GenAI4ELab/papercli-papers huggingface dataset:
MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective
Authors: Weitian Wang; Rai Shubham; Cecilia De La Parra; Akash Kumar
Venue: ICCV 2025 (main)

Abstract: In this paper, we propose MixA-Q, a mixed-precision activation quantization framework that leverages intra-layer activation sparsity (a concept widely explored in activation pruning methods) for efficient inference of quantized window-based vision transformers. For a given uniform-bit quantization configuration, MixA-Q separates the batched window computations within Swin blocks and assigns a lower bit width to the activations of less important windows, improving the trade-off between model performance and efficiency. We introduce a Two-Branch Swin Block that processes activations separately in high- and low-bit precision, enabling seamless integration of our method with most quantization-aware training (QAT) and post-training quantization (PTQ) methods, or with simple modifications. Our experimental evaluations over the COCO dataset demonstrate that MixA-Q achieves a training-free 1.35x computational speedup without accuracy loss in PTQ configuration. With QAT, MixA-Q achieves a lossless 1.25x speedup and a 1.53x speedup with only a 1% mAP drop by incorporating activation pruning. Notably, by reducing the quantization error in important regions, our sparsity-aware quantization adaptation improves the mAP of the quantized W4A4 model (with both weights and activations in 4-bit precision) by 0.7%, reducing quantization degradation by 24%.
A Quality-Guided Mixture of Score-Fusion Experts Framework for Human Recognition
Authors: Jie Zhu; Yiyang Su; Minchul Kim; Anil Jain; Xiaoming Liu
Venue: ICCV 2025 (main)

Abstract: Whole-body biometric recognition is a challenging multi-modal task that integrates various biometric modalities, including face, gait, and body. This integration is essential for overcoming the limitations of unimodal systems. Traditionally, whole-body recognition involves deploying different models to process multiple modalities, achieving the final outcome by score-fusion (e.g., weighted averaging similarity matrices from each model). However, these conventional methods may overlook the variations in score distributions of individual modalities, making it challenging to improve final performance. In this work, we present Quality-guided Mixture of score-fusion Experts (QME), a novel framework designed for improving whole-body biometric recognition performance through a learnable score-fusion strategy using a Mixture of Experts (MoE). We introduce a novel pseudo quality loss for quality estimation with a modality-specific Quality Estimator (QE), and a score triplet loss to improve the metric performance. Extensive experiments on multiple whole-body biometric datasets demonstrate the effectiveness of our proposed approach, achieving state-of-the-art results across various metrics compared to baseline methods. Our method is effective for multi-modal and multi-model, addressing key challenges such as model misalignment in the similarity score domain and variability in data quality. Code is available at the \href https://github.com/jiezhu23/QME_ICCV25  Project Link
EMD: Explicit Motion Modeling for High-Quality Street Gaussian Splatting
Authors: Xiaobao Wei; Qingpo Wuwu; Zhongyu Zhao; Zhuangzhe Wu; Nan Huang; Ming Lu; Ningning Ma; Shanghang Zhang
Venue: ICCV 2025 (main)

Abstract: Photorealistic reconstruction of street scenes is essential for developing real-world simulators in autonomous driving. While recent methods based on 3D/4D Gaussian Splatting (GS) have demonstrated promising results, they still encounter challenges in complex street scenes due to the unpredictable motion of dynamic objects. Current methods typically decompose street scenes into static and dynamic objects, learning the Gaussians in either a supervised manner (e.g., w/ 3D bounding-box) or a self-supervised manner (e.g., w/o 3D bounding-box). However, these approaches do not effectively model the motions of dynamic objects (e.g., the motion speed of pedestrians is clearly different from that of vehicles), resulting in suboptimal scene decomposition. To address this, we propose Explicit Motion Decomposition (EMD), which models the motions of dynamic objects by introducing learnable motion embeddings to the Gaussians, enhancing the decomposition in street scenes. The proposed plug-and-play EMD module compensates for the lack of motion modeling in self-supervised street Gaussian splatting methods. We also introduce tailored training strategies to extend EMD to supervised approaches. Comprehensive experiments demonstrate the effectiveness of our method, achieving state-of-the-art novel view synthesis performance in self-supervised settings. The code is available at: https://qingpowuwu.github.io/emd
MOVE: Motion-Guided Few-Shot Video Object Segmentation
Authors: Kaining Ying; Hengrui Hu; Henghui Ding
Venue: ICCV 2025 (main)

Abstract: This work addresses motion-guided few-shot video object segmentation (FSVOS), which aims to segment dynamic objects in videos based on a few annotated examples with the same motion patterns. Existing FSVOS datasets and methods typically focus on object categories, which are static attributes that ignore the rich temporal dynamics in videos, limiting their application in scenarios requiring motion understanding. To fill this gap, we introduce MOVE, a large-scale dataset specifically designed for motion-guided FSVOS. Based on MOVE, we comprehensively evaluate 6 state-of-the-art methods from 3 different related tasks across 2 experimental settings. Our results reveal that current methods struggle to address motion-guided FSVOS, prompting us to analyze the associated challenges and propose a baseline method, Decoupled Motion Appearance Network (DMA). Experiments demonstrate that our approach achieves superior performance in few shot motion understanding, establishing a solid foundation for future research in this direction.
Neural Multi-View Self-Calibrated Photometric Stereo without Photometric Stereo Cues
Authors: Xu Cao; Takafumi Taketomi
Venue: ICCV 2025 (main)

Abstract: We propose a neural inverse rendering approach that jointly reconstructs geometry, spatially varying reflectance, and lighting conditions from multi-view images captured under varying directional lighting. Unlike prior multi-view photometric stereo methods that require light calibration or intermediate cues such as per-view normal maps, our method jointly optimizes all scene parameters from raw images in a single stage. We represent both geometry and reflectance as neural implicit fields and apply shadow-aware volume rendering. A spatial network first predicts the signed distance and a reflectance latent code for each scene point. A reflectance network then estimates reflectance values conditioned on the latent code and angularly encoded surface normal, view, and light directions. The proposed method outperforms state-of-the-art normal-guided approaches in shape and lighting estimation accuracy, generalizes to view-unaligned multi-light images, and handles objects with challenging geometry and reflectance.
CO2-Net: A Physics-Informed Spatio-Temporal Model for Global Surface CO2 Reconstruction
Authors: Hao Zheng; Yuting Zheng; Hanbo Huang; Chaofan Sun; Enhui Liao; Lin Liu; Yi Han; Hao Zhou; Shiyu Liang
Venue: ICCV 2025 (main)

Abstract: Reconstructing atmospheric surface \text CO _2 is crucial for understanding climate dynamics and informing global mitigation strategies. Traditional inversion models achieve precise global \text CO _2 reconstruction but rely heavily on uncertain prior estimates of fluxes and emissions. Inspired by recent advances in data-driven weather forecasting, we explore whether data-driven models can reduce reliance on these priors. However, \text CO _2 reconstruction presents unique challenges, including complex spatio-temporal dynamics, periodic patterns and sparse observations. We propose \text CO _2-Net, a data-driven model that addresses these challenges without requiring extensive prior data. We formulate \text CO _2 reconstruction as solving a constrained advection-diffusion equation and derive three key components: physics-informed spatio-temporal factorization for capturing complex transport dynamics, wind-based embeddings for modeling periodic variations and a semi-supervised loss for integrating sparse \text CO _2 observations with dense meteorological data. \text CO _2-Net is designed in three sizes---small (S), base (B) and large (L)---to balance performance and efficiency. On CMIP6 reanalysis data, \text CO _2-Net (S) and (L) reduce RMSE by  11%  and  71% , respectively, when compared to the best data-driven baseline. On real observations, \text CO _2-Net (L) achieves RMSE comparable to inversion models. The ablation study shows that the effectiveness of wind-based embedding and semi-supervised loss stems from their compatibility with our spatio-temporal factorization. Code is available at https://github.com/Leamonz/CORE.
Multi-Modal Multi-Task Unified Embedding Model (M3T-UEM): A Task-Adaptive Representation Learning Framework
Authors: Rohan Sharma; Changyou Chen; Feng-Ju Chang; Seongjun Yun; Xiaohu Xie; Rui Meng; Dehong Xu; Alejandro Mottini; Qingjun Cui
Venue: ICCV 2025 (main)

Abstract: We present Multi-Modal Multi-Task Unified Embedding Model (M3T-UEM), a framework that advances vision-language matching and retrieval by leveraging a large language model (LLM) backbone. While concurrent LLM-based approaches like VLM2VEC, MM-Embed, NV-Embed, and MM-GEM have demonstrated impressive capabilities in multi-modal and multi-task scenarios, our work introduces novel mechanisms for task-adaptive learning and embedding extraction that further enhance the potential of LLM-based retrieval systems. Our key technical contribution lies in the development of a task-aware contrastive learning framework with an automated Bayesian weighing mechanism. This approach provides a principled way to balance multiple tasks during training, departing from conventional contrastive learning strategies. We further enhance the framework through a multiple-token summarization strategy and an auxiliary language modeling objective, which together significantly improve retrieval performance.Comprehensive experiments on M-BEIR and ICinW benchmarks demonstrate M3T-UEM's effectiveness, showing competitive or superior performance compared to both traditional encoder-based methods and recent LLM-based approaches. Furthermore, we demonstrate particular strengths in handling compositional conceptual changes and multilingual scenarios owing to the incorporation of an LLM backbone where the method drastically outperforms CLIP in zero-shot settings, often by orders of magnitude.
Randomized Autoregressive Visual Generation
Authors: Qihang Yu; Ju He; Xueqing Deng; Xiaohui Shen; Liang-Chieh Chen
Venue: ICCV 2025 (main)

Abstract: This paper presents Randomized AutoRegressive modeling (RAR) for visual generation, which sets a new state-of-the-art performance on the image generation task while maintaining full compatibility with language modeling frameworks. The proposed RAR is simple: during a standard autoregressive training process with a next-token prediction objective, the input sequence-typically ordered in raster form-is randomly permuted into different factorization orders with a probability r, where r starts at 1 and linearly decays to 0 over the course of training. This annealing training strategy enables the model to learn to maximize the expected likelihood over all factorization orders and thus effectively improve the model's capability of modeling bidirectional contexts. Importantly, RAR preserves the integrity of the autoregressive modeling framework, ensuring full compatibility with language modeling while significantly improving performance in image generation. On the ImageNet-256 benchmark, RAR achieves an FID score of 1.48, not only surpassing prior state-of-the-art autoregressive image generators but also outperforming leading diffusion-based and masked transformer-based methods. Code and models are available at https://github.com/bytedance/1d-tokenizer
Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views
Authors: Xiangdong Zhang; Shaofeng Zhang; Junchi Yan
Venue: ICCV 2025 (main)

Abstract: Point cloud learning, especially in a self-supervised way without manual labels, has gained growing attention in both vision and learning communities due to its potential utility in a wide range of applications. Most existing generative approaches for point cloud self-supervised learning focus on recovering masked points from visible ones within a single view. Recognizing that a two-view pre-training paradigm inherently introduces greater diversity and variance, it may thus enable more challenging and informative pre-training. Inspired by this, we explore the potential of two-view learning in this domain. In this paper, we propose Point-PQAE, a cross-reconstruction generative paradigm that first generates two decoupled point clouds/views and then reconstructs one from the other. To achieve this goal, we develop a crop mechanism for point cloud view generation for the first time and further propose a novel positional encoding to represent the 3D relative position between the two decoupled views. The cross-reconstruction significantly increases the difficulty of pre-training compared to self-reconstruction, which enables our method to surpass previous single-modal self-reconstruction methods in 3D self-supervised learning. Specifically, it outperforms the self-reconstruction baseline (Point-MAE) by 6.5%, 7.0%, and 6.7% in three variants of ScanObjectNN with the Mlp-Linear evaluation protocol. The code is available at https://github.com/aHapBean/Point-PQAE.
Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation and Reconstruction
Authors: Yuanhao Cai; He Zhang; Kai Zhang; Yixun Liang; Mengwei Ren; Fujun Luan; Qing Liu; Soo Ye Kim; Jianming Zhang; Zhifei Zhang; Yuqian Zhou; Yulun Zhang; Xiaokang Yang; Zhe Lin; Alan Yuille
Venue: ICCV 2025 (main)

Abstract: Existing feedforward image-to-3D methods mainly rely on 2D multi-view diffusion models that cannot guarantee 3D consistency. These methods easily collapse when changing the prompt view direction and mainly handle object-centric cases. In this paper, we propose a novel single-stage 3D diffusion model, DiffusionGS, for object generation and scene reconstruction from a single view. DiffusionGS directly outputs 3D Gaussian point clouds at each timestep to enforce view consistency and allow the model to generate robustly given prompt views of any directions, beyond object-centric inputs. Plus, to improve the capability and generality of DiffusionGS, we scale up 3D training data by developing a scene-object mixed training strategy. Experiments show that DiffusionGS yields improvements of 2.20 dB/23.25 and 1.34 dB/19.16 in PSNR/FID for objects and scenes than the state-of-the-art methods, without using 2D diffusion prior and depth estimator. Plus, our method enjoys over 5x faster speed ( 6s on an A100 GPU). The project page is at https://caiyuanhao1998.github.io/project/DiffusionGS/
Variance-Based Pruning for Accelerating and Compressing Trained Networks
Authors: Uranik Berisha; Jens Mehnert; Alexandru Paul Condurache
Venue: ICCV 2025 (main)

Abstract: Increasingly expensive training of ever larger models such as Vision Transfomers motivate reusing the vast library of already trained state-of-the-art networks. However, their latency, high computational costs and memory demands pose significant challenges for deployment, especially on resource-constrained hardware. While structured pruning methods can reduce these factors, they often require costly retraining, sometimes for up to hundreds of epochs, or even training from scratch to recover the lost accuracy resulting from the structural modifications. Maintaining the provided performance of trained models after structured pruning and thereby avoiding extensive retraining remains a challenge. To solve this, we introduce Variance-Based Pruning, a simple and structured one-shot pruning technique for efficiently compressing networks, with minimal finetuning. Our approach first gathers activation statistics, which are then used to select neurons for pruning. Simultaneously the mean activations are integrated back into the model to preserve a high degree of performance. On ImageNet-1k recognition tasks, we demonstrate that directly after pruning DeiT-Base retains over 70% of its original performance and requires only 10 epochs of fine-tuning to regain 99% of the original accuracy while simultaneously reducing MACs by 35% and model size by 36%, thus speeding up the model by 1.44 times.
Learning Normal Flow Directly From Events
Authors: Dehao Yuan; Levi Burner; Jiayi Wu; Minghui Liu; Jingxi Chen; Yiannis Aloimonos; Cornelia Fermüller
Venue: ICCV 2025 (main)

Abstract: Event-based motion field estimation is an important task. However, current optical flow methods face challenges: learning-based approaches, often frame-based and relying on CNNs, lack cross-domain transferability, while model-based methods, though more robust, are less accurate. To address the limitations of optical flow estimation, recent works have focused on normal flow, which can be more reliably measured in regions with limited texture or strong edges. However, existing normal flow estimators are predominantly model-based and suffer from high errors. In this paper, we propose a novel supervised point-based method for normal flow estimation that overcomes the limitations of existing event learning-based approaches. Using a local point cloud encoder, our method directly estimates per-event normal flow from raw events, offering multiple unique advantages: 1) It produces temporally and spatially sharp predictions. 2) It supports more diverse data augmentation, such as random rotation, to improve robustness across various domains. 3) It naturally supports uncertainty quantification via ensemble inference, which benefits downstream tasks. 4) It enables training and inference on undistorted data in normalized camera coordinates, improving transferability across cameras. Extensive experiments demonstrate our method achieves better and more consistent performance than state-of-the-art methods when transferred across different datasets. Leveraging this transferability, we train our model on the union of datasets and release it for public use. Finally, we introduce an egomotion solver based on a maximum-margin problem that uses normal flow and IMU to achieve strong performance in challenging scenarios. Codes are available at https://github.com/dhyuan99/VecKM_flow.
Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data
Authors: Thu Hang Phung; Duong M. Nguyen; Thanh Trung Huynh; Quoc Viet Hung Nguyen; Trong Nghia Hoang; Phi Le Nguyen
Venue: ICCV 2025 (main)

Abstract: This paper introduces a generalized federated prompt-tuning framework for practical scenarios where local datasets are multi-modal and exhibit different distributional patterns of missing features at the input level. The proposed framework bridges the gap between federated learning and multi-modal prompt-tuning which have traditionally focused on either uni-modal or centralized data. A key challenge in this setting arises from the lack of semantic alignment between prompt instructions that encode similar distributional patterns of missing data across different clients. To address this, our framework introduces specialized client-tuning and server-aggregation designs that simultaneously optimize, align, and aggregate prompt-tuning instructions across clients and data modalities. This allows prompt instructions to complement one another and be combined effectively. Extensive evaluations on diverse multimodal benchmark datasets demonstrate that our work consistently outperforms state-of-the-art (SOTA) baselines.
Trace3D: Consistent Segmentation Lifting via Gaussian Instance Tracing
Authors: Hongyu Shen; Junfeng Ni; Yixin Chen; Weishuo Li; Mingtao Pei; Siyuan Huang
Venue: ICCV 2025 (main)

Abstract: We address the challenge of lifting 2D visual segmentation to 3D in Gaussian Splatting. Existing methods often suffer from inconsistent 2D masks across viewpoints and produce noisy segmentation boundaries as they neglect these semantic cues to refine the learned Gaussians. To overcome this, we introduce Gaussian Instance Tracing (GIT), which augments the standard Gaussian representation with an instance weight matrix across input views. Leveraging the inherent consistency of Gaussians in 3D, we use this matrix to identify and correct 2D segmentation inconsistencies. Furthermore, since each Gaussian ideally corresponds to a single object, we propose a GIT-guided adaptive density control mechanism to split and prune ambiguous Gaussians during training, resulting in sharper and more coherent 2D and 3D segmentation boundaries. Experimental results show that our method extracts clean 3D assets and consistently improves 3D segmentation in both online (e.g., self-prompting) and offline (e.g., contrastive lifting) settings, enabling applications such as hierarchical segmentation, object extraction, and scene editing.
LMM-Det: Make Large Multimodal Models Excel in Object Detection
Authors: Jincheng Li; Chunyu Xie; Ji Ao; Dawei Leng; Yuhui Yin
Venue: ICCV 2025 (main)

Abstract: Large multimodal models (LMMs) have garnered wide-spread attention and interest within the artificial intelligence research and industrial communities, owing to their remarkable capability in multimodal understanding, reasoning, and in-context learning, among others. While LMMs have demonstrated promising results in tackling multimodal tasks like image captioning, visual question answering, and visual grounding, the object detection capabilities of LMMs exhibit a significant gap compared to specialist detectors. To bridge the gap, we depart from the conventional methods of integrating heavy detectors with LMMs and propose LMM-Det, a simple yet effective approach that leverages a large multimodal model for vanilla object detection without relying on specialized detection modules. Specifically, we conduct a comprehensive exploratory analysis when a large multimodal model meets with object detection, revealing that the recall rate degrades significantly compared with specialist detection models. To mitigate this, we propose to increase the recall rate by introducing data distribution adjustment and inference optimization tailored for object detection. We re-organize the instruction conversations to enhance the object detection capabilities of large multimodal models. We claim that a large multimodal model possesses detection capability without any extra detection modules. Extensive experiments support our claim and show the effectiveness of the versatile LMM-Det. The datasets, models, and codes are available at https://github.com/360CVGroup/LMM-Det.