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Open problems in Multimodal

Lack of holistic video understanding benchmarks1 paper raises it · 1 addresses it
Existing benchmarks cover only partial evaluation dimensions, limiting systematic diagnosis of omni-modal video understanding.
- OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understandingraises · addresses
Long-horizon task execution and recovery1 paper raises it · 0 address it
Agents struggle with preserving progress, recovering from failures, and verifying physical states over long sequences.
- Has GPT-6-Astra Achieved Embodied Intelligence?raises
Efficient spatial reasoning for real-time interaction2 papers raise it · 2 address it
Current models use slow autoregressive decoding for compact output spaces, limiting interaction speed.
- VideoJev: System One Model for Video Understanding and Interactionraises · addresses
- MJev-Spatial: System One Spatial Intelligence via Native Attention Matchingraises · addresses
Cross-shot consistency in agentic video generation1 paper raises it · 1 addresses it
Maintaining spatial and temporal consistency across non-consecutive video shots is difficult.
- Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generationraises · addresses
Precise grounding for physical intelligence1 paper raises it · 1 addresses it
General-purpose backbones fail to precisely ground objects in clutter or for tiny targets at control speeds.
- GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitivesraises · addresses

Named by an AI from these papers' abstracts.

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Topics in this field

video understanding · video language · video generation301 papers49 this week
visual question answering · vision language models · vision language165 papers46 this week
autoencoders · imagenet · autoencoder224 papers40 this week
semantic segmentation · vision language · attention202 papers31 this week
video generation · efficient video · autoregressive video181 papers31 this week
visual reasoning · multimodal reasoning · visual question answering177 papers30 this week

Topics in this field

Active contributors · 90 days

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arXiv cs.CVOct 16
DICL-Qwen3VL: Decoupled Confidence Calibration for Interpretable Workwear Detection
Meng Wang, Wu Zeng, Mei Li, Chao He
Poor illumination and occlusion obscure clothing cues in indoor work sites, while diverse clothing styles complicate workwear compliance judgments. Although vision-language models (VLMs) support localization and semantic reasoning, their predictions often lack explicit supporting evidence, and a single confidence score cannot distinguish visual uncertainty from ambiguity in compliance judgments. We propose DICL-Qwen3VL, a decoupled interpretable VLM that integrates structured workwear detection, evidence generation, and separate visual and reasoning confidence estimates. First, low-rank adaptation (LoRA) adapts Qwen3-VL to localize visible upper-body clothing and identify workwear. Second, decoupled calibration separates visual observations from compliance reasoning and assigns a confidence score to each. Token entropy and sensitivity to image perturbations provide supervision for visual confidence, linking confidence estimation to the generated observations and their dependence on image content. Finally, group relative policy optimization (GRPO) jointly optimizes detection accuracy, output validity, and confidence calibration. Experiments on the company-collected Indoor…
PDF arXiv
arXiv cs.CVOct 16
ODSS-Net: Orthogonally Decoupled Shared-Specific Representations for Multi-Modal Object Re-Identification
Zhifeng Ma, Yuhong Feng, Chongyi Huang, Mingyang Zhou, Minwen Deng
Multi-modal object re-identification (ReID) leverages complementary information from heterogeneous sensors. Cross-modal alignment and fusion remain challenging. Existing methods mainly reduce modality gaps via feature alignment and fusion. However, alignment may entangle modality-shared structural information with modality-specific cues, while conventional fusion may not fully exploit complementary information. To address these issues, we propose ODSS-Net, a framework for orthogonally decoupled shared-specific representation learning and adaptive fusion of complementary modality-specific features. First, a Shared Structural Feature Construction (SSFC) module suppresses modality-dependent appearance variations and extracts modality-shared structural information. Then, an Orthogonal Specific Feature Learning (OSFL) module aggregates identity-relevant local information and removes the shared-aligned component via geometric orthogonal projection. Finally, a Sample Adaptive Mixture of Experts (SAMoE) module uses the shared structure as routing context to adaptively refine and fuse modality-specific features. Experiments on three benchmarks demonstrate the effectiveness of ODSS-Net for…
PDF arXiv
arXiv cs.CVOct 16
MaskMerge: Structure-aware Token Merging for Efficient Instance Segmentation
Bavesh Balaji, Letian Jiang, Alexander Wong, David Clausi, Sirisha Rambhatla
Token merging improves the efficiency of Vision Transformers by combining redundant tokens, but existing methods largely rely on feature similarity to determine which tokens to merge. This criterion is effective for semantic segmentation, yet can fail in instance segmentation, where adjacent objects of the same class often have highly similar features. We identify this failure mode as cross-instance token fusion. We introduce MaskMerge, an instance-aware token merging method that uses coarse intermediate mask predictions to constrain feature-based merging and preserve instance boundaries. We evaluate MaskMerge on two diverse instance segmentation domains characterized by high inter-instance visual similarity: PanNuke and iSAID. Against the SOTA token merging method ALGM, MaskMerge improves mAP from 30.1 to 32.1 on PanNuke while reducing computation from 229 to 153 GFLOPs and running 1.18x faster, and from 33.1 to 36.2 on iSAID while reducing computation from 198 to 182 GFLOPs and running 1.23x faster. Additionally, we also test it on Cityscapes and match ALGM's performance while using fewer FLOPs. The gains also transfer to an MAE pretrained backbone, demonstrating that…
PDF arXiv
arXiv cs.CVOct 16
HAPACT: A Benchmark For Human-Centric Physical Impact Localization in Movies
Youngrae Kim, Yejin Jang, Eunho Kim, Youjin Sung, Kun-Woo Song +2
Physical reasoning about humans in videos has largely focused on everyday interactions, where contact, pressure, pose, and motion support embodied perception and contact-aware applications. Still, short and forceful properties like impact events remain relatively underexplored, despite their relevance to broader understanding of how abrupt physical events affect the human body. To fill this gap, we introduce human-centric impact localization, a new task that localizes target physical impacts temporally within a video and spatially on the human body. We further present HAPACT, a HumAn-centric imPACT dataset and benchmark built from movie clips, containing over 2.8K video-query pairs with more than 20K fine-grained human-annotated frames. We built HAPACT around viewer-grounded annotation, text-conditioned localization, and supervision at frame-level temporal and SMPL-X-based spatial granularity. To demonstrate the effectiveness of our benchmark, we conduct extensive evaluations of existing methods, employing video moment retrieval models for temporal localization and human-scene contact models for spatial localization. For the spatial task, we propose a text-conditioned baseline by…
PDF arXiv
arXiv cs.ROOct 16
Sample-Efficient Reinforcement Learning for Policy Improvement via Flow-Reversed Action Anchors
Carl Qi, Weijun Dong, Xiaojie Wang, Peiyi Chen, Rahul Sajnani +6
Generalist robot policies offer strong behavioral priors, but achieving deployment-level reliability and throughput remains difficult. Reinforcement learning (RL) can improve these policies, yet its effectiveness is constrained by the cost of online interaction. Existing methods adapt frozen flow-matching policies by treating noise space as a continuous action space. We show that this behaviorally unstructured space makes exploration inefficient, often preventing these methods from extracting meaningful learning signals on challenging tasks. We introduce AnchorQ, which enables coarse-to-fine policy improvement through a structured, refinable latent action representation. At each decision step, AnchorQ uses flow reversal to map reference behaviors to state-conditioned latent anchors, samples local particles around those anchors, and applies value-guided sampling to explore promising refinements. Our analysis shows that anchors reduce identification complexity at the cost of support bias; particle refinement trades this bias against action-class complexity; and critic accuracy determines the reliability of KL-regularized, value-guided selection. Across simulated and real-world…
PDF arXiv
arXiv cs.CVOct 16
Variational and Decomposed method for Mitigating Bimodal Bias in Robust Visual Question Answering
Yihan Zhao, Yuncheng Jiang
Visual question answering (VQA) models are known to suffer from the bias issue, which refers to overfitting the regularities of frequent patterns. Previously, researchers only consider the language bias. Recently, the multimodal bias is noticed. However, what multimodal bias is remains unclear. In this paper, we investigate the multimodal bias in counting and non-counting questions. We find that they have distinct underlying mechanism, so their biases come from distinct reasons. We provide valuable interpretations of VQA counting mechanism: \textbf{quantity information stems from the magnitude of the multimodal features in specific dimensions.} Thus, counting bias stems from the vision side and propagates to multimodal features through modality fusion, while the non-counting bias stems from primarily language bias and slightly vision bias. Thus, we propose a method to deal with them differently: Variational and Decomposed method for mitigating Bimodal Bias (VDB). It extracts bimodal bias affected by different proportion of vision and language sides. We introduce variational bias models utilizing the principle of Variational Auto-Encoder to learn mainly language bias with slight…
PDF arXiv

Papers

Topics in this field

video understanding · video language · video generation301 papers49 this week
visual question answering · vision language models · vision language165 papers46 this week
autoencoders · imagenet · autoencoder224 papers40 this week
semantic segmentation · vision language · attention202 papers31 this week
video generation · efficient video · autoregressive video181 papers31 this week
visual reasoning · multimodal reasoning · visual question answering177 papers30 this week

Active contributors · 90 days

Recent papers

All in Research →

arXiv cs.CVOct 16
DICL-Qwen3VL: Decoupled Confidence Calibration for Interpretable Workwear Detection
Meng Wang, Wu Zeng, Mei Li, Chao He
Poor illumination and occlusion obscure clothing cues in indoor work sites, while diverse clothing styles complicate workwear compliance judgments. Although vision-language models (VLMs) support localization and semantic reasoning, their predictions often lack explicit supporting evidence, and a single confidence score cannot distinguish visual uncertainty from ambiguity in compliance judgments. We propose DICL-Qwen3VL, a decoupled interpretable VLM that integrates structured workwear detection, evidence generation, and separate visual and reasoning confidence estimates. First, low-rank adaptation (LoRA) adapts Qwen3-VL to localize visible upper-body clothing and identify workwear. Second, decoupled calibration separates visual observations from compliance reasoning and assigns a confidence score to each. Token entropy and sensitivity to image perturbations provide supervision for visual confidence, linking confidence estimation to the generated observations and their dependence on image content. Finally, group relative policy optimization (GRPO) jointly optimizes detection accuracy, output validity, and confidence calibration. Experiments on the company-collected Indoor…
PDF arXiv
arXiv cs.CVOct 16
ODSS-Net: Orthogonally Decoupled Shared-Specific Representations for Multi-Modal Object Re-Identification
Zhifeng Ma, Yuhong Feng, Chongyi Huang, Mingyang Zhou, Minwen Deng
Multi-modal object re-identification (ReID) leverages complementary information from heterogeneous sensors. Cross-modal alignment and fusion remain challenging. Existing methods mainly reduce modality gaps via feature alignment and fusion. However, alignment may entangle modality-shared structural information with modality-specific cues, while conventional fusion may not fully exploit complementary information. To address these issues, we propose ODSS-Net, a framework for orthogonally decoupled shared-specific representation learning and adaptive fusion of complementary modality-specific features. First, a Shared Structural Feature Construction (SSFC) module suppresses modality-dependent appearance variations and extracts modality-shared structural information. Then, an Orthogonal Specific Feature Learning (OSFL) module aggregates identity-relevant local information and removes the shared-aligned component via geometric orthogonal projection. Finally, a Sample Adaptive Mixture of Experts (SAMoE) module uses the shared structure as routing context to adaptively refine and fuse modality-specific features. Experiments on three benchmarks demonstrate the effectiveness of ODSS-Net for…
PDF arXiv
arXiv cs.CVOct 16
MaskMerge: Structure-aware Token Merging for Efficient Instance Segmentation
Bavesh Balaji, Letian Jiang, Alexander Wong, David Clausi, Sirisha Rambhatla
Token merging improves the efficiency of Vision Transformers by combining redundant tokens, but existing methods largely rely on feature similarity to determine which tokens to merge. This criterion is effective for semantic segmentation, yet can fail in instance segmentation, where adjacent objects of the same class often have highly similar features. We identify this failure mode as cross-instance token fusion. We introduce MaskMerge, an instance-aware token merging method that uses coarse intermediate mask predictions to constrain feature-based merging and preserve instance boundaries. We evaluate MaskMerge on two diverse instance segmentation domains characterized by high inter-instance visual similarity: PanNuke and iSAID. Against the SOTA token merging method ALGM, MaskMerge improves mAP from 30.1 to 32.1 on PanNuke while reducing computation from 229 to 153 GFLOPs and running 1.18x faster, and from 33.1 to 36.2 on iSAID while reducing computation from 198 to 182 GFLOPs and running 1.23x faster. Additionally, we also test it on Cityscapes and match ALGM's performance while using fewer FLOPs. The gains also transfer to an MAE pretrained backbone, demonstrating that…
PDF arXiv
arXiv cs.CVOct 16
HAPACT: A Benchmark For Human-Centric Physical Impact Localization in Movies
Youngrae Kim, Yejin Jang, Eunho Kim, Youjin Sung, Kun-Woo Song +2
Physical reasoning about humans in videos has largely focused on everyday interactions, where contact, pressure, pose, and motion support embodied perception and contact-aware applications. Still, short and forceful properties like impact events remain relatively underexplored, despite their relevance to broader understanding of how abrupt physical events affect the human body. To fill this gap, we introduce human-centric impact localization, a new task that localizes target physical impacts temporally within a video and spatially on the human body. We further present HAPACT, a HumAn-centric imPACT dataset and benchmark built from movie clips, containing over 2.8K video-query pairs with more than 20K fine-grained human-annotated frames. We built HAPACT around viewer-grounded annotation, text-conditioned localization, and supervision at frame-level temporal and SMPL-X-based spatial granularity. To demonstrate the effectiveness of our benchmark, we conduct extensive evaluations of existing methods, employing video moment retrieval models for temporal localization and human-scene contact models for spatial localization. For the spatial task, we propose a text-conditioned baseline by…
PDF arXiv
arXiv cs.ROOct 16
Sample-Efficient Reinforcement Learning for Policy Improvement via Flow-Reversed Action Anchors
Carl Qi, Weijun Dong, Xiaojie Wang, Peiyi Chen, Rahul Sajnani +6
Generalist robot policies offer strong behavioral priors, but achieving deployment-level reliability and throughput remains difficult. Reinforcement learning (RL) can improve these policies, yet its effectiveness is constrained by the cost of online interaction. Existing methods adapt frozen flow-matching policies by treating noise space as a continuous action space. We show that this behaviorally unstructured space makes exploration inefficient, often preventing these methods from extracting meaningful learning signals on challenging tasks. We introduce AnchorQ, which enables coarse-to-fine policy improvement through a structured, refinable latent action representation. At each decision step, AnchorQ uses flow reversal to map reference behaviors to state-conditioned latent anchors, samples local particles around those anchors, and applies value-guided sampling to explore promising refinements. Our analysis shows that anchors reduce identification complexity at the cost of support bias; particle refinement trades this bias against action-class complexity; and critic accuracy determines the reliability of KL-regularized, value-guided selection. Across simulated and real-world…
PDF arXiv
arXiv cs.CVOct 16
Variational and Decomposed method for Mitigating Bimodal Bias in Robust Visual Question Answering
Yihan Zhao, Yuncheng Jiang
Visual question answering (VQA) models are known to suffer from the bias issue, which refers to overfitting the regularities of frequent patterns. Previously, researchers only consider the language bias. Recently, the multimodal bias is noticed. However, what multimodal bias is remains unclear. In this paper, we investigate the multimodal bias in counting and non-counting questions. We find that they have distinct underlying mechanism, so their biases come from distinct reasons. We provide valuable interpretations of VQA counting mechanism: \textbf{quantity information stems from the magnitude of the multimodal features in specific dimensions.} Thus, counting bias stems from the vision side and propagates to multimodal features through modality fusion, while the non-counting bias stems from primarily language bias and slightly vision bias. Thus, we propose a method to deal with them differently: Variational and Decomposed method for mitigating Bimodal Bias (VDB). It extracts bimodal bias affected by different proportion of vision and language sides. We introduce variational bias models utilizing the principle of Variational Auto-Encoder to learn mainly language bias with slight…
PDF arXiv

Activity · 12 weeks

Coming with more sources: repos

Fields · AI · updated 1h ago

Multimodal

Models that combine modalities such as vision-language models, video-language and any-to-any models.

519papers this week−29.5% vs last week

All papers · 3.3k With code · 220

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3.1k papers this month

Open problems in Multimodal

Lack of holistic video understanding benchmarks1 paper raises it · 1 addresses it
Existing benchmarks cover only partial evaluation dimensions, limiting systematic diagnosis of omni-modal video understanding.
- OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understandingraises · addresses
Long-horizon task execution and recovery1 paper raises it · 0 address it
Agents struggle with preserving progress, recovering from failures, and verifying physical states over long sequences.
- Has GPT-6-Astra Achieved Embodied Intelligence?raises
Efficient spatial reasoning for real-time interaction2 papers raise it · 2 address it
Current models use slow autoregressive decoding for compact output spaces, limiting interaction speed.
- VideoJev: System One Model for Video Understanding and Interactionraises · addresses
- MJev-Spatial: System One Spatial Intelligence via Native Attention Matchingraises · addresses
Cross-shot consistency in agentic video generation1 paper raises it · 1 addresses it
Maintaining spatial and temporal consistency across non-consecutive video shots is difficult.
- Any Time, Any Space: A World-State-Centric Framework for Agentic Video Generationraises · addresses
Precise grounding for physical intelligence1 paper raises it · 1 addresses it
General-purpose backbones fail to precisely ground objects in clutter or for tiny targets at control speeds.
- GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitivesraises · addresses

Named by an AI from these papers' abstracts.