Past ArXiv / July 23, 2026

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Archive date 07/23/2026

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15 papers

Benchmark & Evaluation

1 VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers Xianghong Fang, Yuan Yuan, Dehan Kong, Tim G. J. Rudner

Benchmark & Evaluation

ArXiv

Paper 1 / arXiv:2607.19575 Open arXiv

Relevance 5 Novelty 6

Why selected: Matches criterion 4 closely: it is about visual tokenizer/VQ infrastructure for pre-trained vision foundation models and efficient adaptation of discrete visual tokenization.

Vector Quantization (VQ) underpins modern discrete visual tokenization. However, training quantization modules for state-of-the-art VQ-based models requires significant computational resources which, in practice, all but prevents the development of novel, cutting-edge VQ techniques under resource constraints. To address this limitation, we propose {\bf VQ-Transplant}, a simple framework that enables plug-and-play integration of new VQ modules into frozen, pre-trained tokenizers by replacing their native VQ modules. Crucially, the proposed transplantation process preserves all encoder-decoder parameters, obviating the need for costly end-to-end retraining when modifying the quantization method. To mitigate decoder-quantization mismatch, we introduce a lightweight decoder adaptation strategy (trained for only 5 epochs on ImageNet-1k) to align feature priors with the new quantization space. In our empirical evaluation, we find that VQ-Transplant allows obtaining near state-of-the-art reconstruction fidelity for industry-level models like VAR while reducing the training cost by 95\%. VQ-Transplant democratizes quantization research by enabling resource-efficient integration of novel VQ techniques while matching industry-level reconstruction performance.

13 OffNadirLoc: Benchmark and Framework for Challenging UAV-to-Satellite Geo-Localization under Large Off-Nadir Views Qian Qiao, Wenye Liu, Ting Liu, Jiuhe Shu, Peng Wang

Benchmark & Evaluation LLM Interpretability

ArXiv

Paper 13 / arXiv:2607.19951 Open arXiv

Relevance 4 Novelty 5

Why selected: Partial match to criterion 3: it introduces a new benchmark for UAV-to-satellite geo-localization under challenging viewpoints, but it is not an embodied AI simulator benchmark.

Cross-view geo-localization between UAV and satellite imagery remains a fundamental yet highly challenging task, especially under large off-nadir views where drastic perspective distortions, occlusions, and appearance gaps occur. Existing benchmarks and methods primarily focus on near-nadir scenarios and often overlook the importance of structural scene understanding and intra-domain relational constraints, limiting their performance in real-world deployments. In this work, we introduce OffNadirLoc, a new benchmark for large off-nadir UAV-to-satellite geo-localization. To tackle the unique challenges posed by off-nadir perspectives, we further propose ONLoc, a framework that incorporates a structure-aware contextual weighting mechanism to dynamically emphasize reliable local features while suppressing ambiguous or repetitive regions. Additionally, we design a view-coherent learning strategy, which treats one satellite image and the corresponding UAV images from multiple views as a cohesive semantic group. This set-level supervision enables the model to learn viewpoint-invariant and discriminative features, making it more effective at capturing multi-view consistency than conventional pairwise contrastive learning. Extensive experiments on the OffNadirLoc benchmark and four near-nadir datasets demonstrate that our method consistently outperforms state-of-the-art approaches while exhibiting strong zero-shot generalization to unseen datasets without additional training. The code will be released at https://montalario.github.io/offnadirloc/.

Vision-Language Reasoning

2 RS-RIE-Bench: Benchmarking Reasoning-Guided Remote Sensing Image Editing Zihan Qin, Boao Xu, Zhao Dong, Yingping Sun, Ziheng Jiao, Junying Wang, Hongwei Wang

Vision-Language Reasoning Multimodal LLM Benchmark & Evaluation Generative Modeling

ArXiv

Paper 2 / arXiv:2607.20197 Open arXiv

Relevance 5 Novelty 6

Why selected: Matches criterion 3 very closely: a new benchmark for reasoning-guided remote sensing image editing, with explicit spatial reasoning evaluation.

Remote sensing image editing aims to modify remote sensing images according to natural language instructions while preserving geographic rules and sensor observation characteristics. Existing benchmarks mainly target natural images or general visual scenes, and thus may not fully capture the reasoning, regional control, and sensor-consistency abilities required in remote sensing editing. To fill this gap, we introduce RS-RIE-Bench, the first benchmark for reasoning-guided remote sensing image editing. RS-RIE-Bench organizes tasks into three categories: temporal reasoning, causal reasoning, and spatial reasoning. These categories capture temporal evolution, causal consequence, and spatial imaging consistency in remote sensing scenes. The evaluation protocol covers three dimensions: target region plausibility, non-target region preservation, and image quality consistency. We further demonstrate the feasibility of MLLM-based evaluation through cross-judge consistency analysis and stratified expert review. Systematic evaluation on eight open-source and closed-source image editing models shows that current models still have clear limitations in reasoning-guided remote sensing editing. Even the strongest model achieves only 24.28\% overall accuracy under the strict joint-satisfaction criterion, while the mean relaxed joint-4 success rate across all eight models is 32.23\%. Causal reasoning and spatial reasoning remain especially challenging, and several open-source models are close to zero in some categories. These results show that RS-RIE-Bench can effectively reveal the limitations of current models in geographic reasoning, regional control, and sensor-consistent generation. It also provides a standardized benchmark and a clear research direction for future remote sensing intelligent editing models.

3 LAVIFT: Latent-Action-Guided Vision Fine-Tuning for Surgical Interaction Recognition Jiajun Cheng, Subarna Tripathi, Sainan Liu, Xiaofan Yu, Shan Lin

Vision-Language Reasoning Benchmark & Evaluation Embodied Interaction LLM Interpretability

ArXiv

Paper 3 / arXiv:2607.19889 Open arXiv

Relevance 5 Novelty 6

Why selected: Strong match to criterion 4: it adapts pretrained vision-language / vision foundation models to a surgical interaction recognition task with a grounding-oriented fine-tuning method.

Understanding instrument-tissue interactions is essential for context-aware surgical AI and autonomous robotic surgery. Pretrained vision-language models (VLMs) and vision encoders offer an alternative to conventional interaction classifiers by transferring broad visual and semantic knowledge. However, adapting them to fine-grained surgical interactions remains challenging: (1) freezing the vision encoder depends entirely on pretrained representations that may retain noise and provide weak spatial localization, while (2) full fine-tuning can improve global semantic alignment without ensuring that the encoder learns meaningful features in the correct action region. We address these limitations by introducing LAViFiT, an end-to-end latent-action-guided framework for vision-language fine-tuning. An inverse dynamics model captures the visual changes induced by each action, while a forward world model drives the encoder to represent action-relevant regions. A patch-level SIG Regularizer further prevents local feature collapse without additional supervision, such as bounding boxes or pseudo-labels. Experiments across multiple encoders and datasets improve recognition and image-text alignment, while representation analyses show stronger grounding over the complete instrument-tissue interaction region and more spatially coherent features.

4 Look Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs Pengcheng Wang, Zhiquan Wang, Jayoung Lee, Zhuoyan Xu, Ran Xu, Saurabh Bagchi, Yin Li, Somali Chaterji

Vision-Language Reasoning Multimodal LLM Benchmark & Evaluation LLM Interpretability

ArXiv

Paper 4 / arXiv:2607.20357 Open arXiv

Relevance 5 Novelty 6

Why selected: Strong match to criterion 2: it proposes a new MLLM inference framework with joint vision-token and LLM compute adaptation.

Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance across vision-language tasks. However, their high inference cost, arising from both the large number of input visual tokens and the heavy computation of the large language model (LLM), remains a key barrier to practical deployment. Recent work attempts to reduce the cost by adaptively optimizing individual dimensions, e.g., pruning redundant visual tokens or skipping LLM layers and heads. Nonetheless, prior approaches typically treat these dimensions independently and overlook a fundamental coupling: the available compute resources must be dynamically allocated across all dimensions based on the input content. To bridge the gap, we propose SmartVL, a unified adaptive inference framework that jointly controls vision token number and model compute capability in response to varying input contents and compute budgets. SmartVL introduces a vision-side token controller that dynamically selects informative visual tokens and an LLM-side compute controller that adaptively adjusts LLM computation. Importantly, these controllers are trained to coordinate with each other so that the overall inference cost satisfies a target budget. To allow this joint scheduling, we connect the controllers using a shared budget encoding and leverage a differentiable latency estimator for end-to-end training. This design enables SmartVL to learn cross-stage allocation strategies that adapt to both input complexity and runtime compute constraints. Experiments across multiple MLLM benchmarks demonstrate that, with joint scheduling, SmartVL consistently outperforms prior adaptive methods and achieves superior accuracy-efficiency Pareto frontiers. Project page: https://www.schaterji.io/publications/2026/jointtokencompute.

5 Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training Qiwei Ma, Bin Deng, Junjie Zhu, Qiangjuan Huang, Puhong Duan, Ke Yang, Xudong Kang, Shutao Li

Vision-Language Reasoning Benchmark & Evaluation Safety & Backdoors Retrieval & Grounding

ArXiv

Paper 5 / arXiv:2607.20238 Open arXiv

Relevance 5 Novelty 5

Why selected: Matches criterion 4 very closely: a VIS-IR pre-training method for multi-sensor representation learning, with a nontrivial sampling/curriculum trick for foundation-style pretraining.

Visible-infrared (VIS-IR) alignment is a key pre-training task for robust multi-sensor perception. Most existing methods use uniform patch-wise contrastive learning, but this can be unreliable in VIS-IR data because imaging-physics differences make some spatially paired regions inherently less comparable, and aligning them with equal strength hinders representation learning and downstream transfer. In this paper, we revisit VIS-IR pre-training from a sampling perspective and propose Importance-Aware Sampling (IAS), which adjusts training emphasis based on patch reliability. Specifically, IAS (i) derives patch weights from infrared structural cues and uses them to reweight the contrastive objective; (ii) learns a soft importance mask with a lightweight sampler, optionally warm-started from the hand-crafted prior; and (iii) employs a patch curriculum learning strategy that gradually expands from high-reliability regions to harder patches. It is worth noting that IAS is plug-and-play and works with both patch-/correlation-level alignment (e.g., UNIV-style) and image-level contrastive baselines (e.g., ImageBind-style). Extensive experiments on multiple VIS-IR benchmarks demonstrate consistent improvements over strong baselines, including for IR semantic segmentation, IR object detection and VIS semantic segmentation and cross-modal retrieval task. Code will be released on https://github.com/KlayMa527/IAS.

9 Multimodal Large Language Models for Remote Sensing Image Understanding: Domain-Specific or General-Purpose? Qiwei Ma, Chunping Qiu, Xinjun Cheng, Xiaoyu Zhang, Puhong Duan, Ke Yang, Xudong Kang, Shutao Li

Vision-Language Reasoning Multimodal LLM Benchmark & Evaluation Embodied Interaction

ArXiv

Paper 9 / arXiv:2607.20284 Open arXiv

Relevance 5 Novelty 4

Why selected: Matches criterion 2 very closely: a systematic survey/diagnostic of MLLMs for remote sensing image understanding, including general-purpose versus domain-specific model comparison.

The rapid development of multimodal large language models (MLLMs) has introduced a flexible paradigm for remote sensing image scene understanding (RSISU), enabling natural-language interaction with remote sensing imagery. However, a systematic understanding of the capability boundaries, cross-task generalization, and task-specific limitations of existing remote sensing MLLMs (RS-MLLMs) is still lacking. This paper presents a systematic survey and diagnostic evaluation of MLLMs for RSISU. We review the technical evolution of RS-MLLMs, focusing on model design, multimodal learning, training data, and downstream capabilities. We further compare RS-MLLMs with general-purpose computer vision MLLMs (CV-MLLMs) across diverse RSISU tasks and benchmarks. RS-MLLMs remain competitive in domain-specific settings, particularly remote sensing visual grounding and high-resolution visual question answering. More notably, general-purpose CV-MLLMs can match or even outperform these specialized models on several RSISU tasks without remote sensing-specific fine-tuning. These findings demonstrate the strong transferability of general-purpose CV-MLLMs and show that current RS-MLLMs do not consistently outperform them across diverse RSISU tasks. Current MLLMs also face limitations in spatial and relational reasoning, fine-grained visual understanding, instruction diversity, and generalization across heterogeneous task formats. Based on these findings, we outline future directions toward reliable evaluation, multimodal and high-resolution reasoning, efficient deployment, and tool-augmented remote sensing agents. This survey provides a systematic reference for developing robust, generalizable, and practical MLLMs for RSISU.

10 HeadCast: Casting Attention Heads for Efficient Autoregressive Video Generation Jinliang Shen, Lianghao Su, Zheming Li, Kang He, ZiLiang Lai, Yanbing Jiang, Chengru Song

Vision-Language Reasoning Generative Modeling LLM Interpretability Time-Series LLM

ArXiv

Paper 10 / arXiv:2607.20125 Open arXiv

Relevance 3 Novelty 6

Why selected: No close match to criteria 1-4; this is efficient autoregressive video generation, relevant to generative modeling but not specifically to the listed spatial/embodied/MLLM/benchmark themes.

Autoregressive (AR) video diffusion models have become a promising paradigm for long and streaming video synthesis, but the continuously growing Key-Value (KV) cache makes attention the dominant inference cost, especially at high resolution where each frame contributes many tokens. Existing remedies either evict the cache with coarse heuristics that cause inter-frame flickering, or require model re-training. We propose HeadCast, a training-free, plug-and-play acceleration framework built on the observation that a pre-trained AR model's attention heads exhibit stable, heterogeneous behaviors. After a short warm-up, HeadCast performs a one-time classification at the maximum-noise step that sorts every head into one of four archetypes: Sink, Dummy, Spatial, and Global, and restructures the monolithic KV cache into head-specific pathways. Crucially, it retains the Global heads that preserve the long-range temporal consistency aggressive eviction destroys. Because the Spatial pathway operates on a fixed-size grid, its savings grow with resolution: across state-of-the-art AR models, HeadCast accelerates inference by up to 1.62x at 720P and 1.95x at 1080P, while keeping VBench quality on par with full attention and largely flicker-free. Code is available at https://github.com/sjlgaga/HeadCast .

11 WearWow: Native 2K Multi-Garment Virtual Try-On via Adaptive Token Packing and Preference Alignment Xujie Zhang, Runyan Du, Song Chang, Jiang Li, Dongliang Shao, Liping Wu, Wei Luo, Xiaochao Qu, Luoqi Liu, Xiaodan Liang

Vision-Language Reasoning Benchmark & Evaluation Generative Modeling LLM Interpretability

ArXiv

Paper 11 / arXiv:2607.19923 Open arXiv

Relevance 3 Novelty 6

Why selected: No close match to criteria 1-4; this is a multimodal generative modeling paper for virtual try-on, which may still interest your friend but is not specifically about the listed criteria.

Synthesizing native 2K multi-garment virtual try-on is a formidable frontier in digital fashion, critically bottlenecked by two fundamental limitations: the O(N^2) memory explosion induced by 2k conditions, and the spectral bias of diffusion models that over-smooths high-frequency fabric details. We present WearWow, an end-to-end, mask-free generative framework that pioneers ultra-high-resolution multi-garment synthesis. To mitigate the memory explosion , we propose Adaptive 2D Token Packing (ATP). ATP leverages inherent garment sparsity to algorithmically pack heterogeneous items onto a unified 2D canvas and prune uninformative background tokens, minimizing the effective sequence length and subsequent memory overhead while rigorously preserving 2D spatial priors. To rectify texture degradation, we introduce the Multi-dimensional Try-on Reward (MTR) system. MTR synergizes a Semantic Guidance Reward to explicitly drive tactile restoration with a Cloth Distribution Reward to implicitly anchor the physical distribution, a joint formulation that effectively mitigates the severe reward hacking. Furthermore, we curate WearWow-2K, an extreme-quality dataset comprising native 2K triplets, providing physically correct spatial interactions that naturally empower the model's mask-free generation. Extensive experiments demonstrate that WearWow establishes a new state-of-the-art, exceeding existing commercial baselines in native 2K multi-garment synthesis.

14 Importance-Aware OBS Pruning for Diffusion Models Ba-Thinh Lam, Srijan Das, Hieu Le

Vision-Language Reasoning Benchmark & Evaluation Generative Modeling

ArXiv

Paper 14 / arXiv:2607.20048 Open arXiv

Relevance 3 Novelty 5

Why selected: No close match to criteria 1-4; this is a training-free compression/pruning method for diffusion models, adjacent to criterion 4 but not a foundation-model application paper.

We propose importance-aware pruning for diffusion models, a training-free framework that prioritizes preserving parameters critical to semantically salient image regions. To do so, we incorporate spatial importance maps -- derived from conditioning signals or model attention -- into the pruning objective. This produces parameter rankings aligned with perceptual relevance rather than uniform reconstruction error. On MS-COCO dataset, our proposed approach consistently retains subject fidelity and structural correctness at high compression ratios where conventional pruning causes visible degradation. These results demonstrate that content-aware objectives are key to perceptually faithful compression of generative models.

Multimodal LLM

6 Diverse-Intent Multi-Turn Fashion Image Retrieval Mingqiang Tang, Haokun Wen, Meng Liu, Yupeng Hu, Weili Guan, Xuemeng Song

Multimodal LLM Benchmark & Evaluation LLM Interpretability Retrieval & Grounding

ArXiv

Paper 6 / arXiv:2607.20291 Open arXiv

Relevance 5 Novelty 5

Why selected: Strong match to criterion 2: it proposes an MLLM-based framework and a new benchmark for diverse-intent multi-turn fashion image retrieval.

Real-world fashion search involves interactive retrieval across multiple turns. However, existing multi-turn retrieval methods are built on a restrictive assumption that every interaction follows the same attribute-editing paradigm, leaving heterogeneous intent transitions unexplored. Moreover, existing approaches often rely on textification to bridge multimodal queries and visual retrieval, which may lose fine-grained visual cues. To address these gaps, we introduce DIM-Fashion, a benchmark of 26K multi-turn sessions constructed from 13 fashion retrieval datasets across 7 tasks, featuring diverse intent transitions and rollback behaviors. We further propose FashionAM, an MLLM-VLP framework that directly aligns multimodal conversational queries with a fashion-oriented gallery embedding space, avoiding intermediate textification. Extensive experiments demonstrate the effectiveness of FashionAM over existing approaches. The dataset and code will be made publicly available upon acceptance.

7 MV-Bench: Benchmarking Multimodal Large Language Models for Coordinated Multi-View Interface Construction Yue Zhao, Hongxu Liu, Feiyu Wang, Xiaoyu Yang, Tong Ge, Zhen Yang, Chao Wang, Qiong Zeng

Multimodal LLM Benchmark & Evaluation Generative Modeling LLM Interpretability

ArXiv

Paper 7 / arXiv:2607.19910 Open arXiv

Relevance 5 Novelty 5

Why selected: Strong match to criterion 2: it introduces a benchmark for evaluating MLLMs on coordinated multi-view interface construction.

Multimodal large language models (MLLMs) are increasingly expected to automate visualization development by generating code directly from visual designs. However, existing evaluations mainly focus on single-chart generation and overlook coordinated multi-view interface construction, which requires joint reasoning about data semantics, view coordination, and interaction logic. Consequently, MLLM capabilities in this setting remain underexplored, and the field lacks a dedicated benchmark for systematic assessment. We introduce MV-Bench, a benchmark for evaluating MLLMs on coordinated multi-view interface construction. Instead of relying on incomplete or inconsistent open-source implementations, we use Tableau workbook files as ground truth because they explicitly encode data bindings, visual mappings, and interactions. We develop a multi-stage pipeline that converts these specifications into executable web interfaces through structured intermediate representations. The benchmark contains 92 base interfaces and 1,048 verified instances created by recombining chart types, datasets, and interaction patterns. Each instance includes executable code, a rendered interface, a dataset, and interaction annotations. We evaluate five state-of-the-art MLLMs in a single-pass setting using metrics for visual fidelity, data binding correctness, and interaction completeness. The strongest model achieves 75.45 percent accuracy in visual layout reproduction, but only 21.71 percent in data binding and 11.68 percent in interaction completeness. These results show that current MLLMs can reproduce visual appearance but remain limited in generating the data semantics and interactive logic required by coordinated multi-view interfaces. Iterative refinement improves code executability but does not substantially reduce the gap in data binding and interaction generation.

8 STEREOFLOW: Progressive Stereo Matching with StereoDiT and Transition Flow Matching Hao Wang, Haoran Geng, Xiaotong Yang, Jing Tang, Songlin Wei, Linlong Lang, Yeying Jin, Zheng Zhu, Zhaoxin Fan, Biao Leng

Multimodal LLM Benchmark & Evaluation Generative Modeling LLM Interpretability

ArXiv

Paper 8 / arXiv:2607.19986 Open arXiv

Relevance 3 Novelty 6

Why selected: Weakly related to criterion 4 via generative modeling in vision, but it is stereo matching rather than a vision foundation model or embodied AI method.

Stereo matching is a fundamental task in 3D reconstruction. Despite remarkable advances, the prevailing paradigms formulate stereo matching as a deterministic regression problem, collapsing the multimodal distribution modeling into a single-point estimation. This formulation suffers from a regression-to-mean bias, frequently struggling with ambiguous regions. In contrast, we introduce a prior-guided generative framework that integrates deterministic matching regression and generative distribution modeling within a complementary formulation. Built upon this formulation, we introduce StereoFlow through three key components: (i) a two-stage progressive cascade matching network that progressively produces multi-resolution stereo conditions with complementary matching cues; (ii) a pixel diffusion transformer (termed StereoDiT) with a frequency-decoupled architecture for modeling correspondence ambiguity; (iii) a few-step flow matching objective (termed Transition Flow Matching) for efficient optimization. In summary, \textsc{\textbf{StereoFlow}} achieves strong geometric consistency and rich fine-grained details in ill-posed, discontinuous regions and under zero-shot generalization. Extensive experiments demonstrate that the proposed StereoFlow establishes multiple state-of-the-art results across benchmarks, including Scene Flow, KITTI, ETH3D, and Middlebury.

Safety & Backdoors

12 Efficient Tracking and Understanding Object Transformations Yihong Sun, Bharath Hariharan

Safety & Backdoors LLM Interpretability Time-Series LLM

ArXiv

Paper 12 / arXiv:2607.19743 Open arXiv

Relevance 3 Novelty 6

Why selected: No close match to criteria 1-4, though it does use a vision foundation model component (SAM2) for video object transformation tracking as an application.

Tracking objects through state transformations is essential for understanding real-world dynamics. However, existing methods are computationally expensive. TubeletGraph recently showed impressive capabilities, but its inference cost (~$4.4$ seconds per object-frame on VOST) precludes any real-time deployment possibilities. We observe that TubeletGraph's overhead arises from building a spatiotemporal partition of the input video: (1) entity segmentation is computed densely for every frame regardless of whether a transformation occurs, and (2) every entity in the scene is tracked, scaling cost with scene complexity rather than the number of transformations of interest. To address both, we propose FluxGraph, a reactive variant that uses SAM2's internal multi-mask disagreement as a lightweight trigger for transformation detection, and removes the need for tracking all entities in the given video. FluxGraph is ~$3.3\times$ faster than TubeletGraph on VOST while improving tracking performance and preserving state graph quality. Furthermore, we also observe consistent speedups of $3.7-10.7\times$ across VSCOS, M$^3$-VOS, and DAVIS17 while maintaining performance. Code is publicly available at https://github.com/YihongSun/FluxGraph.

General ML

15 Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout Xuchen Zhu, Yajuan Wei, Shuang Hao, Jiwei Jiang, Guanxiang Mao, Fang Ren

General ML

ArXiv

Paper 15 / arXiv:2607.20326 Open arXiv

Relevance 3 Novelty 4

Why selected: No close match to criteria 1-4; this is RGB-D semantic segmentation robustness under missing modalities, which is adjacent but not centered on the requested themes.

RGB-D semantic segmentation has achieved remarkable progress, yet most models assume that RGB and depth are always available. In practice, failures or occlusions of surveillance sensors often remove one modality. Although RGB or depth alone can contain sufficient cues, models trained only on full-modality inputs fail to exploit the remaining modality once one is missing, causing severe degradation. We tackle this issue with a simple continued-training paradigm, \emph{Condition Dropout (ConD)}, which mitigates degradation while preserving full-modality accuracy. Starting from a pretrained RGB-D model, ConD adds a second stage that randomly simulates complete, RGB-missing, and depth-missing inputs, freezes the original encoders, and trains copied encoders with zero-initialized feature injection. Experiments on NYU-Depth V2 and SUN RGB-D show that ConD improves robustness under missing modalities and even yields slight gains when modalities are complete. Our code will be made publicly available upon acceptance.

Archived papers 15

Top score 11

Average score 9.5