Knowledge catalogue
arXiv:2603.19036v2 Announce Type: replace Abstract: Single image reflection removal (SIRR) is challenging in real scenes, where reflection strength varies spatially and reflection patterns are tightly
arXiv:2607.01049v1 Announce Type: new Abstract: Industrial inspection requires more than binary anomaly detection: a practical system should determine whether an anomaly exists, localize the defective
arXiv:2603.13859v2 Announce Type: replace Abstract: Intrinsic image decomposition aims to estimate physically based rendering (PBR) parameters such as albedo, roughness, and metallicity from images. W
arXiv:2607.00887v1 Announce Type: new Abstract: Low-altitude Unmanned Aerial Vehicles (UAVs) often need to infer channel knowledge across a range of heights from only sparse observations collected at
arXiv:2512.09112v3 Announce Type: replace Abstract: Recent progress in text-to-video generation has achieved remarkable realism, yet fine-grained control over camera motion and orientation remains elu
arXiv:2607.00920v1 Announce Type: new Abstract: Real-world e-commerce image editing often requires multiple, localized, and auditable operations rather than global restyling. This compositional nature
arXiv:2607.01089v1 Announce Type: cross Abstract: Active learning reduces labeling cost by querying the most informative unlabeled samples, but standard coreset methods ignore known data symmetries an
arXiv:2602.03370v2 Announce Type: replace Abstract: Handwritten mathematical expression recognition (HMER) requires reasoning over diverse symbols and structures, yet autoregressive models struggle wi
arXiv:2502.07221v4 Announce Type: replace Abstract: To overcome the black-box nature of predictive AI and the hallucination risks of generative models, retrieval-based models offer an interpretable, e
arXiv:2607.00428v1 Announce Type: new Abstract: CLIP (Contrastive Language-Image Pre-training) has become a de facto paradigm for image-text alignment, but it struggles with long-context descriptions
arXiv:2607.00696v1 Announce Type: new Abstract: Long-horizon egocentric question answering involves answering about events that have occurred hours or days in the past. This requires memory representa
arXiv:2603.16943v2 Announce Type: replace-cross Abstract: Skeleton-based action recognition is widely applied in sensor-based systems, including human-computer interaction and intelligent surveillance
arXiv:2607.00482v1 Announce Type: new Abstract: Reasoning language models frequently overthink: generating extended chains of behaviors such as hedging, approach abandonment, and self contradiction th
arXiv:2606.17416v2 Announce Type: replace-cross Abstract: Multilingual speaker verification remains challenging because language-dependent acoustic variability causes speaker identity to become entang
arXiv:2607.00257v1 Announce Type: new Abstract: Accurate prediction of complex dynamical systems from noisy measurements remains a significant challenge in scientific computing. Kernel ridge regressio
arXiv:2607.00374v1 Announce Type: cross Abstract: Composed Image Retrieval (CIR) retrieves a target image from a reference image and a textual modification. While supervised CIR relies on costly tripl
arXiv:2607.00251v1 Announce Type: cross Abstract: While most image deblurring techniques directly restore the spatial image variable, we propose an amplitude and phase decomposition recognizing the im
arXiv:2607.00375v1 Announce Type: new Abstract: We present LIST3R, an instance-aware framework for long-sequence 3D reconstruction inspired by the way humans organize spatial memory around stable and
arXiv:2607.00733v1 Announce Type: cross Abstract: Mechanistic modeling via ordinary differential equations (ODEs) provides interpretable descriptions of complex dynamics and enables inference of under
arXiv:2607.01002v1 Announce Type: cross Abstract: In long-context use, large language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pastin
arXiv:2510.24434v3 Announce Type: replace Abstract: The effectiveness of instruction-tuned Large Language Models (LLMs) is often limited in low-resource linguistic settings due to a lack of high-quali
arXiv:2607.00304v1 Announce Type: cross Abstract: The bias-reliability tradeoff conjectures that LLM evaluation systems are constrained in (gamma, H, CV) space, where evaluator coupling (gamma), strat
arXiv:2607.00409v1 Announce Type: new Abstract: Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into accurate pixel-level pr
arXiv:2607.00371v1 Announce Type: cross Abstract: Visual AutoRegressive modeling (VAR) has pioneered a coarse-to-fine multi-scale autoregressive generative paradigm, demonstrating strong capabilities
arXiv:2607.00138v1 Announce Type: new Abstract: MG-SpaIR is a training-data-free framework for restoring a clean image from a single observation corrupted by a mixture of blur, downsampling, noise, an
arXiv:2607.00410v1 Announce Type: new Abstract: Recent brain decoding studies have made substantial progress in reconstructing externally perceived visual content from neural signals. However, using e
arXiv:2601.11641v3 Announce Type: replace Abstract: While Diffusion Transformers (DiTs) have achieved notable progress in video generation, this long-sequence generation task remains constrained by th
arXiv:2602.21397v2 Announce Type: replace Abstract: Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying pretrai
arXiv:2607.00259v1 Announce Type: cross Abstract: Test-Time Adaptation (TTA) seeks to improve model robustness under distribution shifts by adapting parameters using unlabeled target data. However, in
arXiv:2607.00671v1 Announce Type: cross Abstract: Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role. Ex
arXiv:2607.00461v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are often constrained by a language-space bottleneck, forcing complex visual reasoning into discrete tokens whi
arXiv:2505.19614v2 Announce Type: replace-cross Abstract: Multimodal learning has seen remarkable progress, particularly with large-scale pre-training across various modalities. Most current approache
arXiv:2607.00647v1 Announce Type: new Abstract: Training-free guidance (TFG) steers a pretrained diffusion model toward a desired attribute at inference. To be effective, this guidance must be applied
arXiv:2505.19889v3 Announce Type: replace Abstract: Visual fall detection models are usually trained on small, staged datasets. Their real-world utility remains unclear; such data lacks diversity and
arXiv:2607.00881v1 Announce Type: new Abstract: Spatial intelligence remains a persistent challenge for Multimodal Large Language Models (MLLMs), as it requires coherent spatial scene representations
Reinforcement learning (RL) finetuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision language models (VLMs). Wh
arXiv:2603.10834v4 Announce Type: replace-cross Abstract: Understanding how neural networks rely on visual cues offers a human-interpretable view of their internal decision processes. The cue-conflict
arXiv:2510.24636v3 Announce Type: replace Abstract: Reward models (RMs) have become essential for aligning large language models (LLMs), serving as scalable proxies for human evaluation in both traini
arXiv:2607.00832v1 Announce Type: cross Abstract: A single panorama captures the full visual sphere from one camera center, yet confines users to looking around in place without enabling true scene ex
arXiv:2607.00716v1 Announce Type: cross Abstract: Skeleton-based action recognition has achieved remarkable success by exploiting joint coordinates and their topological connections, yet prevailing me
arXiv:2607.01191v1 Announce Type: new Abstract: Fine-grained visual reasoning remains challenging for vision-language models, especially when small but critical visual cues are buried in high-resoluti
arXiv:2607.00115v1 Announce Type: new Abstract: This paper explores multi-turn visual reasoning and observes that MLLMs repeatedly fail to localize the target, leading to long, redundant trajectories.
arXiv:2607.00012v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, effectively mitigating their inherent
arXiv:2509.21013v4 Announce Type: replace-cross Abstract: Given the prohibitive cost of pre-training large language models, it is essential to leverage smaller proxy models to optimize datasets before
arXiv:2607.00983v1 Announce Type: new Abstract: Video understanding is often plagued by severe temporal redundancy, where processing dense frame sequences is both semantically inefficient and computat
arXiv:2607.00974v1 Announce Type: cross Abstract: Static radio maps provide location-dependent propagation priors, but they cannot capture short-term blockage caused by moving objects. Direct sensing-
arXiv:2607.01179v1 Announce Type: cross Abstract: Scaling inference compute, by generating many parallel attempts per problem, is a costly but reliable lever for improving language model capabilities.
arXiv:2607.00378v1 Announce Type: new Abstract: Colored sectors in a microbial range expansion encode more than lineage survival counts. We formulate a computer-vision inverse problem: from one endpoi
arXiv:2607.00147v1 Announce Type: new Abstract: Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructu
arXiv:2511.04694v5 Announce Type: replace-cross Abstract: As large language model (LLM) based systems take on high-stakes roles in real-world decision-making, they must reconcile competing instruction
arXiv:2607.01140v1 Announce Type: new Abstract: Open-vocabulary 3D Gaussian segmentation is challenging because it requires language understanding for diverse queries and accurate separation of Gaussi
arXiv:2607.00522v1 Announce Type: new Abstract: Restoring incomplete or damaged 3D objects is crucial for cultural heritage preservation, occluded object reconstruction, and artistic design. Existing
arXiv:2607.00606v1 Announce Type: new Abstract: Recent work on Thinking with Images makes vision a dynamic part of reasoning, but does so through generation: the model invokes external tools, synthesi
arXiv:2603.19122v2 Announce Type: replace Abstract: Class-conditional generative models have emerged as accurate and robust classifiers, with diffusion models demonstrating clear advantages over other
arXiv:2607.00377v1 Announce Type: new Abstract: Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision - a parad
arXiv:2607.00248v1 Announce Type: new Abstract: We present Seed2.0, a model series that takes a meaningful step toward solving complex, real-world tasks. Our approach begins with identifying users' ge
arXiv:2603.17920v2 Announce Type: replace Abstract: Semantic segmentation for uncrewed aerial vehicles (UAVs) is fundamental for aerial scene understanding, yet existing RGB and RGB-T datasets remain
arXiv:2607.00124v1 Announce Type: new Abstract: Object-centric models inspired by DETR have become the dominant paradigm for open-vocabulary video instance segmentation (OV-VIS). While recent efforts
arXiv:2607.00423v1 Announce Type: new Abstract: Vision language models (VLMs) demonstrate strong zero-shot performance, but often perpetuate social stereotypes in person-centric queries, yielding skew
arXiv:2607.00369v1 Announce Type: new Abstract: Domain adaptive visual object tracking under adverse weather conditions has garnered significant attention in recent years. Despite the impressive perfo