-shaped geometries, or reflective surfaces. To address the above issue, we design a plug-and-play module called TimeFormer to enable existing deformable 3D Gaussians reconstruction methods with the ability to implicitly model motion patterns from a learning perspective. Specifically, TimeFormer includes a Cross-Temporal Transformer Encoder, which adaptively learns the temporal relationships of deformable 3D Gaussians. Furthermore, we propose a two-stream optimization strategy that transfers the motion knowledge learned from TimeFormer to the base stream during the training phase. This allows us to remove TimeFormer during inference, thereby preserving the original rendering speed. Extensive experiments in the multi-view and monocular dynamic scenes validate qualitative and quantitative improvement brought by TimeFormer.Project