GenAI4ELab/papercli-papers huggingface dataset:
Flash-VStream: Efficient Real-Time Understanding for Long Video Streams
Authors: Haoji Zhang; Yiqin Wang; Yansong Tang; Yong Liu; Jiashi Feng; Xiaojie Jin
Venue: ICCV 2025 (main)

Abstract: Benefiting from the advances in large language models and cross-modal alignment, existing multimodal large language models have achieved prominent performance in image and short video understanding. However, the understanding of long videos is still challenging, as their long-context nature results in significant computational and memory overhead. Most existing work treats long videos in the same way as short videos, which is inefficient for real-world applications and hard to generalize to even longer videos. To address these issues, we propose Flash-VStream, an efficient video language model capable of processing extremely long videos and responding to user queries in real time. Particularly, we design a Flash Memory module, containing a low-capacity context memory to aggregate long-context temporal information and model the distribution of information density, and a high-capacity augmentation memory to retrieve detailed spatial information based on this distribution. Compared to existing models, Flash-VStream achieves significant reductions in inference latency. Extensive experiments on long video benchmarks and comprehensive video benchmarks, i.e., EgoSchema, MLVU, LVBench, MVBench and Video-MME, demonstrate the state-of-the-art performance and outstanding efficiency of our method. Code is available at https://github.com/IVGSZ/Flash-VStream.
RoboTron-Drive: All-in-One Large Multimodal Model for Autonomous Driving
Authors: Zhijian Huang; Chengjian Feng; Feng Yan; Baihui Xiao; Zequn Jie; Yujie Zhong; Xiaodan Liang; Lin Ma
Venue: ICCV 2025 (main)

Abstract: Large Multimodal Models (LMMs) have demonstrated exceptional comprehension and interpretation capabilities in Autonomous Driving (AD) by incorporating large language models. Despite the advancements, current data-driven AD approaches tend to concentrate on a single dataset and specific tasks, neglecting their overall capabilities and ability to generalize. To bridge these gaps, we propose RoboTron-Drive, a general large multimodal model designed to process diverse data inputs, such as images and multi-view videos, while performing a broad spectrum of AD tasks, including perception, prediction, and planning. Initially, the model undergoes curriculum pre-training to process varied visual signals and perform basic visual comprehension and perception tasks. Subsequently, we augment and standardize various AD datasets to fine-tune the model, resulting in an all-in-one LMM for autonomous driving. To assess the general capabilities and generalization ability, we conduct evaluations on six public benchmarks and undertake zero-shot transfer on three unseen datasets, where RoboTron-Drive achieves state-of-the-art performance across all tasks. We hope RoboTron-Drive as a promising solution for AD in the real world.
Dual-Process Image Generation
Authors: Grace Luo; Jonathan Granskog; Aleksander Holynski; Trevor Darrell
Venue: ICCV 2025 (main)

Abstract: Prior methods for controlling image generation are limited in their ability to be taught new tasks. In contrast, vision-language models, or VLMs, can learn tasks in-context and produce the correct outputs for a given input. We propose a dual-process distillation scheme that allows feed-forward image generators to learn new tasks from deliberative VLMs. Our scheme uses a VLM to rate the generated images and backpropagates this gradient to update the weights of the image generator. Our general framework enables a wide variety of new control tasks through the same text-and-image based interface. We showcase a handful of applications of this technique for different types of control signals, such as commonsense inferences and visual prompts. With our method, users can implement multimodal controls for properties such as color palette, line weight, horizon position, and relative depth within a matter of minutes.
GroundFlow: A Plug-in Module for Temporal Reasoning on 3D Point Cloud Sequential Grounding
Authors: Zijun Lin; Shuting He; Cheston Tan; Bihan Wen
Venue: ICCV 2025 (main)

Abstract: Sequential grounding in 3D point clouds (SG3D) refers to locating sequences of objects by following text instructions for a daily activity with detailed steps. Current 3D visual grounding (3DVG) methods treat text instructions with multiple steps as a whole, without extracting useful temporal information from each step. However, the instructions in SG3D often contain pronouns such as "it", "here" and "the same" to make language expressions concise. This requires grounding methods to understand the context and retrieve relevant information from previous steps to correctly locate object sequences. Due to the lack of an effective module for collecting related historical information, state-of-the-art 3DVG methods face significant challenges in adapting to the SG3D task. To fill this gap, we propose GroundFlow -- a plug-in module for temporal reasoning on 3D point cloud sequential grounding. Firstly, we demonstrate that integrating GroundFlow improves the task accuracy of 3DVG baseline methods by a large margin (+7.5% and +10.2%) in the SG3D benchmark, even outperforming a 3D large language model pre-trained on various datasets. Furthermore, we selectively extract both short-term and long-term step information based on its relevance to the current instruction, enabling GroundFlow to take a comprehensive view of historical information and maintain its temporal understanding advantage as step counts increase. Overall, our work introduces temporal reasoning capabilities to existing 3DVG models and achieves state-of-the-art performance in the SG3D benchmark across five datasets.
Long-Tailed Classification with Multi-Granularity Semantics
Authors: Yuting Liu; Liu Yang; Yu Wang
Venue: ICCV 2025 (main)

Abstract: Real-world data often exhibit long-tailed distributions, which degrade data quality and pose challenges for deep learning. To address this issue, knowledge transfer from head classes to tail classes has been shown to effectively mitigate feature sparsity. However, existing methods often overlook class differences, leading to suboptimal knowledge transfer. While the class space exhibits a label hierarchy, similarity relationships beyond hierarchically related categories remain underexplored. Considering the human ability to process visual perception problems in a multi-granularity manner guided by semantics, this paper presents a novel semantic knowledge-driven contrastive learning method. Inspired by the implicit knowledge embedded in large language models, the proposed LLM-based label semantic generation method overcomes the limitations of the label hierarchy. Additionally, a semantic knowledge graph is constructed based on the extended label information to guide representation learning. This enables the model to dynamically identify relevant classes for learning and facilitates multi-granularity knowledge transfer between similar categories. Experiments on long-tail benchmark datasets, including CIFAR-10-LT, CIFAR-100-LT, and ImageNet-LT, demonstrate that the proposed method significantly improves the accuracy of tail classes and enhances overall performance without compromising the accuracy of head classes.
DAA*: Deep Angular A Star for Image-based Path Planning
Authors: Zhiwei Xu
Venue: ICCV 2025 (main)

Abstract: Path smoothness is often overlooked in path imitation learning from expert demonstrations. In this paper, we introduce a novel learning method, termed deep angular A* (DAA*), by incorporating the proposed path angular freedom (PAF) into A* to improve path similarity through adaptive path smoothness. The PAF aims to explore the effect of move angles on path node expansion by finding the trade-off between their minimum and maximum values, allowing for high adaptiveness for imitation learning. DAA* improves path optimality by closely aligning with the reference path through joint optimization of path shortening and smoothing, which correspond to heuristic distance and PAF, respectively. Throughout comprehensive evaluations on 7 datasets, including 4 maze datasets, 2 video-game datasets, and a real-world drone-view dataset containing 2 scenarios, we demonstrate remarkable improvements of our DAA* over neural A* in path similarity between the predicted and reference paths with a shorter path length when the shortest path is plausible, improving by 9.0% SPR, 6.9% ASIM, and 3.9% PSIM. Furthermore, when jointly learning pathfinding with both path loss and path probability map loss, DAA* significantly outperforms the state-of-the-art TransPath by 6.3% SPR, 6.0% PSIM, and 3.7% ASIM. We also discuss the minor trade-off between path optimality and search efficiency where applicable.
TrackVerse: A Large-Scale Object-Centric Video Dataset for Image-Level Representation Learning
Authors: Yibing Wei; Samuel Church; Victor Suciu; Jinhong Lin; Cheng-En Wu; Pedro Morgado
Venue: ICCV 2025 (main)

Abstract: Video data inherently captures rich, dynamic contexts that reveal objects in varying poses, interactions, and state transitions, offering rich potential for unsupervised object representation learning. However, most prior representation learning methods rely on static image datasets like ImageNet, which lack temporal cues and only provide high-level semantic supervision. Meanwhile, existing natural video datasets are not ideal for learning object-centric representations due to limited object focus and class diversity. To explore unsupervised object representation learning grounded in object dynamics--beyond static appearance--we introduce TrackVerse, a large-scale video dataset of 31.9 million object tracks spanning over 1,000 categories, each capturing the motion, appearance, and evolving states of an object over time. We further propose a variance-aware contrastive learning framework that adapts to data augmentations, encouraging the model to learn state-sensitive features. Extensive experiments demonstrate that representations learned from TrackVerse with variance-aware contrastive learning significantly outperform those from static image datasets and non-object-centric natural video across multiple downstream tasks including object/attributie recognition, action recognition and video instance segmentation, highlighting the rich semantic and state content in TrackVerse feature.
Perspective-Invariant 3D Object Detection
Authors: Ao Liang; Lingdong Kong; Dongyue Lu; Youquan Liu; Jian Fang; Huaici Zhao; Wei Tsang Ooi
Venue: ICCV 2025 (main)

Abstract: With the rise of robotics, LiDAR-based 3D object detection has garnered significant attention in both academia and industry. However, existing datasets and methods predominantly focus on vehicle-mounted platforms, leaving other autonomous platforms underexplored. To bridge this gap, we introduce Pi3DET, the first benchmark featuring LiDAR data and 3D bounding box annotations collected from multiple platforms: vehicle, quadruped, and drone, thereby facilitating research in 3D object detection for non-vehicle platforms as well as cross-platform 3D detection. Based on Pi3DET, we propose a novel cross-platform adaptation framework that transfers knowledge from the well-studied vehicle platform to other platforms. This framework achieves perspective-invariant 3D detection through robust alignment at both geometric and feature levels. Additionally, we establish a benchmark to evaluate the resilience and robustness of current 3D detectors in cross-platform scenarios, providing valuable insights for developing adaptive 3D perception systems. Extensive experiments validate the effectiveness of our approach on challenging cross-platform tasks, demonstrating substantial gains over existing adaptation methods. We hope this work paves the way for generalizable and unified 3D perception systems across diverse and complex environments. Our Pi3DET dataset, cross-platform benchmark suite, and annotation toolkit have been made publicly available.
Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage Gaussian Splats
Authors: Chen Ziwen; Hao Tan; Kai Zhang; Sai Bi; Fujun Luan; Yicong Hong; Li Fuxin; Zexiang Xu
Venue: ICCV 2025 (main)

Abstract: We propose Long-LRM, a feed-forward 3D Gaussian reconstruction model for instant, high-resolution, 360deg wide-coverage, scene-level reconstruction. Specifically, it takes in 32 input images at a resolution of 960x540 and produces the Gaussian reconstruction in just 1 second on a single A100 GPU. To handle the long sequence of 250K tokens brought by the large input size, Long-LRM features a mixture of the recent Mamba2 blocks and the classical transformer blocks, enhanced by a light-weight token merging module and Gaussian pruning steps that balance between quality and efficiency. We evaluate Long-LRM on the large-scale DL3DV benchmark and Tanks&Temples, demonstrating reconstruction quality comparable to the optimization-based methods while achieving an 800x speedup w.r.t. the optimization-based approaches and an input size at least 60x larger than the previous feed-forward approaches. We conduct extensive ablation studies on our model design choices for both rendering quality and computation efficiency. We also explore Long-LRM's compatibility with other Gaussian variants such as 2D GS, which enhances Long-LRM's ability in geometry reconstruction. Project page: http://arthurhero.github.io/projects/llrm/
Unlearning the Noisy Correspondence Makes CLIP More Robust
Authors: Haochen Han; Alex Jinpeng Wang; Peijun Ye; Fangming Liu
Venue: ICCV 2025 (main)

Abstract: The data appetite for Vision-Language Models (VLMs) has continuously scaled up from the early millions to billions today, which faces an untenable trade-off with data quality and inevitably introduces Noisy Correspondence (NC) samples. Undoubtedly, such semantically unrelated data significantly impairs the performance of VLMs. Previous efforts mainly address this challenge by estimating refined alignment for more precise guidance. However, such resource-intensive pipelines that train VLMs from scratch struggle to meet realistic data demands. In this paper, we present a brand new perspective that seeks to directly eliminate the harmful effects of NC in pre-trained VLMs. Specifically, we propose NCU, a Noisy Correspondence Unlearning fine-tuning framework that efficiently enhances VLMs' robustness by forgetting learned noisy knowledge. The key to NCU is learning the hardest negative information, which can provide explicit unlearning direction for both false positives and false negatives. Such twin goals unlearning process can be formalized into one unified optimal transport objective for fast fine-tuning. We validate our approach with the prevailing CLIP model over various downstream tasks. Remarkably, NCU surpasses the robust pre-trained method on zero-shot transfer while with lower computational overhead. The code is available at https://github.com/hhc1997/NCU.
HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly
Authors: Chang Liu; Yunfan Ye; Fan Zhang; Qingyang Zhou; Yuchuan Luo; Zhiping Cai
Venue: ICCV 2025 (main)

Abstract: Numerous synthesized videos from generative models, especially human-centric ones that simulate realistic human actions, pose significant threats to human information security and authenticity. While progress has been made in binary forgery video detection, the lack of fine-grained understanding of forgery types raises concerns regarding both reliability and interpretability, which are critical for real-world applications. To address this limitation, we propose HumanSAM, a new framework that builds upon the fundamental challenges of video generation models. Specifically, HumanSAM aims to classify human-centric forgeries into three distinct types of artifacts commonly observed in generated content: spatial, appearance, and motion anomaly.To better capture the features of geometry, semantics and spatiotemporal consistency, we propose to generate the human forgery representation by fusing two branches of video understanding and spatial depth. We also adopt a rank-based confidence enhancement strategy during the training process to learn more robust representation by introducing three prior scores. For training and evaluation, we construct the first public benchmark, the Human-centric Forgery Video (HFV) dataset, with all types of forgeries carefully annotated semi-automatically. In our experiments, HumanSAM yields promising results in comparison with state-of-the-art methods, both in binary and multi-class forgery classification.
An Information-Theoretic Regularizer for Lossy Neural Image Compression
Authors: Yingwen Zhang; Meng Wang; Xihua Sheng; Peilin Chen; Junru Li; Li Zhang; Shiqi Wang
Venue: ICCV 2025 (main)

Abstract: Lossy image compression networks aim to minimize the latent entropy of images while adhering to specific distortion constraints. However, optimizing the neural network can be challenging due to its nature of learning quantized latent representations. In this paper, our key finding is that minimizing the latent entropy is, to some extent, equivalent to maximizing the conditional source entropy, an insight that is deeply rooted in information-theoretic equalities. Building on this insight, we propose a novel structural regularization method for the neural image compression task by incorporating the negative conditional source entropy into the training objective, such that both the optimization efficacy and the model's generalization ability can be promoted. The proposed information-theoretic regularizer is interpretable, plug-and-play, and imposes no inference overheads. Extensive experiments demonstrate its superiority in regularizing the models and further squeezing bits from the latent representation across various compression structures and unseen domains.
ReTracker: Exploring Image Matching for Robust Online Any Point Tracking
Authors: Dongli Tan; Xingyi He; Sida Peng; Yiqing Gong; Xing Zhu; Jiaming Sun; Ruizhen Hu; Yujun Shen; Hujun Bao; Xiaowei Zhou
Venue: ICCV 2025 (main)

Abstract: This paper aims to establish correspondences for a set of 2D query points across a video sequence in an online manner. Recent methods leverage future frames to achieve smooth point tracking at the current frame, but they still struggle to find points with significant viewpoint changes after long-term occlusions and inherently cannot achieve online tracking. To overcome these challenges, we develop a novel online tracking framework, named ReTracker, that integrates two advances in image matching with tracking-specific designs. First, a decoder network with a global receptive field is incorporated with a temporal attention module to robustly track points undergoing large location changes. Second, the decoder network is adapted to pretrain on large-scale two-view matching data, which offers significantly greater diversity and volume than tracking data, to learn general matching priors. This pretraining strategy effectively enhances our tracker's ability to handle viewpoint and appearance variations after long-term occlusions. Experiments demonstrate that our method outperforms recent online trackers across multiple benchmarks and achieves competitive or superior performance compared to offline methods. Furthermore, we collect an ego-centric, occlusion-heavy dataset to illustrate the retracking capabilities of our approach. The code and dataset will be released for the reproducibility.
DC-AE 1.5: Accelerating Diffusion Model Convergence with Structured Latent Space
Authors: Junyu Chen; Dongyun Zou; Wenkun He; Junsong Chen; Enze Xie; Song Han; Han Cai
Venue: ICCV 2025 (main)

Abstract: We present DC-AE 1.5, a new family of deep compression autoencoders for high-resolution diffusion models. Increasing the autoencoder's latent channel number is a highly effective approach for improving its reconstruction quality. However, it results in slow convergence for diffusion models, leading to poorer generation quality despite better reconstruction quality. This issue limits the quality upper bound of latent diffusion models and hinders the employment of autoencoders with higher spatial compression ratios. We introduce two key innovations to address this challenge: i) Structured Latent Space, a training-based approach to impose a desired channel-wise structure on the latent space with front latent channels capturing object structures and latter latent channels capturing image details; ii) Augmented Diffusion Training, an augmented diffusion training strategy with additional diffusion training objectives on object latent channels to accelerate convergence. With these techniques, DC-AE 1.5 delivers faster convergence and better diffusion scaling results than DC-AE. On ImageNet 512x512, DC-AE-1.5-f64c128 delivers better image generation quality than DC-AE-f32c32 while being 4x faster.
V2M4: 4D Mesh Animation Reconstruction from a Single Monocular Video
Authors: Jianqi Chen; Biao Zhang; Xiangjun Tang; Peter Wonka
Venue: ICCV 2025 (main)

Abstract: We present V2M4, a novel 4D reconstruction method that directly generates a usable 4D mesh animation asset from a single monocular video. Unlike existing approaches that rely on priors from multi-view image and video generation models, our method is based on native 3D mesh generation models. Naively applying 3D mesh generation models to generate a mesh for each frame in a 4D task can lead to issues such as incorrect mesh poses, misalignment of mesh appearance, and inconsistencies in mesh geometry and texture maps. To address these problems, we propose a structured workflow that includes camera search and mesh reposing, condition embedding optimization for mesh appearance refinement, pairwise mesh registration for topology consistency, and global texture map optimization for texture consistency. Our method outputs high-quality 4D animated assets that are compatible with mainstream graphics and game software. Experimental results across a variety of animation types and motion amplitudes demonstrate the generalization and effectiveness of our method.
MixA: A Mixed Attention approach with Stable Lightweight Linear Attention to enhance Efficiency of Vision Transformers at the Edge
Authors: Sabbir Ahmed; Jingtao Li; Weiming Zhuang; Chen Chen; Lingjuan Lyu
Venue: ICCV 2025 (main)

Abstract: Vision transformers (ViTs) have become widely popular due to their strong performance across various computer vision tasks. However, deploying ViTs on edge devices remains a persistent challenge due to their high computational demands primarily caused by the over use of self-attention layers with quadratic complexity together with the resource-intensive softmax operation. To resolve this challenge, linear self-attention approach has emerged as an efficient alternative. Nonetheless, current linear attention methods experience considerable performance degradation compared to the softmax-based quadratic attention. Hence, we propose MixA, a novel mixed attention approach that enhances efficiency of ViT models while maintaining comparable performance to softmax-based quadratic attention. MixA takes a pretrained ViT model and analyzes the significance of each attention layer, and selectively apply ReLU-based quadratic attention in the critical layers to ensure high model performance. To enhance efficiency, MixA selects the less critical layers and replaces them with our novel ReLU-based linear attention module called SteLLA. SteLLA utilizes theoretically motivated normalization terms that improve stability of prior ReLU-based linear attention, resulting in better performance while achieving significant speedup compared to softmax based quadratic attention. Experiments conducted on three benchmark vision tasks show that MixA can significantly improve efficiency of ViT models with competitive performance. Notably, MixA can improve inference speed of DeiT-T model by 22% on Apple M1 chip with only  0.1% accuracy loss.