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- Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework436
This paper proposes a purely point-based framework for joint crowd counting and individual localization, called Point to Point Network (P2PNet), and proposes a new metric, called density Normalized Average Precision (nAP), to provide more comprehensive and more precise performance evaluation.
- Dynamic Fusion With Intra- and Inter-Modality Attention Flow for Visual Question Answering426
A novel method of dynamically fuse multi-modal features with intra- and inter-modality information flow, which alternatively pass dynamic information between and across the visual and language modalities is proposed, which can robustly capture the high-level interactions between language and vision domains.
- Personalize Segment Anything Model with One Shot400
A training-free Personalization approach for SAM, termed as PerSAM, which effectively adapt SAM for private use without any training, and can enhance DreamBooth to personalize Stable Diffusion for text-to-image generation, which discards the background disturbance for better target appearance learning.
- Rethinking Mobile Block for Efficient Attention-based Models312
This work rethinks lightweight infrastructure from efficient IRB and effective components of Transformer from a unified perspective, extending CNN-based IRB to attention-based models and abstracting a one-residual Meta Mobile Block (MMB) for lightweight model design.
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- Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model177
This paper presents Instruct2Act, a framework that utilizes Large Language Models to map multi-modal instructions to sequential actions for robotic manipulation tasks, employing the LLM model to generate Python programs that constitute a comprehensive perception, planning, and action loop for robotic tasks.
- SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking164
This work proposes a novel symmetric multimodal tracking framework called SDSTrack, which introduces lightweight adaptation for efficient fine-tuning, which directly transfers the feature extraction ability from RGB to other domains with a small number of trainable parameters and integrates multimodal features in a balanced, symmetric manner.
- Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers147
This technical report introduces the Lumina-T2X family - a series of Flow-based Large Diffusion Transformers (Flag-DiT) equipped with zero-initialized attention, as a unified framework designed to transform noise into images, videos, multi-view 3D objects, and audio clips conditioned on text instructions.
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- DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation99
The domain-invariant representation is constructed which suppresses the effect of the domain-specific style on the quality and correlation of the features and can enhance the robustness of various backbone networks with little computational cost.
- You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction79
This work proposes a lightweight fast Illumination Adaptive Transformer (IAT) which comprises two transformer-style branches: local estimation branch and global ISP branch which could also conduct both object detection and semantic segmentation under various light conditions.
- Learning Where to Focus for Efficient Video Object Detection70
A novel module called Learnable Spatio-Temporal Sampling (LSTS) has been proposed to learn semantic-level correspondences among adjacent frame features accurately and achieves state-of-the-art performance on the ImageNet VID dataset with less computational complexity and real-time speed.
- ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models57
ManipVQA is proposed, a novel framework that infuses MLLMs with manipulation-centric knowledge through a Visual Question-Answering (VQA) format and preserves the original vision-reasoning abilities while incorporating the newly acquired robotic insights.
- Video Object Detection with Locally-Weighted Deformable Neighbors56
This paper proposes LWDN (Locally-Weighted Deformable Neighbors) for video object detection without utilizing time-consuming optical flow extraction networks and adopts brain-inspired memory mechanism to propagate and update the memory feature from keyframes to keyframes, called Memory-Guided Propagation.
- SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking42
A novel siamese tracking algorithm called SiamRCR is proposed, addressing this problem with a simple, light and effective solution, and builds reciprocal links between classification and regression branches, which can dynamically re-weight their losses for each positive sample.
- Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement39
RAG is a Regional-Aware text-to-image Generation method conditioned on regional descriptions for precise layout composition that achieves superior performance over attribute binding and object relationship than previous tuning-free methods.
- OSV: One Step is Enough for High-Quality Image to Video Generation37
This work introduces a two-stage training framework that effectively combines consistency distillation with adversarial training to address video diffusion challenges, and proposes a novel video discriminator design, which eliminates the need for decoding the video latents and improves the final performance.
- VividPose: Advancing Stable Video Diffusion for Realistic Human Image Animation34
This work proposes VividPose, an innovative end-to-end pipeline based on Stable Video Diffusion (SVD) that ensures superior temporal stability and enhances the retention of human identity, and introduces a geometry-aware pose controller that utilizes both dense rendering maps from SMPL-X and sparse skeleton maps.
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- MambaGesture: Enhancing Co-Speech Gesture Generation with Mamba and Disentangled Multi-Modality Fusion19
This work presents MambaGesture, a novel framework integrating a Mamba-based attention block, MambaAttn, with a multi-modality feature fusion module, SEAD, achieving state-of-the-art performance in co-speech gesture generation.
- STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation19
This work presents a simple and efficient single-stage VIS framework based on the instance segmentation method CondInst by adding an extra tracking head and proposes a novel bi-directional spatio-temporal contrastive learning strategy for tracking embedding across frames.
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- Unify-Agent: A Unified Multimodal Agent for World-Grounded Image Synthesis16
This work presents Unify-Agent, a unified multimodal agent for world-grounded image synthesis, which reframes image generation as an agentic pipeline consisting of prompt understanding, multimodal evidence searching, grounded recaptioning, and final synthesis.
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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 2026-10-14. 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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