Disentanglement-based methods for learning shared representations are widely used in multimodal sentiment analysis. However, existing methods often overlook potential emotional conflicts across modalities within the same sample. They typically adopt intra-modal reconstruction and rely on similarity losses to align shared representations, which may distort the shared semantics under such conflicts. To address these, we propose a Conflict-aware Adaptive Cross-Reconstruction approach (CACR). First, we formally define emotional conflict and design a conflict-aware weighting strategy. This strategy calculates conflict scores for each modality and maps them to the corresponding cross-reconstruction weights. Second, we construct a cross-reconstruction module. It reconstructs each target modality using its own specific features and the shared features of other modalities, achieving implicit alignment of shared representations. Coupled with the aforementioned weights, this module effectively suppresses conflicting modalities and mitigates semantic ambiguity. Furthermore, we develop a fine-grained sentiment refinement module that captures fine-grained cues from visual- and audio-specific features to supplement textual semantics. Extensive experiments on three datasets show that CACR outperforms existing state-of-the-art methods, demonstrating its effectiveness in handling emotional conflict.
论文检索
输入标题、作者或关键词，从 9,654 篇学术成果中精准定位
Learning to See Through a Baby's Eyes: Early Visual Diets Enable Robust Visual Intelligence in Humans and Machines
PDF ↗Newborns perceive the world with low-acuity, color-degraded, and temporally continuous vision, which gradually sharpens as infants develop. To explore the ecological advantages of such staged "visual diets", we train self-supervised learning (SSL) models on object-centric videos under constraints that simulate infant vision: grayscale-to-color (C), blur-to-sharp (A), and preserved temporal continuity (T)--collectively termed CATDiet. For evaluation, we establish a comprehensive benchmark across ten datasets, covering clean and corrupted image recognition, texture-shape cue conflict tests, silhouette recognition, depth-order classification, and the visual cliff paradigm.All CATDiet variants demonstrate enhanced robustness in object recognition, despite being trained solely on object-centric videos. Remarkably, models also exhibit biologically aligned developmental patterns, including neural plasticity changes mirroring synaptic density in macaque V1 and behaviors resembling infants' visual cliff responses. Building on these insights, CombDiet initializes SSL with CATDiet before standard training while preserving temporal continuity. Trained on object-centric or head-mounted infant videos, CombDiet outperforms standard SSL on both in-domain and out-of-domain object recognition and depth perception. Together, these results suggest that the developmental progression of early infant visual experience offers a powerful reverse-engineering framework for understanding the emergence of robust visual intelligence in machines. All code, data, and models are available at Github.
We propose Mesh4D, a feed-forward model for monocular 4D mesh reconstruction. Given a monocular video of a dynamic object, our model reconstructs the object's complete 3D shape and motion, represented as a deformation field. Our key contribution is a compact latent space that encodes the entire animation sequence. This latent space is learned by an autoencoder that, during training, is guided by the skeletal structure of the training objects, providing strong priors on plausible deformations. Crucially, skeletal information is not required at inference time. The encoder employs spatio-temporal attention, yielding a more stable representation of the object's overall deformation. Building on this representation, we train a latent diffusion model that, conditioned on the input video and the mesh reconstructed from the first frame, predicts the full animation in one shot. We evaluate Mesh4D on reconstruction and novel view synthesis benchmarks, outperforming prior methods in recovering accurate 3D shape and deformation.
URICA: A Uniformity Region Affine Identifier Capture Algorithm for Arbitrary Region Retrieval in Pathology Images
PDF ↗Whole slide image (WSI) region retrieval remains an open challenge in computational pathology, as existing methods struggle to represent and preserve information of all possible regions. Current approaches that rely on fixed-size patches or slide-level retrieval are misaligned with real clinical workflows, where pathologists often examine WSI regions of arbitrary orientations and sizes rather than predefined patches or slides. In this work, we redefine WSI retrieval as a semantically optimal matching problem between arbitrary regions under spatial transformations, which necessitates a region-level representation that maintains semantic consistency. To fulfill this requirement, we introduce semantic tessellation, which organizes patch units into flexible, geometry-aware region descriptors. Building on this representation, we develop the affine identifier, a semantic signature that enables rotation- and scale-consistent region matching. We further derive theoretical bounds between the tessellation-derived descriptors and the ideal pixel-level semantic mask objective, showing that they reliably approximate mask-based region similarity. Together, these components form URICA, a theoretically grounded algorithm for robust WSI region retrieval. Experiments on large public datasets demonstrate that URICA achieves strong and consistent performance across diverse WSI retrieval tasks.
Virtual task assistants must recognize and explain users' mistakes to provide effective and corrective guidance. In this paper, we address the problem of error reasoning in long task videos, which is to detect and explain errors. Although recent Vision-Language Models (VLMs) demonstrate strong capabilities in visual question answering, they struggle to attend to the sparse spatiotemporal cues associated with errors in long task videos. We introduce an error reasoning framework, AXG-Reasoner, that leverages a frozen VLM in conjunction with a proposed Action eXecution Graph (AXG) and a temporal action segmentation (TAS) model, obtained and learned from normal (error-free) videos. To enable VLMs to attend to the sparse spatiotemporal cues associated with errors, we decompose each action segment of the video, obtained by TAS, into a sequence of fine-grained subactions by aligning it with the AXG. For each subaction segment, we query the VLM using a small number of keyframes and enhanced prompts to detect and explain errors, enabling data efficiency. To avoid costly manual subaction annotations, we develop a method to automatically construct AXG from training videos using foundation models. Extensive experiments on EgoPER and CaptainCook4D show that our method consistently improves over VLM baselines in error explanation by effectively identifying spatiotemporal cues and achieves state-of-the-art performance in error detection.
Style-GRPO: Semantic-Aware Preference Optimization for Image Style Transfer Guided by Reward Modeling
PDF ↗Recent progress in text-to-image generation has greatly advanced visual fidelity and creativity, but it has also imposed higher demands on prompt complexity--particularly in encoding intricate spatial relationships. In such cases, achieving satisfactory results often requires multiple sampling attempts.To address this challenge, we introduce a novel method that strengthens the spatial understanding of current image generation models. We first construct the **SpatialReward-Dataset** with over 80k preference pairs. Building on this dataset, we build **SpatialScore**, a reward model designed to evaluate the accuracy of spatial relationships in text-to-image generation, achieving performance that even surpasses leading proprietary models on spatial evaluation.% Through rigorous data curation and filtering, **SpatialScore** surpasses several leading vision-language models (VLMs) and existing reward models on spatial evaluation. We further demonstrate that this reward model effectively enables online reinforcement learning for the complex spatial generation. Extensive experiments across multiple benchmarks show that our specialized reward model yields significant and consistent gains in spatial understanding for image generation. All models and datasets will be released.
We present IMDD-1M, the first large-scale Industrial Multimodal Defect Dataset comprising 1,000,000 aligned image-text pairs, designed to advance multimodal learning for manufacturing and quality inspection. IMDD-1M contains high-resolution real-world defects spanning over 60 material categories and more than 400 defect types, each accompanied by expert-verified annotations and fine-grained textual descriptions detailing defect location, severity, and contextual attributes. This dataset enables a wide spectrum of applications, including classification, segmentation, retrieval, captioning, and generative modeling. Building upon IMDD-1M, we train a diffusion-based vision-language foundation model from scratch, specifically tailored for industrial scenarios. The model serves as a generalizable foundation that can be efficiently adapted to specialized domains through lightweight fine-tuning. With less than 5% of the task-specific data required by dedicated expert models, it achieves comparable performance, highlighting the potential of data-efficient foundation model adaptation for industrial inspection and generation, paving the way for scalable, domain-adaptive, and knowledge-grounded manufacturing intelligence.
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.
Dual-Prototype-Guided Multi-task Learning for Unsupervised Anomaly Detection and Classification
PDF ↗Unsupervised Anomaly Detection (UAD) and anomaly classification are frequently used in industrial and medical scenarios. Specifically, UAD identifies anomalous regions at the fine-grained pixel-level, while anomaly classification distinguishes anomaly types at the anomaly region level. However, existing approaches typically treat these tasks independently and sequentially, overlooking the benefits of jointly training them to suppress Local Visual Ambiguity (LVA) caused by the similarities of different types of anomalies in local visual patterns. Moreover, a multi-task learning framework cannot be directly applied to jointly train the two tasks, since UAD and anomaly classification exhibit feature preference incompatibility. To address these limitations, we propose the Prototype-Guided Semi-Supervised Feature Disentanglement (PG-SFD) framework, which makes a paradigm shift from implicit feature sharing to explicit feature disentanglement and explicitly constructs normal and category prototypes to eliminate implicit normal-abnormal semantic coupling via a Dual-Prototype Disentanglement Module (DPRM). Moreover, for cross-task feature differential injection and gradient conflict mitigation, the Differential Gated Interaction (DGI) and Geometry-Regularized Optimization (GRO) are proposed to form a cohesive framework with DPRM. PG-SFD demonstrates high effectiveness in both UAD tasks and weakly supervised classification tasks. Meanwhile, it exhibits stable performance across multiple types of datasets, including industrial and medical datasets, indicating its strong generalizability.
World Generation Models are emerging as a cornerstone of next-generation multimodal intelligence systems. Unlike traditional 2D visual generation, World Models aim to construct realistic, dynamic, and physically consistent 3D/4D worlds from images, videos, or text. These models not only need to produce high-fidelity visual content but also maintain coherence across space, time, physics, and instruction control, enabling applications in virtual reality, autonomous driving, Embodied Intelligence, and content creation.However, prior benchmarks, however, each emphasize different evaluation dimensions and lack a unified assessment of world-realism capability.To systematically evaluate World Models, we introduce the 4DWorldBench, which measures models across four key dimensions: Perceptual Quality, Condition-4D Alignment, Physical Realism, and 4D Consistency. The benchmark covers tasks such as Image-to-3D/4D, Video-to-4D, Text-to-3D/4D. Beyond these, we innovatively introduce adaptive conditioning across multiple modalities, which not only integrates but also extends traditional evaluation paradigms. To accommodate different modality-conditioned inputs, we map all modality conditions into a unified textual space during evaluation, and further integrate LLM-as-judge, MLLM-as-judge, and traditional network-based methods. This unified and adaptive design enables more comprehensive and consistent evaluation of alignment, physical Realism, and cross-modal coherence.Preliminary human studies further demonstrate that our adaptive tool selection achieves closer agreement with subjective human judgments.We hope this benchmark will serve as a foundation for objective comparisons and improvements, accelerating the transition from "visual generation" to "world generation." Our project can be found at https://yeppp27.github.io/4DWorldBench.github.io/.
ShotDirector: Directorially Controllable Multi-Shot Video Generation with Cinematographic Transitions
PDF ↗Shot transitions play a pivotal role in multi-shot video generation, as they determine the overall narrative expression and the directorial design of visual storytelling.However, recent progress has primarily focused on low-level visual consistency across shots, neglecting how transitions are designed and how cinematographic language contributes to coherent narrative expression. This often leads to mere sequential shot changes without intentional film-editing patterns. To address this limitation, we propose ShotDirector, an efficient framework that integrates parameter-level camera control and hierarchical editing-pattern-aware prompting.Specifically, we adopt a camera control module that incorporates 6-DoF poses and intrinsic settings to enable precise camera information injection. In addition, a shot-aware mask mechanism is employed to introduce hierarchical prompts aware of professional editing patterns, allowing fine-grained control over shot content. Through this design, our framework effectively combines parameter-level conditions with high-level semantic guidance, achieving film-like controllable shot transitions.To facilitate training and evaluation, we construct ShotWeaver40K, a dataset that captures the priors of film-like editing patterns, and develop a set of evaluation metrics for controllable multi-shot video generation. Extensive experiments demonstrate the effectiveness of our framework.
Multi-Modal Representation Learning via Semi-Supervised Rate Reduction for Generalized Category Discovery
PDF ↗Generalized Category Discovery (GCD) aims to identify both known and unknown categories, with only partial labels given for the known categories, posing a challenging open-set recognition problem. State-of-the-art approaches for GCD are usually built on multi-modality representation learning, which pays heavily attention upon inter-modality alignment rather than intra-modality alignment. In this paper, we propose a novel and effective multi-modal representation learning approach for GCD via Semi-Supervised Rate Reduction, called SSR^2-GCD, to learn cross-modality representations with desired underlying structure properties via properly harnessing intra-modality alignment. Moreover, to boost knowledge transfer, we integrate prompt candidates by leveraging the inter-modal alignment offered by Vision Language Models. We conduct extensive experiments on generic and fine-grained benchmark datasets, demonstrating superior performance of the proposed approach and verifying the importance of harnessing an proper intra-modality alignment.
Models for long-term point tracking are typically trained on large synthetic datasets. The performance of these models degrades in real-world videos due todifferent characteristics and the absence of dense ground-truth annotations.Self-training on unlabeled videos has been explored as a practical solution, but the quality of pseudo-labels strongly depends on the reliability of teacher predictions, which vary across frames and scenes.In this paper, we address the problem of real-world fine-tuning and introduce Verifier, a meta-model that learns to assess the reliability of tracker predictions and guide pseudo-label generation.Given candidate trajectories from multiple pretrained trackers, the verifier evaluates them per frame and selects the most trustworthy predictions to construct refined pseudo-label trajectories.When applied during fine-tuning, verifier-guided pseudo-labeling substantially improves the quality of supervision and enables data-efficient adaptation to unlabeled videos.Extensive experiments on four real-world benchmarks demonstrate that our approach achieves state-of-the-art results while requiring less data than prior self-training methods.
Efficient and robust feature matching is crucial for latency-sensitive and resource-constrained applications. However, many current semi-dense feature matching methods scale quadratically with spatial resolution, either due to transformer-based long-range context modeling or from redundant full correlation computations. To overcome these limitations, we present SLiM (Salient Lightweight Matching), a novel scalable feature matching method that delivers reliable matches with low memory footprint and latency, especially at high resolutions. Our approach introduces three key innovations: (1) a hybrid Conv-Mamba backbone for efficient cross-scale and cross-view feature extraction with linear complexity, (2) a training-free norm-based feature filtering mechanism, enabling sparse correlation that significantly reduces computation overhead during inference, and (3) a lightweight recurrent coordinate refinement that surpasses expectation-based regression in subpixel accuracy. Experimental results show that SLiM consistently achieves a strong accuracy-efficiency trade-off across indoor and outdoor benchmarks, with clear advantages in memory usage, inference speed, and high-resolution scalability. These results demonstrate the practical efficiency and strong scalability of SLiM. Project page: https://github.com/Band-127/SLiM
Reconstructing surface meshes from multi-view images has remained a core challenge in recent years. Most existing methods, whether implicit or explicit, depend on intermediate representations and post-processing steps like Marching Cubes or TSDF fusion, often resulting in artifacts and fragmented geometry. Directly optimizing explicit meshes is a promising approach. However, it presents two critical challenges. The first is how to adaptively refine mesh topology to capture detail without introducing degenerate faces. The second is how to maintain consistent UV coordinates for high-fidelity texturing as the mesh structure evolves. To overcome these, we propose ExMesh, a novel framework that directly optimizes explicit meshes by integrating differentiable optimization with discrete topology updates. Specifically, we introduce an adaptive vertex splitting and merging strategy, along with real-time UV maintenance, to enable coarse-to-fine optimization while preserving geometric integrity. To our knowledge, ExMesh is the first framework to seamlessly integrate discrete topology operations into a continuous differentiable optimization pipeline. Extensive experiments demonstrate that ExMesh achieves a balance among accuracy, computational efficiency, and mesh conciseness.
Despite the remarkable success of Vision-Language Models (VLMs), their performance on a range of complex visual tasks is often hindered by a "visual processing bottleneck": a propensity to lose grounding in visual evidence and exhibit a deficit in contextualized visual experience during prolonged generation. Drawing inspiration from human cognitive memory theory, which distinguishes short-term visually-dominant memory and long-term semantically-dominant memory, we propose VisMem, a cognitively-aligned framework that equips VLMs with dynamic latent vision memories, a short-term module for fine-grained perceptual retention and a long-term module for abstract semantic consolidation. These memories are seamlessly invoked during inference, allowing VLMs to maintain both perceptual fidelity and semantic consistency across thinking and generation. Extensive experiments across diverse visual benchmarks for understanding, reasoning, and generation reveal that VisMem delivers a significant average performance boost of 11.0% relative to the vanilla model and outperforms all counterparts, establishing a new paradigm for latent-space memory enhancement. The code will be available: https://github.com/YU-deep/VisMem.git.
Vision-Language Models (VLMs) have shown strong performance on User Interface (UI) grounding tasks, driven by their ability to process increasingly high-resolution screenshots. However, screenshots are tokenized into thousands of visual tokens (e.g., about 4,700 for 2K resolution), which incurs significant computational overhead and dilutes attention. In contrast, humans typically focus on regions of interest when interacting with UIs. In this work, we pioneer the task of efficient UI grounding. We propose FocusUI, an efficient UI grounding framework that selects patches most relevant to the instruction while preserving positional continuity for precise grounding. FocusUI addresses two key challenges: (1) eliminating redundant tokens in visual encoding by constructing patch-level supervision that fuses an instruction-conditioned score with a rule-based UI-graph score, down-weighting large homogeneous regions to select distinct and instruction-relevant visual tokens; and (2) preserving positional continuity during visual token selection. We find that general visual token pruning methods suffer from severe accuracy degradation on UI grounding tasks due to broken positional information. To address this, we introduce a PosPad strategy, which compresses each contiguous sequence of dropped visual tokens into a single special marker placed at the sequence's last index to preserve positional continuity. Comprehensive experiments on four grounding benchmarks demonstrate that FocusUI surpasses GUI-specific baselines. On the ScreenSpot-Pro benchmark, FocusUI-7B achieves a 3.7% performance improvement over GUI-Actor-7B. Even with only 30% visual token retention, the performance of FocusUI-7B drops by just 3.2%, while achieving up to 1.44x faster inference and 17% lower peak GPU memory.
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.
Accurate capture of human-object interaction from ubiquitous sensors like RGB cameras is important for applications in human understanding, gaming, and robot learning. However, inferring 4D interactions from a single RGB view is highly challenging due to the unknown object and human information, depth ambiguity, occlusion, and complex motion, which hinder consistent 3D and temporal reconstruction. Previous methods simplify the setup by assuming ground truth object template or constraining to a limited set of object categories. We present CARI4D, the first category-agnostic method that reconstructs spatially and temporarily consistent 4D human-object interaction at metric scale from monocular RGB videos. To this end, we propose a pose hypothesis selection algorithm that robustly integrates the individual predictions from foundation models, jointly refine them through a learned render-and-compare paradigm to ensure spatial, temporal and pixel alignment, and finally reasoning about intricate contacts for further refinement satisfying physical constraints. Experiments show that our method outperforms prior art by 35% on in-distribution dataset and 36% on unseen dataset in terms of reconstruction error. Our model generalizes beyond the training categories and thus can be applied zero-shot to in-the-wild internet videos. Our code and pretrained models will be publicly released.
SSM-Aware Token-Efficient VMamba via Adaptive Patch Pruning and Merging for Person Re-Identification
PDF ↗Person re-identification (Re-ID) requires a balance between discriminative capability and computational efficiency for real-world deployment. However, even the Visual State Space Model (SSM), despite its linear complexity, suffers from redundant computation due to dense token processing. We propose SSM-aware Token-Efficient VMamba (TE-VMamba), which integrates adaptive patch pruning and merging modules to reduce redundant tokens while preserving identity-discriminative cues. The layer-adaptive pruning strategy removes low-importance tokens in shallow layers to enhance efficiency, whereas the depth-aware merging strategy consolidates semantically similar tokens in deeper layers to improve representation compactness. Learnable layer-wise thresholds dynamically balance accuracy and computational cost across the network. On the Market-1501 benchmark, TE-VMamba reduces FLOPs by over 60%, achieving significant computational savings while maintaining competitive accuracy. These results highlight the potential of structured token reduction in state-space models for efficient and powerful person re-identification.