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- Point Transformer V3: Simpler, Faster, Stronger1,171
This paper presents Point Transformer V3 (PTv3), which prioritizes simplicity and efficiency over the accuracy of certain mechanisms that are minor to the over-all performance after scaling, such as replacing the precise neighbor search by KNN with an efficient serialized neighbor mapping of point clouds organized with specific patterns.
- HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis566
A new attentionbased deep neural network, named as HydraPlus-Net (HPnet), that multi-directionally feeds the multi-level attention maps to different feature layers to enrich the final feature representations for a pedestrian image.
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- CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval361
This paper proposes Cross-modal Adaptive Message Passing (CAMP), which adaptively controls the information flow for message passing across modalities and properly handles negative pairs and irrelevant information with an adaptive gating scheme.
- More Control for Free! Image Synthesis with Semantic Diffusion Guidance354
This work investigates fine-grained, continuous control of the denoising diffusion probabilistic model class, and introduces a novel unified framework for semantic diffusion guidance, which allows either language or image guidance, or both.
- DDP: Diffusion Model for Dense Visual Prediction254
The method, called DDP, efficiently extends the denoising diffusion process into the modern perception pipeline and shows attractive properties such as dynamic inference and uncertainty awareness, in contrast to previous single-step discriminative methods.
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- Improving Referring Expression Grounding With Cross-Modal Attention-Guided Erasing225
A novel cross-modal attention-guided erasing approach is designed, where the most dominant information from either textual or visual domains is discarded to generate difficult training samples online, and to drive the model to discover complementary textual-visual correspondences.
- EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI206
This work introduces EmbodiedScan, a multi-modal, ego-centric 3D perception dataset and benchmark for holistic 3D scene understanding, and a baseline framework named Embodied Perceptron, capable of processing an arbitrary number of multi-modal inputs and demonstrating remarkable 3D perception capabilities.
- Bridging Video-text Retrieval with Multiple Choice Questions131
This work enables fine-grained video-text interactions while maintaining high efficiency for retrieval via a novel pretext task, dubbed as Multiple Choice Questions (MCQ), where a parametric module BridgeFormer is trained to answer the “questions” constructed by the text features via resorting to the video features.
- Towards Large-Scale 3D Representation Learning with Multi-Dataset Point Prompt Training117
PPT is introduced, a novel framework for multi-dataset synergistic learning in the context of 3D representation learning that supports multiple pre-training paradigms and achieves state-of-the-art performance on each dataset using a single weight-shared model with supervised multi-dataset training.
- Back to the Source: Diffusion-Driven Adaptation to Test-Time Corruption111
This diffusion-driven adaptation (DDA) method shares its models for classification and generation across all domains, training both on source then freezing them for all targets, to avoid expensive domain-wise retraining.
- Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning82
An efficient and effective contrastive learning framework, which generates contrastive views directly on scene-level point clouds by a well-curated data augmentation pipeline and a practical view mixing strategy, and an exquisite design of contrastive cross masks, which targets the reconstruction of point color and surfel normal.
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- A Survey of Reasoning with Foundation Models73
This paper introduces seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks and discusses the relevance of multimodal learning, autonomous agents, and super alignment in the context of reasoning.
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- OV-PARTS: Towards Open-Vocabulary Part Segmentation49
An Open-Vocabulary Part Segmentation (OV-PARTS) benchmark, providing insights into analogical reasoning, open granularity and few-shot adapting abilities of models, and two prevailing paradigms of existing object-level OVSS methods for OV- PARTS are analyzed and adapted.
- EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning in Real-World Scenarios46
It is found that MLLMs’ planning performance on EgoPlan-Bench2 is critically dependent on temporally structured action sequences in historical task progress and interactions between objects and humans in current observation state, which underscores the necessity for strong reasoning abilities to integrate diverse multimodal cues and analysis before making final decision.
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- RIFormer: Keep Your Vision Backbone Effective But Removing Token Mixer33
This paper develops an RepIdentityFormer base on the re-parameterizing idea, and explores the improved learning paradigm to break the limitation of simple token mixer free backbone, and summarizes the empirical practice into 5 guidelines.
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- Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detection32
Object2Scene is proposed, the first approach that leverages large-scale large-vocabulary 3D object datasets to augment existing 3D scene datasets for open- Vocabulary 3d object detection and a cross-domain category-level contrastive learning approach to mitigate the domain gap between 3D objects from different datasets.
- A Survey of Interactive Generative Video27
This paper defines IGV as a technology that combines generative capabilities to produce diverse high-quality video content with interactive features that enable user engagement through control signals and responsive feedback, and proposes a comprehensive framework that decomposes an ideal IGV system into five essential modules.
- Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R127
SEED-Bench-R1 is introduced, a benchmark designed to systematically evaluate post-training methods for MLLMs in video understanding and identifies key limitations such as inconsistent reasoning and overlooked visual cues, and suggest future improvements in base model reasoning, reward modeling, and RL robustness against noisy signals.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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