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Hao Xu, Haipeng Li, Yinqiao Wang, Shuaicheng Liu, Chi-Wing Fu

Reconstructing 3D hand mesh robustly from a single image is very challenging due to the lack of diversity in existing real-world datasets. While data synthesis helps relieve the issue the syn-to-real gap still hinders its usage. In this work we present HandBooster a new approach to uplift the data diversity and boost the 3D hand-mesh reconstruction performance by training a conditional generative space on hand-object interactions and purposely sampling the space to synthesize effective data samples. First we construct versatile content-aware conditions to guide a diffusion model to produce realistic images with diverse hand appearances poses views and backgrounds; favorably accurate 3D annotations are obtained for free. Then we design a novel condition creator based on our similarity-aware distribution sampling strategies to deliberately find novel and realistic interaction poses that are distinctive from the training set. Equipped with our method several baselines can be significantly improved beyond the SOTA on the HO3D and DexYCB benchmarks. Our code will be released on https://github.com/hxwork/HandBooster_Pytorch.

Sachit Menon, Ishan Misra, Rohit Girdhar

We introduce a new task of generating "Illustrated Instructions" i.e. visual instructions customized to a user's needs. We identify desiderata unique to this task and formalize it through a suite of automatic and human evaluation metrics designed to measure the validity consistency and efficacy of the generations. We combine the power of large language models (LLMs) together with strong text-to-image generation diffusion models to propose a simple approach called StackedDiffusion which generates such illustrated instructions given text as input. The resulting model strongly outperforms baseline approaches and state-of-the-art multimodal LLMs; and in 30% of cases users even prefer it to human-generated articles. Most notably it enables various new and exciting applications far beyond what static articles on the web can provide such as personalized instructions complete with intermediate steps and pictures in response to a user's individual situation.

Yue Niu, Ramy E. Ali, Saurav Prakash, Salman Avestimehr

Data privacy is of great concern in cloud machine-learning service platforms when sensitive data are exposed to service providers. While private computing environments (e.g. secure enclaves) and cryptographic approaches (e.g. homomorphic encryption) provide strong privacy protection their computing performance still falls short compared to cloud GPUs. To achieve privacy protection with high computing performance we propose Delta a new private training and inference framework with comparable model performance as non-private centralized training. Delta features two asymmetric data flows: the main information-sensitive flow and the residual flow. The main part flows into a small model while the residuals are offloaded to a large model. Specifically Delta embeds the information-sensitive representations into a low-dimensional space while pushing the information-insensitive part into high-dimension residuals. To ensure privacy protection the low-dimensional information-sensitive part is secured and fed to a small model in a private environment. On the other hand the residual part is sent to fast cloud GPUs and processed by a large model. To further enhance privacy and reduce the communication cost Delta applies a random binary quantization technique along with a DP-based technique to the residuals before sharing them with the public platform. We theoretically show that Delta guarantees differential privacy in the public environment and greatly reduces the complexity in the private environment. We conduct empirical analyses on CIFAR-10 CIFAR-100 and ImageNet datasets and ResNet-18 and ResNet-34 showing that Delta achieves strong privacy protection fast training and inference without significantly compromising the model utility.

Jan-Niklas Dihlmann, Andreas Engelhardt, Hendrik Lensch

Advances in image diffusion models have recently led to notable improvements in the generation of high-quality images. In combination with Neural Radiance Fields (NeRFs) they enabled new opportunities in 3D generation. However most generative 3D approaches are object-centric and applying them to editing existing photorealistic scenes is not trivial. We propose SIGNeRF a novel approach for fast and controllable NeRF scene editing and scene-integrated object generation. A new generative update strategy ensures 3D consistency across the edited images without requiring iterative optimization. We find that depth-conditioned diffusion models inherently possess the capability to generate 3D consistent views by requesting a grid of images instead of single views. Based on these insights we introduce a multi-view reference sheet of modified images. Our method updates an image collection consistently based on the reference sheet and refines the original NeRF with the newly generated image set in one go. By exploiting the depth conditioning mechanism of the image diffusion model we gain fine control over the spatial location of the edit and enforce shape guidance by a selected region or an external mesh.

Zhiyuan Min, Yawei Luo, Wei Yang, Yuesong Wang, Yi Yang

Generalizable NeRF can directly synthesize novel views across new scenes eliminating the need for scene-specific retraining in vanilla NeRF. A critical enabling factor in these approaches is the extraction of a generalizable 3D representation by aggregating source-view features. In this paper we propose an Entangled View-Epipolar Information Aggregation method dubbed EVE-NeRF. Different from existing methods that consider cross-view and along-epipolar information independently EVE-NeRF conducts the view-epipolar feature aggregation in an entangled manner by injecting the scene-invariant appearance continuity and geometry consistency priors to the aggregation process. Our approach effectively mitigates the potential lack of inherent geometric and appearance constraint resulting from one-dimensional interactions thus further boosting the 3D representation generalizablity. EVE-NeRF attains state-of-the-art performance across various evaluation scenarios. Extensive experiments demonstate that compared to prevailing single-dimensional aggregation the entangled network excels in the accuracy of 3D scene geometry and appearance reconstruction. Our code is publicly available at https://github.com/tatakai1/EVENeRF.

Reuben Tan, Ximeng Sun, Ping Hu, Jui-hsien Wang, Hanieh Deilamsalehy, Bryan A. Plummer, Bryan Russell, Kate Saenko

Long video question answering is a challenging task that involves recognizing short-term activities and reasoning about their fine-grained relationships. State-of-the-art video Large Language Models (vLLMs) hold promise as a viable solution due to their demonstrated emergent capabilities on new tasks. However despite being trained on millions of short seconds-long videos vLLMs are unable to understand minutes-long videos and accurately answer questions about them. To address this limitation we propose a lightweight and self-supervised approach Key frame-conditioned long video-LLM (Koala) that introduces learnable spatiotemporal queries to adapt pretrained vLLMs for generalizing to longer videos. Our approach introduces two new tokenizers that condition on visual tokens computed from sparse video key frames for understanding short and long video moments. We train our proposed approach on HowTo100M and demonstrate its effectiveness on zero-shot long video understanding benchmarks where it outperforms state-of-the-art large models by 3 - 6% in absolute accuracy across all tasks. Surprisingly we also empirically show that our approach not only helps a pretrained vLLM to understand long videos but also improves its accuracy on short-term action recognition.

Boyi Li, Yue Wang, Jiageng Mao, Boris Ivanovic, Sushant Veer, Karen Leung, Marco Pavone

Adapting driving behavior to new environments customs and laws is a long-standing problem in autonomous driving precluding the widespread deployment of autonomous vehicles (AVs). In this paper we present LLaDA a simple yet powerful tool that enables human drivers and autonomous vehicles alike to drive everywhere by adapting their tasks and motion plans to traffic rules in new locations. LLaDA achieves this by leveraging the impressive zero-shot generalizability of large language models (LLMs) in interpreting the traffic rules in the local driver handbook. Through an extensive user study we show that LLaDA's instructions are useful in disambiguating in-the-wild unexpected situations. We also demonstrate LLaDA's ability to adapt AV motion planning policies in real-world datasets; LLaDA outperforms baseline planning approaches on all our metrics. Please check our website for more details: https://boyiliee.github.io/llada.

Lukas Haas, Michal Skreta, Silas Alberti, Chelsea Finn

Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made significant progress in geolocalization accuracy success in prior literature is constrained to narrow distributions of images of landmarks and performance has not generalized to unseen places. We present a new geolocalization system that combines semantic geocell creation multi-task contrastive pretraining and a novel loss function. Additionally our work is the first to perform retrieval over location clusters for guess refinements. We train two models for evaluations on street-level data and general-purpose image geolocalization; the first model PIGEON is trained on data from the game of GeoGuessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally. We also develop a bot and deploy PIGEON in a blind experiment against humans ranking in the top 0.01% of players. We further challenge one of the world's foremost professional GeoGuessr players to a series of six matches with millions of viewers winning all six games. Our second model PIGEOTTO differs in that it is trained on a dataset of images from Flickr and Wikipedia achieving state-of-the-art results on a wide range of image geolocalization benchmarks outperforming the previous SOTA by up to 7.7 percentage points on the city accuracy level and up to 38.8 percentage points on the country level. Our findings suggest that PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places and that our approach can pave the way for highly accurate planet-scale image geolocalization systems. Our code is available on GitHub.

Yuan Dong, Qi Zuo, Xiaodong Gu, Weihao Yuan, Zhengyi Zhao, Zilong Dong, Liefeng Bo, Qixing Huang

State-of-the-art man-made shape generative models usually adopt established generative models under a suitable implicit shape representation. A common theme is to perform distribution alignment which does not explicitly model important shape priors. As a result many synthetic shapes are not connected. Other synthetic shapes present problems of physical stability and geometric feasibility. This paper introduces a novel latent diffusion shape-generative model regularized by a quality checker that outputs a score of a latent code. The scoring function employs a learned function that provides a geometric feasibility score and a deterministic procedure to quantify a physical stability score. The key to our approach is a new diffusion procedure that combines the discrete empirical data distribution and a continuous distribution induced by the quality checker. We introduce a principled approach to determine the tradeoff parameters for learning the denoising network at different noise levels. Experimental results show that our approach outperforms state-of-the-art shape generations quantitatively and qualitatively on ShapeNet-v2.

Bingfeng Zhang, Siyue Yu, Yunchao Wei, Yao Zhao, Jimin Xiao

Weakly supervised semantic segmentation has witnessed great achievements with image-level labels. Several recent approaches use the CLIP model to generate pseudo labels for training an individual segmentation model while there is no attempt to apply the CLIP model as the backbone to directly segment objects with image-level labels. In this paper we propose WeCLIP a CLIP-based single-stage pipeline for weakly supervised semantic segmentation. Specifically the frozen CLIP model is applied as the backbone for semantic feature extraction and a new decoder is designed to interpret extracted semantic features for final prediction. Meanwhile we utilize the above frozen backbone to generate pseudo labels for training the decoder. Such labels cannot be optimized during training. We then propose a refinement module (RFM) to rectify them dynamically. Our architecture enforces the proposed decoder and RFM to benefit from each other to boost the final performance. Extensive experiments show that our approach significantly outperforms other approaches with less training cost. Additionally our WeCLIP also obtains promising results for fully supervised settings. The code is available at https://github.com/zbf1991/WeCLIP.

Wei Dong, Xing Zhang, Bihui Chen, Dawei Yan, Zhijun Lin, Qingsen Yan, Peng Wang, Yang Yang

Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently there is a lack of focus on guiding this delicate trade-off. In this study we approach the problem from the perspective of Singular Value Decomposition (SVD) of pre-trained parameter matrices providing insights into the tuning dynamics of existing methods. Building upon this understanding we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pre-trained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at https://github.com/zstarN70/RLRR.git.

Jonas Herzog

Few-shot segmentation performance declines substantially when facing images from a domain different than the training domain effectively limiting real-world use cases. To alleviate this recently cross-domain few-shot segmentation (CD-FSS) has emerged. Works that address this task mainly attempted to learn segmentation on a source domain in a manner that generalizes across domains. Surprisingly we can outperform these approaches while eliminating the training stage and removing their main segmentation network. We show test-time task-adaption is the key for successful CD-FSS instead. Task-adaption is achieved by appending small networks to the feature pyramid of a conventionally classification-pretrained backbone. To avoid overfitting to the few labeled samples in supervised fine-tuning consistency across augmented views of input images serves as guidance while learning the parameters of the attached layers. Despite our self-restriction not to use any images other than the few labeled samples at test time we achieve new state-of-the-art performance in CD-FSS evidencing the need to rethink approaches for the task. Code is available at https://github.com/Vision-Kek/ABCDFSS.

Shangqian Gao, Junyi Li, Zeyu Zhang, Yanfu Zhang, Weidong Cai, Heng Huang

Neural network pruning particularly channel pruning is a widely used technique for compressing deep learning models to enable their deployment on edge devices with limited resources. Typically redundant weights or structures are removed to achieve the target resource budget. Although data-driven pruning approaches have proven to be more effective they cannot be directly applied to federated learning (FL) which has emerged as a popular technique in edge computing applications because of distributed and confidential datasets. In response to this challenge we design a new network pruning method for FL. We propose device-wise sub-networks for each device assuming that the data distribution is similar within each device. These sub-networks are generated through sub-network embeddings and a hypernetwork. To further minimize memory usage and communication costs we permanently prune the full model to remove weights that are not useful for all devices. During the FL process we simultaneously train the device-wise sub-networks and the base sub-network to facilitate the pruning process. We then finetune the pruned model with device-wise sub-networks to regain performance. Moreover we provided the theoretical guarantee of convergence for our method. Our method achieves better performance and resource trade-off than other well-established network pruning baselines as demonstrated through extensive experiments on CIFAR-10 CIFAR-100 and TinyImageNet.

Mengwei Ren, Wei Xiong, Jae Shin Yoon, Zhixin Shu, Jianming Zhang, HyunJoon Jung, Guido Gerig, He Zhang

Portrait harmonization aims to composite a subject into a new background adjusting its lighting and color to ensure harmony with the background scene. Existing harmonization techniques often only focus on adjusting the global color and brightness of the foreground and ignore crucial illumination cues from the background such as apparent lighting direction leading to unrealistic compositions. We introduce Relightful Harmonization a lighting-aware diffusion model designed to seamlessly harmonize sophisticated lighting effect for the foreground portrait using any background image. Our approach unfolds in three stages. First we introduce a lighting representation module that allows our diffusion model to encode lighting information from target image background. Second we introduce an alignment network that aligns lighting features learned from image background with lighting features learned from panorama environment maps which is a complete representation for scene illumination. Last to further boost the photorealism of the proposed method we introduce a novel data simulation pipeline that generates synthetic training pairs from a diverse range of natural images which are used to refine the model. Our method outperforms existing benchmarks in visual fidelity and lighting coherence showing superior generalization in real-world testing scenarios highlighting its versatility and practicality.

Zizhang Li, Dor Litvak, Ruining Li, Yunzhi Zhang, Tomas Jakab, Christian Rupprecht, Shangzhe Wu, Andrea Vedaldi, Jiajun Wu

Learning 3D models of all animals in nature requires massively scaling up existing solutions. With this ultimate goal in mind we develop 3D-Fauna an approach that learns a pan-category deformable 3D animal model for more than 100 animal species jointly. One crucial bottleneck of modeling animals is the limited availability of training data which we overcome by learning our model from 2D Internet images. We show that prior approaches which are category-specific fail to generalize to rare species with limited training images. We address this challenge by introducing the Semantic Bank of Skinned Models (SBSM) which automatically discovers a small set of base animal shapes by combining geometric inductive priors with semantic knowledge implicitly captured by an off-the-shelf self-supervised feature extractor. To train such a model we also contribute a new large-scale dataset of diverse animal species. At inference time given a single image of any quadruped animal our model reconstructs an articulated 3D mesh in a feed-forward manner in seconds.

Liqiong Wang, Jinyu Yang, Yanfu Zhang, Fangyi Wang, Feng Zheng

Concealed Object Detection (COD) aims to identify objects visually embedded in their background. Existing COD datasets and methods predominantly focus on animals or humans ignoring the agricultural domain which often contains numerous small and concealed crops with severe occlusions. In this paper we introduce Concealed Crop Detection (CCD) which extends classic COD to agricultural domains. Experimental study shows that unimodal data provides insufficient information for CCD. To address this gap we first collect a large-scale RGB-D dataset ACOD-12K containing high-resolution crop images and depth maps. Then we propose a foundational framework named Recurrent Iterative Segmentation Network (RISNet). To tackle the challenge of dense objects we employ multi-scale receptive fields to capture objects of varying sizes thus enhancing the detection performance for dense objects. By fusing depth features our method can acquire spatial information about concealed objects to mitigate disturbances caused by intricate backgrounds and occlusions. Furthermore our model adopts a multi-stage iterative approach using predictions from each stage as gate attention to reinforce position information thereby improving the detection accuracy for small objects. Extensive experimental results demonstrate that our RISNet achieves new state-of-the-art performance on both newly proposed CCD and classic COD tasks. All resources will be available at https://github.com/Kki2Eve/RISNet.

Jinglin Xu, Guohao Zhao, Sibo Yin, Wenhao Zhou, Yuxin Peng

Fine-grained action analysis in multi-person sports is complex due to athletes' quick movements and intense physical confrontations which result in severe visual obstructions in most scenes. In addition accessible multi-person sports video datasets lack fine-grained action annotations in both space and time adding to the difficulty in fine-grained action analysis. To this end we construct a new multi-person basketball sports video dataset named FineSports which contains fine-grained semantic and spatial-temporal annotations on 10000 NBA game videos covering 52 fine-grained action types 16000 action instances and 123000 spatial-temporal bounding boxes. We also propose a new prompt-driven spatial-temporal action location approach called PoSTAL composed of a prompt-driven target action encoder (PTA) and an action tube-specific detector (ATD) to directly generate target action tubes with fine-grained action types without any off-line proposal generation. Extensive experiments on the FineSports dataset demonstrate that PoSTAL outperforms state-of-the-art methods. Data and code are available at https://github.com/PKU-ICST-MIPL/FineSports_CVPR2024.

Shuaibo Li, Wei Ma, Jianwei Guo, Shibiao Xu, Benchong Li, Xiaopeng Zhang

We present UnionFormer a novel framework that integrates tampering clues across three views by unified learning for image manipulation detection and localization. Specifically we construct a BSFI-Net to extract tampering features from RGB and noise views achieving enhanced responsiveness to boundary artifacts while modulating spatial consistency at different scales. Additionally to explore the inconsistency between objects as a new view of clues we combine object consistency modeling with tampering detection and localization into a three-task unified learning process allowing them to promote and improve mutually. Therefore we acquire a unified manipulation discriminative representation under multi-scale supervision that consolidates information from three views. This integration facilitates highly effective concurrent detection and localization of tampering. We perform extensive experiments on diverse datasets and the results show that the proposed approach outperforms state-of-the-art methods in tampering detection and localization.

Jaewoo Jeong, Daehee Park, Kuk-Jin Yoon

Human pose forecasting garners attention for its diverse applications. However challenges in modeling the multi-modal nature of human motion and intricate interactions among agents persist particularly with longer timescales and more agents. In this paper we propose an interaction-aware trajectory-conditioned long-term multi-agent human pose forecasting model utilizing a coarse-to-fine prediction approach: multi-modal global trajectories are initially forecasted followed by respective local pose forecasts conditioned on each mode. In doing so our Trajectory2Pose model introduces a graph-based agent-wise interaction module for a reciprocal forecast of local motion-conditioned global trajectory and trajectory-conditioned local pose. Our model effectively handles the multi-modality of human motion and the complexity of long-term multi-agent interactions improving performance in complex environments. Furthermore we address the lack of long-term (6s+) multi-agent (5+) datasets by constructing a new dataset from real-world images and 2D annotations enabling a comprehensive evaluation of our proposed model. State-of-the-art prediction performance on both complex and simpler datasets confirms the generalized effectiveness of our method. The code is available at https://github.com/Jaewoo97/T2P.

Xingqian Xu, Jiayi Guo, Zhangyang Wang, Gao Huang, Irfan Essa, Humphrey Shi

Text-to-image (T2I) research has grown explosively in the past year owing to the large-scale pre-trained diffusion models and many emerging personalization and editing approaches. Yet one pain point persists: the text prompt engineering and searching high-quality text prompts for customized results is more art than science. Moreover as commonly argued: "an image is worth a thousand words" - the attempt to describe a desired image with texts often ends up being ambiguous and cannot comprehensively cover delicate visual details hence necessitating more additional controls from the visual domain. In this paper we take a bold step forward: taking "Text" out of a pretrained T2I diffusion model to reduce the burdensome prompt engineering efforts for users. Our proposed framework Prompt-Free Diffusion relies on only visual inputs to generate new images: it takes a reference image as "context" an optional image structural conditioning and an initial noise with absolutely no text prompt. The core architecture behind the scene is Semantic Context Encoder (SeeCoder) substituting the commonly used CLIP-based or LLM-based text encoder. The reusability of SeeCoder also makes it a convenient drop-in component: one can also pre-train a SeeCoder in one T2I model and reuse it for another. Through extensive experiments Prompt-Free Diffusion is experimentally found to (i) outperform prior exemplar-based image synthesis approaches; (ii) perform on par with state-of-the-art T2I models using prompts following the best practice; and (iii) be naturally extensible to other downstream applications such as anime figure generation and virtual try-on with promising quality. Our code and models will be open-sourced.