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Le Zhang, Rabiul Awal, Aishwarya Agrawal

Vision-Language Models (VLMs) such as CLIP exhibit strong image-text comprehension abilities facilitating advances in several downstream tasks such as zero-shot image classification image-text retrieval and text-to-image generation. However the compositional reasoning abilities of existing VLMs remains subpar. The root of this limitation lies in the inadequate alignment between the images and captions in the pretraining datasets. Additionally the current contrastive learning objective fails to focus on fine-grained grounding components like relations actions and attributes resulting in "bag-of-words" representations. We introduce a simple and effective method to improve compositional reasoning in VLMs. Our method better leverages available datasets by refining and expanding the standard image-text contrastive learning framework. Our approach does not require specific annotations and does not incur extra parameters. When integrated with CLIP our technique yields notable improvement over state-of-the-art baselines across five vision-language compositional benchmarks.

Ruiyang Hao, Siqi Fan, Yingru Dai, Zhenlin Zhang, Chenxi Li, Yuntian Wang, Haibao Yu, Wenxian Yang, Jirui Yuan, Zaiqing Nie

The value of roadside perception which could extend the boundaries of autonomous driving and traffic management has gradually become more prominent and acknowledged in recent years. However existing roadside perception approaches only focus on the single-infrastructure sensor system which cannot realize a comprehensive understanding of a traffic area because of the limited sensing range and blind spots. Orienting high-quality roadside perception we need Roadside Cooperative Perception (RCooper) to achieve practical area-coverage roadside perception for restricted traffic areas. Rcooper has its own domain-specific challenges but further exploration is hindered due to the lack of datasets. We hence release the first real-world large-scale RCooper dataset to bloom the research on practical roadside cooperative perception including detection and tracking. The manually annotated dataset comprises 50k images and 30k point clouds including two representative traffic scenes (i.e. intersection and corridor). The constructed benchmarks prove the effectiveness of roadside cooperation perception and demonstrate the direction of further research. Codes and dataset can be accessed at: https://github.com/AIR-THU/DAIR-RCooper.

Ruoxi Shi, Xinyue Wei, Cheng Wang, Hao Su

We present ZeroRF a novel per-scene optimization method addressing the challenge of sparse view 360deg reconstruction in neural field representations. Current breakthroughs like Neural Radiance Fields (NeRF) have demonstrated high-fidelity image synthesis but struggle with sparse input views. Existing methods such as Generalizable NeRFs and per-scene optimization approaches face limitations in data dependency computational cost and generalization across diverse scenarios. To overcome these challenges we propose ZeroRF whose key idea is to integrate a tailored Deep Image Prior into a factorized NeRF representation. Unlike traditional methods ZeroRF parametrizes feature grids with a neural network generator enabling efficient sparse view 360deg reconstruction without any pretraining or additional regularization. Extensive experiments showcase ZeroRF's versatility and superiority in terms of both quality and speed achieving state-of-the-art results on benchmark datasets. ZeroRF's significance extends to applications in 3D content generation and editing. Project page: https://sarahweiii.github.io/zerorf/

Sai Wang, Yutian Lin, Yu Wu

Unsupervised visual grounding methods alleviate the issue of expensive manual annotation of image-query pairs by generating pseudo-queries. However existing methods are prone to confusing the spatial relationships between objects and rely on designing complex prompt modules to generate query texts which severely impedes the ability to generate accurate and comprehensive queries due to ambiguous spatial relationships and manually-defined fixed templates. To tackle these challenges we propose a omni-directional language query generation approach for unsupervised visual grounding named Omni-Q. Specifically we develop a 3D spatial relation module to extend the 2D spatial representation to 3D thereby utilizing 3D location information to accurately determine the spatial position among objects. Besides we introduce a spatial graph module leveraging the power of graph structures to establish accurate and diverse object relationships and thus enhancing the flexibility of query generation. Extensive experiments on five public benchmark datasets demonstrate that our method significantly outperforms existing state-of-the-art unsupervised methods by up to 16.17%. In addition when applied in the supervised setting our method can freely save up to 60% human annotations without a loss of performance.

Nisarg A. Shah, Vibashan VS, Vishal M. Patel

Referring Image Segmentation (RIS) aims to segment objects from an image based on a language description. Recent advancements have introduced transformer-based methods that leverage cross-modal dependencies significantly enhancing performance in referring segmentation tasks. These methods are designed such that each query predicts different masks. However RIS inherently requires a single-mask prediction leading to a phenomenon known as Query Collapse where all queries yield the same mask prediction. This reduces the generalization capability of the RIS model for complex or novel scenarios. To address this issue we propose a Multi-modal Query Feature Fusion technique characterized by two innovative designs: (1) Gaussian enhanced Multi-Modal Fusion a novel visual grounding mechanism that enhances overall representation by extracting rich local visual information and global visual-linguistic relationships and (2) A Dynamic Query Module that produces a diverse set of queries through a scoring network where the network selectively focuses on queries for objects referred to in the language description. Moreover we show that including an auxiliary loss to increase the distance between mask representations of different queries further enhances performance and mitigates query collapse. Extensive experiments conducted on four benchmark datasets validate the effectiveness of our framework.

Yuqi Wang, Yuntao Chen, Xingyu Liao, Lue Fan, Zhaoxiang Zhang

Comprehensive modeling of the surrounding 3D world is crucial for the success of autonomous driving. However existing perception tasks like object detection road structure segmentation depth & elevation estimation and open-set object localization each only focus on a small facet of the holistic 3D scene understanding task. This divide-and-conquer strategy simplifies the algorithm development process but comes at the cost of losing an end-to-end unified solution to the problem. In this work we address this limitation by studying camera-based 3D panoptic segmentation aiming to achieve a unified occupancy representation for camera-only 3D scene understanding. To achieve this we introduce a novel method called PanoOcc which utilizes voxel queries to aggregate spatiotemporal information from multi-frame and multi-view images in a coarse-to-fine scheme integrating feature learning and scene representation into a unified occupancy representation. We have conducted extensive ablation studies to validate the effectiveness and efficiency of the proposed method. Our approach achieves new state-of-the-art results for camera-based semantic segmentation and panoptic segmentation on the nuScenes dataset. Furthermore our method can be easily extended to dense occupancy prediction and has demonstrated promising performance on the Occ3D benchmark. The code will be made available at https://github.com/Robertwyq/PanoOcc.

Jingjing Xie, Yuxin Zhang, Mingbao Lin, Liujuan Cao, Rongrong Ji

Post-training Sparsity (PTS) is a recently emerged avenue that chases efficient network sparsity with limited data in need. Existing PTS methods however undergo significant performance degradation compared with traditional methods that retrain the sparse networks via the whole dataset especially at high sparsity ratios. In this paper we attempt to reconcile this disparity by transposing three cardinal factors that profoundly alter the performance of conventional sparsity into the context of PTS. Our endeavors particularly comprise (1) A base-decayed sparsity objective that promotes efficient knowledge transferring from dense network to the sparse counterpart. (2) A reducing-regrowing search algorithm designed to ascertain the optimal sparsity distribution while circumventing overfitting to the small calibration set in PTS. (3) The employment of dynamic sparse training predicated on the preceding aspects aimed at comprehensively optimizing the sparsity structure while ensuring training stability. Our proposed framework termed UniPTS is validated to be much superior to existing PTS methods across extensive benchmarks. As an illustration it amplifies the performance of POT a recently proposed recipe from 3.9% to 68.6% when pruning ResNet-50 at 90% sparsity ratio on ImageNet.

Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote 等

We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g. sports music dance bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts yielding long-form captures from 1 to 42 minutes each and 1286 hours of video combined. The multimodal nature of the dataset is unprecedented: the video is accompanied by multichannel audio eye gaze 3D point clouds camera poses IMU and multiple paired language descriptions---including a novel "expert commentary" done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity we also present a suite of benchmark tasks and their annotations including fine-grained activity understanding proficiency estimation cross-view translation and 3D hand/body pose. All resources are open sourced to fuel new research in the community.

Jiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang, Dahua Lin, Yu Qiao, Peng Gao, Xiangyu Yue

Multimodal large language models (MLLMs) have gained significant attention due to their strong multimodal understanding capability. However existing works rely heavily on modality-specific encoders which usually differ in architecture and are limited to common modalities. In this paper we present OneLLM an MLLM that aligns eight modalities to language using a unified framework. We achieve this through a unified multimodal encoder and a progressive multimodal alignment pipeline. In detail we first train an image projection module to connect a vision encoder with LLM. Then we build a universal projection module (UPM) by mixing multiple image projection modules and dynamic routing. Finally we progressively align more modalities to LLM with the UPM. To fully leverage the potential of OneLLM in following instructions we also curated a comprehensive multimodal instruction dataset including 2M items from image audio video point cloud depth/normal map IMU and fMRI brain activity. OneLLM is evaluated on 25 diverse benchmarks encompassing tasks such as multimodal captioning question answering and reasoning where it delivers excellent performance. Code data model and online demo are available at https://github.com/csuhan/OneLLM

Nirat Saini, Khoi Pham, Abhinav Shrivastava

Learning from seen attribute-object pairs to generalize to unseen compositions has been studied extensively in Compositional Zero-Shot Learning (CZSL). However CZSL setup is still limited to seen attributes and objects and cannot generalize to unseen concepts and their compositions. To overcome this limitation we propose a new task Open Vocabulary-Compositional Zero-shot Learning (OV-CZSL) where unseen attributes objects and unseen compositions are evaluated. To show that OV-CZSL is a challenging yet solvable problem we propose three new benchmarks based on existing datasets MIT-States C-GQA and VAW-CZSL along with new baselines and evaluation setup. We use language embeddings and external vocabulary with our novel neighborhood expansion loss to allow any method to learn semantic correlations between seen and unseen primitives.

Leigang Qu, Wenjie Wang, Yongqi Li, Hanwang Zhang, Liqiang Nie, Tat-Seng Chua

Despite advancements in text-to-image generation (T2I) prior methods often face text-image misalignment problems such as relation confusion in generated images. Existing solutions involve cross-attention manipulation for better compositional understanding or integrating large language models for improved layout planning. However the inherent alignment capabilities of T2I models are still inadequate. By reviewing the link between generative and discriminative modeling we posit that T2I models' discriminative abilities may reflect their text-image alignment proficiency during generation. In this light we advocate bolstering the discriminative abilities of T2I models to achieve more precise text-to-image alignment for generation. We present a discriminative adapter built on T2I models to probe their discriminative abilities on two representative tasks and leverage discriminative fine-tuning to improve their text-image alignment. As a bonus of the discriminative adapter a self-correction mechanism can leverage discriminative gradients to better align generated images to text prompts during inference. Comprehensive evaluations across three benchmark datasets including both in-distribution and out-of-distribution scenarios demonstrate our method's superior generation performance. Meanwhile it achieves state-of-the-art discriminative performance on the two discriminative tasks compared to other generative models. The code is available at https://dpt-t2i.github.io/.

Xiaojie Jin, Bowen Zhang, Weibo Gong, Kai Xu, Xueqing Deng, Peng Wang, Zhao Zhang, Xiaohui Shen, Jiashi Feng

State-of-the-art video-text retrieval (VTR) methods typically involve fully fine-tuning a pre-trained model (e.g. CLIP) on specific datasets. However this can result in significant storage costs in practical applications as a separate model per task must be stored. To address this issue we present our pioneering work that enables parameter-efficient VTR using a pre-trained model with only a small number of tunable parameters during training. Towards this goal we propose a new method dubbed Multimodal Video Adapter (MV-Adapter) for efficiently transferring the knowledge in the pre-trained CLIP from image-text to video-text. Specifically MV-Adapter utilizes bottleneck structures in both video and text branches along with two novel components. The first is a Temporal Adaptation Module that is incorporated in the video branch to introduce global and local temporal contexts. We also train weights calibrations to adjust to dynamic variations across frames. The second is Cross Modality Tying that generates weights for video/text branches through sharing cross modality factors for better aligning between modalities. Thanks to above innovations MV-Adapter can achieve comparable or better performance than standard fine-tuning with negligible parameters overhead. Notably MV-Adapter consistently outperforms various competing methods in V2T/T2V tasks with large margins on five widely used VTR benchmarks (MSR-VTT MSVD LSMDC DiDemo and ActivityNet). Codes will be released.

Zongwei Wu, Jilai Zheng, Xiangxuan Ren, Florin-Alexandru Vasluianu, Chao Ma, Danda Pani Paudel, Luc Van Gool, Radu Timofte

In the realm of video object tracking auxiliary modalities such as depth thermal or event data have emerged as valuable assets to complement the RGB trackers. In practice most existing RGB trackers learn a single set of parameters to use them across datasets and applications. However a similar single-model unification for multi-modality tracking presents several challenges. These challenges stem from the inherent heterogeneity of inputs -- each with modality-specific representations the scarcity of multi-modal datasets and the absence of all the modalities at all times. In this work we introduce Un-Track a Unified Tracker of a single set of parameters for any modality. To handle any modality our method learns their common latent space through low-rank factorization and reconstruction techniques. More importantly we use only the RGB-X pairs to learn the common latent space. This unique shared representation seamlessly binds all modalities together enabling effective unification and accommodating any missing modality all within a single transformer-based architecture. Our Un-Track achieves +8.1 absolute F-score gain on the DepthTrack dataset by introducing only +2.14 (over 21.50) GFLOPs with +6.6M (over 93M) parameters through a simple yet efficient prompting strategy. Extensive comparisons on five benchmark datasets with different modalities show that Un-Track surpasses both SOTA unified trackers and modality-specific counterparts validating our effectiveness and practicality. The source code is publicly available at https://github.com/Zongwei97/UnTrack.

Shizhan Gong, Qi Dou, Farzan Farnia

Gradient-based saliency maps have been widely used to explain the decisions of deep neural network classifiers. However standard gradient-based interpretation maps including the simple gradient and integrated gradient algorithms often lack desired structures such as sparsity and connectedness in their application to real-world computer vision models. A common approach to induce sparsity-based structures into gradient-based saliency maps is to modify the simple gradient scheme using sparsification or norm-based regularization. However one drawback with such post-processing approaches is the potentially significant loss in fidelity to the original simple gradient map. In this work we propose to apply adversarial training as an in-processing scheme to train neural networks with structured simple gradient maps. We demonstrate an existing duality between the regularized norms of the adversarial perturbations and gradient-based maps whereby we design adversarial training schemes promoting sparsity and group-sparsity properties in simple gradient maps. We present comprehensive numerical results to show the influence of our proposed norm-based adversarial training methods on the standard gradient-based maps of standard neural network architectures on benchmark image datasets.

Zhangsihao Yang, Mingyuan Zhou, Mengyi Shan, Bingbing Wen, Ziwei Xuan, Mitch Hill, Junjie Bai, Guo-Jun Qi, Yalin Wang

Our paper aims to generate diverse and realistic animal motion sequences from textual descriptions without a large-scale animal text-motion dataset. While the task of text-driven human motion synthesis is already extensively studied and benchmarked it remains challenging to transfer this success to other skeleton structures with limited data. In this work we design a model architecture that imitates Generative Pretraining Transformer (GPT) utilizing prior knowledge learned from human data to the animal domain. We jointly train motion autoencoders for both animal and human motions and at the same time optimize through the similarity scores among human motion encoding animal motion encoding and text CLIP embedding. Presenting the first solution to this problem we are able to generate animal motions with high diversity and fidelity quantitatively and qualitatively outperforming the results of training human motion generation baselines on animal data. Additionally we introduce AnimalML3D the first text-animal motion dataset with 1240 animation sequences spanning 36 different animal identities. We hope this dataset would mediate the data scarcity problem in text-driven animal motion generation providing a new playground for the research community.

Yunqi Miao, Jiankang Deng, Jungong Han

Although diffusion models are rising as a powerful solution for blind face restoration they are criticized for two problems: 1) slow training and inference speed and 2) failure in preserving identity and recovering fine-grained facial details. In this work we propose WaveFace to solve the problems in the frequency domain where low- and high-frequency components decomposed by wavelet transformation are considered individually to maximize authenticity as well as efficiency. The diffusion model is applied to recover the low-frequency component only which presents general information of the original image but 1/16 in size. To preserve the original identity the generation is conditioned on the low-frequency component of low-quality images at each denoising step. Meanwhile high-frequency components at multiple decomposition levels are handled by a unified network which recovers complex facial details in a single step. Evaluations on four benchmark datasets show that: 1) WaveFace outperforms state-of-the-art methods in authenticity especially in terms of identity preservation and 2) authentic images are restored with the efficiency 10x faster than existing diffusion model-based BFR methods.

Chuong Huynh, Seoung Wug Oh, Abhinav Shrivastava, Joon-Young Lee

Human matting is a foundation task in image and video processing where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional guidance or improve the temporal consistency of a single instance across frames. We propose a new framework MaGGIe Masked Guided Gradual Human Instance Matting which predicts alpha mattes progressively for each human instances while maintaining the computational cost precision and consistency. Our method leverages modern architectures including transformer attention and sparse convolution to output all instance mattes simultaneously without exploding memory and latency. Although keeping constant inference costs in the multiple-instance scenario our framework achieves robust and versatile performance on our proposed synthesized benchmarks. With the higher quality image and video matting benchmarks the novel multi-instance synthesis approach from publicly available sources is introduced to increase the generalization of models in real-world scenarios. Our code and datasets are available at https://maggie-matt.github.io

Youquan Liu, Lingdong Kong, Xiaoyang Wu, Runnan Chen, Xin Li, Liang Pan, Ziwei Liu, Yuexin Ma

A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception. This work presents M3Net a one-of-a-kind framework for fulfilling multi-task multi-dataset multi-modality LiDAR segmentation in a universal manner using just a single set of parameters. To better exploit data volume and diversity we first combine large-scale driving datasets acquired by different types of sensors from diverse scenes and then conduct alignments in three spaces namely data feature and label spaces during the training. As a result M3Net is capable of taming heterogeneous data for training state-of-the-art LiDAR segmentation models. Extensive experiments on twelve LiDAR segmentation datasets verify our effectiveness. Notably using a shared set of parameters M3Net achieves 75.1% 83.1% and 72.4% mIoU scores respectively on the official benchmarks of SemanticKITTI nuScenes and Waymo Open.

Wenhui Xiao, Rodrigo Santa Cruz, David Ahmedt-Aristizabal, Olivier Salvado, Clinton Fookes, Leo Lebrat

Neural Rendering representations have significantly contributed to the field of 3D computer vision. Given their potential considerable efforts have been invested to improve their performance. Nonetheless the essential question of selecting training views is yet to be thoroughly investigated. This key aspect plays a vital role in achieving high-quality results and aligns with the well-known tenet of deep learning: "garbage in garbage out". In this paper we first illustrate the importance of view selection by demonstrating how a simple rotation of the test views within the most pervasive NeRF dataset can lead to consequential shifts in the performance rankings of state-of-the-art techniques. To address this challenge we introduce a unified framework for view selection methods and devise a thorough benchmark to assess its impact. Significant improvements can be achieved without leveraging error or uncertainty estimation but focusing on uniform view coverage of the reconstructed object resulting in a training-free approach. Using this technique we show that high-quality renderings can be achieved faster by using fewer views. We conduct extensive experiments on both synthetic datasets and realistic data to demonstrate the effectiveness of our proposed method compared with random conventional error-based and uncertainty-guided view selection.

Townim Faisal Chowdhury, Kewen Liao, Vu Minh Hieu Phan, Minh-Son To, Yutong Xie, Kevin Hung, David Ross, Anton van den Hengel, Johan W. Verjans, Zhibin Liao

Deep Neural Networks (DNNs) are widely used for visual classification tasks but their complex computation process and black-box nature hinder decision transparency and interpretability. Class activation maps (CAMs) and recent variants provide ways to visually explain the DNN decision-making process by displaying 'attention' heatmaps of the DNNs. Nevertheless the CAM explanation only offers relative attention information that is on an attention heatmap we can interpret which image region is more or less important than the others. However these regions cannot be meaningfully compared across classes and the contribution of each region to the model's class prediction is not revealed. To address these challenges that ultimately lead to better DNN Interpretation in this paper we propose CAPE a novel reformulation of CAM that provides a unified and probabilistically meaningful assessment of the contributions of image regions. We quantitatively and qualitatively compare CAPE with state-of-the-art CAM methods on CUB and ImageNet benchmark datasets to demonstrate enhanced interpretability. We also test on a cytology imaging dataset depicting a challenging Chronic Myelomonocytic Leukemia (CMML) diagnosis problem. Code is available at:https://github.com/AIML-MED/CAPE.