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Andreas Engelhardt, Amit Raj, Mark Boss, Yunzhi Zhang, Abhishek Kar, Yuanzhen Li, Deqing Sun, Ricardo Martin Brualla, Jonathan T. Barron, Hendrik P. A. Lensch 等

We present SHINOBI an end-to-end framework for the reconstruction of shape material and illumination from object images captured with varying lighting pose and background. Inverse rendering of an object based on unconstrained image collections is a long-standing challenge in computer vision and graphics and requires a joint optimization over shape radiance and pose. We show that an implicit shape representation based on a multi-resolution hash encoding enables faster and robust shape reconstruction with joint camera alignment optimization that outperforms prior work. Further to enable the editing of illumination and object reflectance (i.e. material) we jointly optimize BRDF and illumination together with the object's shape. Our method is class-agnostic and works on in-the-wild image collections of objects to produce relightable 3D assets for several use cases such as AR/VR movies games etc.

Tai Wang, Xiaohan Mao, Chenming Zhu, Runsen Xu, Ruiyuan Lyu, Peisen Li, Xiao Chen, Wenwei Zhang, Kai Chen, Tianfan Xue 等

In the realm of computer vision and robotics embodied agents are expected to explore their environment and carry out human instructions. This necessitates the ability to fully understand 3D scenes given their first-person observations and contextualize them into language for interaction. However traditional research focuses more on scene-level input and output setups from a global view. To address the gap we introduce EmbodiedScan a multi-modal ego-centric 3D perception dataset and benchmark for holistic 3D scene understanding. It encompasses over 5k scans encapsulating 1M ego-centric RGB-D views 1M language prompts 160k 3D-oriented boxes spanning over 760 categories some of which partially align with LVIS and dense semantic occupancy with 80 common categories. Building upon this database we introduce a baseline framework named Embodied Perceptron. It is capable of processing an arbitrary number of multi-modal inputs and demonstrates remarkable 3D perception capabilities both within the two series of benchmarks we set up i.e. fundamental 3D perception tasks and language-grounded tasks and in the wild.

Nikola Zubic, Mathias Gehrig, Davide Scaramuzza

Today state-of-the-art deep neural networks that process event-camera data first convert a temporal window of events into dense grid-like input representations. As such they exhibit poor generalizability when deployed at higher inference frequencies (i.e. smaller temporal windows) than the ones they were trained on. We address this challenge by introducing state-space models (SSMs) with learnable timescale parameters to event-based vision. This design adapts to varying frequencies without the need to retrain the network at different frequencies. Additionally we investigate two strategies to counteract aliasing effects when deploying the model at higher frequencies. We comprehensively evaluate our approach against existing methods based on RNN and Transformer architectures across various benchmarks including Gen1 and 1 Mpx event camera datasets. Our results demonstrate that SSM-based models train 33% faster and also exhibit minimal performance degradation when tested at higher frequencies than the training input. Traditional RNN and Transformer models exhibit performance drops of more than 20 mAP with SSMs having a drop of 3.31 mAP highlighting the effectiveness of SSMs in event-based vision tasks.

Ping Chen, Xingpeng Zhang, Chengtao Zhou, Dichao Fan, Peng Tu, Le Zhang, Yanlin Qian

Convolution neural network is successful in pervasive vision tasks including label distribution learning which usually takes the form of learning an injection from the non-linear visual features to the well-defined labels. However how the discrepancy between features is mapped to the label discrepancy is ambient and its correctness is not guaranteed.To address these problems we study the mathematical connection between feature and its label presenting a general and simple framework for label distribution learning. We propose a so-called Triangular Distribution Transform (TDT) to build an injective function between feature and label guaranteeing that any symmetric feature discrepancy linearly reflects the difference between labels. The proposed TDT can be used as a plug-in in mainstream backbone networks to address different label distribution learning tasks. Experiments on Facial Age Recognition Illumination Chromaticity Estimation and Aesthetics assessment show that TDT achieves on-par or better results than the prior arts.

Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao

The rapid advancement of deep learning models is often attributed to their ability to leverage massive training data. In contrast such privilege has not yet fully benefited 3D deep learning mainly due to the limited availability of large-scale 3D datasets. Merging multiple available data sources and letting them collaboratively train a single model is a potential solution. However due to the large domain gap between 3D point cloud datasets such mixed supervision could adversely affect the model's performance and lead to degenerated performance (i.e. negative transfer) compared to single-dataset training. In view of this challenge we introduce Point Prompt Training (PPT) a novel framework for multi-dataset synergistic learning in the context of 3D representation learning that supports multiple pre-training paradigms. Based on this framework we propose Prompt-driven Normalization which adapts the model to different datasets with domain-specific prompts and Language-guided Categorical Alignment that decently unifies the multiple-dataset label spaces by leveraging the relationship between label text. Extensive experiments verify that PPT can overcome the negative transfer associated with synergistic learning and produce generalizable representations. Notably it achieves state-of-the-art performance on each dataset using a single weight-shared model with supervised multi-dataset training. Moreover when served as a pre-training framework it outperforms other pre-training approaches regarding representation quality and attains remarkable state-of-the-art performance across over ten diverse downstream tasks spanning both indoor and outdoor 3D scenarios.

Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian

Recent significant advances in text-to-image models unlock the possibility of training vision systems using synthetic images potentially overcoming the difficulty of collecting curated data at scale. It is unclear however how these models behave at scale as more synthetic data is added to the training set. In this paper we study the scaling laws of synthetic images generated by state of the art text-to-image models for the training of supervised models: image classifiers with label supervision and CLIP with language supervision. We identify several factors including text prompts classifier-free guidance scale and types of text-to-image models that significantly affect scaling behavior. After tuning these factors we observe that synthetic images demonstrate a scaling trend similar to but slightly less effective than real images in CLIP training while they significantly underperform in scaling when training supervised image classifiers. Our analysis indicates that the main reason for this underperformance is the inability of off-the-shelf text-to-image models to generate certain concepts a limitation that significantly impairs the training of image classifiers. Our findings also suggest that scaling synthetic data can be particularly effective in scenarios such as: (1) when there is a limited supply of real images for a supervised problem (e.g. fewer than 0.5 million images in ImageNet) (2) when the evaluation dataset diverges significantly from the training data indicating the out-of-distribution scenario or (3) when synthetic data is used in conjunction with real images as demonstrated in the training of CLIP models.

Zhiyuan Yan, Yuhao Luo, Siwei Lyu, Qingshan Liu, Baoyuan Wu

Deepfake detection faces a critical generalization hurdle with performance deteriorating when there is a mismatch between the distributions of training and testing data. A broadly received explanation is the tendency of these detectors to be overfitted to forgery-specific artifacts rather than learning features that are widely applicable across various forgeries. To address this issue we propose a simple yet effective detector called LSDA (\underline L atent \underline S pace \underline D ata \underline A ugmentation) which is based on a heuristic idea: representations with a wider variety of forgeries should be able to learn a more generalizable decision boundary thereby mitigating the overfitting of method-specific features (see Fig. 1). Following this idea we propose to enlarge the forgery space by constructing and simulating variations within and across forgery features in the latent space. This approach encompasses the acquisition of enriched domain-specific features and the facilitation of smoother transitions between different forgery types effectively bridging domain gaps. Our approach culminates in refining a binary classifier that leverages the distilled knowledge from the enhanced features striving for a generalizable deepfake detector. Comprehensive experiments show that our proposed method is surprisingly effective and transcends state-of-the-art detectors across several widely used benchmarks.

Yi Xie, Yihong Lin, Wenjie Cai, Xuemiao Xu, Huaidong Zhang, Yong Du, Shengfeng He

Existing methods for asymmetric image retrieval employ a rigid pairwise similarity constraint between the query network and the larger gallery network. However these one-to-one constraint approaches often fail to maintain retrieval order consistency especially when the query network has limited representational capacity. To overcome this problem we introduce the Decoupled Differential Distillation (D3still) framework. This framework shifts from absolute one-to-one supervision to optimizing the relational differences in pairwise similarities produced by the query and gallery networks thereby preserving a consistent retrieval order across both networks. Our method involves computing a pairwise similarity differential matrix within the gallery domain which is then decomposed into three components: feature representation knowledge inconsistent pairwise similarity differential knowledge and consistent pairwise similarity differential knowledge. This strategic decomposition aligns the retrieval ranking of the query network with the gallery network effectively. Extensive experiments on various benchmark datasets reveal that D3still surpasses state-of-the-art methods in asymmetric image retrieval. Code is available at https://github.com/SCY-X/D3still.

Xizi Wang, Feng Cheng, Gedas Bertasius

Active Speaker Detection (ASD) aims to identify who is speaking in each frame of a video. Solving ASD involves using audio and visual information in two complementary contexts: long-term intra-speaker context models the temporal dependencies of the same speaker while short-term inter-speaker context models the interactions of speakers in the same scene. Motivated by these observations we propose LoCoNet a simple but effective Long-Short Context Network that leverages Long-term Intra-speaker Modeling (LIM) and Short-term Inter-speaker Modeling (SIM) in an interleaved manner. LIM employs self-attention for long-range temporal dependencies modeling and cross-attention for audio-visual interactions modeling. SIM incorporates convolutional blocks that capture local patterns for short-term inter-speaker context. Experiments show that LoCoNet achieves state-of-the-art performance on multiple datasets with 95.2% (+0.3%) mAP on AVA-ActiveSpeaker 97.2% (+2.7%) mAP on Talkies and 68.4% (+7.7%) mAP on Ego4D. Moreover in challenging cases where multiple speakers are present LoCoNet outperforms previous state-of-the-art methods by 3.0% mAP on AVA-ActiveSpeaker. The code is available at https://github.com/SJTUwxz/LoCoNet_ASD.

Junsheng Zhou, Weiqi Zhang, Baorui Ma, Kanle Shi, Yu-Shen Liu, Zhizhong Han

Diffusion models have shown remarkable results for image generation editing and inpainting. Recent works explore diffusion models for 3D shape generation with neural implicit functions i.e. signed distance function and occupancy function. However they are limited to shapes with closed surfaces which prevents them from generating diverse 3D real-world contents containing open surfaces. In this work we present UDiFF a 3D diffusion model for unsigned distance fields (UDFs) which is capable to generate textured 3D shapes with open surfaces from text conditions or unconditionally. Our key idea is to generate UDFs in spatial-frequency domain with an optimal wavelet transformation which produces a compact representation space for UDF generation. Specifically instead of selecting an appropriate wavelet transformation which requires expensive manual efforts and still leads to large information loss we propose a data-driven approach to learn the optimal wavelet transformation for UDFs. We evaluate UDiFF to show our advantages by numerical and visual comparisons with the latest methods on widely used benchmarks. Page: https://weiqi-zhang.github.io/UDiFF.

Ronghui Li, YuXiang Zhang, Yachao Zhang, Hongwen Zhang, Jie Guo, Yan Zhang, Yebin Liu, Xiu Li

We propose Lodge a network capable of generating extremely long dance sequences conditioned on given music. We design Lodge as a two-stage coarse to fine diffusion architecture and propose the characteristic dance primitives that possess significant expressiveness as intermediate representations between two diffusion models. The first stage is global diffusion which focuses on comprehending the coarse-level music-dance correlation and production characteristic dance primitives. In contrast the second-stage is the local diffusion which parallelly generates detailed motion sequences under the guidance of the dance primitives and choreographic rules. In addition we propose a Foot Refine Block to optimize the contact between the feet and the ground enhancing the physical realism of the motion. Code available at https://li-ronghui.github.io/lodge

Ziwen Li, Feng Zhang, Meng Cao, Jinpu Zhang, Yuanjie Shao, Yuehuan Wang, Nong Sang

Most of the previous exposure correction methods learn dense pixel-wise transformations to achieve promising results but consume huge computational resources. Recently Learnable 3D lookup tables (3D LUTs) have demonstrated impressive performance and efficiency for image enhancement. However these methods can only perform global transformations and fail to finely manipulate local regions. Moreover they uniformly downsample the input image which loses the rich color information and limits the learning of color transformation capabilities. In this paper we present a collaborative transformation framework (CoTF) for real-time exposure correction which integrates global transformation with pixel-wise transformations in an efficient manner. Specifically the global transformation adjusts the overall appearance using image-adaptive 3D LUTs to provide decent global contrast and sharp details while the pixel transformation compensates for local context. Then a relation-aware modulation module is designed to combine these two components effectively. In addition we propose an adaptive sampling strategy to preserve more color information by predicting the sampling intervals thus providing higher quality input data for the learning of 3D LUTs. Extensive experiments demonstrate that our method can process high-resolution images in real-time on GPUs while achieving comparable performance against current state-of-the-art methods. The code is available at https://github.com/HUST-IAL/CoTF.

Kiana Ehsani, Tanmay Gupta, Rose Hendrix, Jordi Salvador, Luca Weihs, Kuo-Hao Zeng, Kunal Pratap Singh, Yejin Kim, Winson Han, Alvaro Herrasti 等

Reinforcement learning (RL) with dense rewards and imitation learning (IL) with human-generated trajectories are the most widely used approaches for training modern embodied agents. RL requires extensive reward shaping and auxiliary losses and is often too slow and ineffective for long-horizon tasks. While IL with human supervision is effective collecting human trajectories at scale is extremely expensive. In this work we show that imitating shortest-path planners in simulation produces agents that given a language instruction can proficiently navigate explore and manipulate objects in both simulation and in the real world using only RGB sensors (no depth map or GPS coordinates). This surprising result is enabled by our end-to-end transformer-based SPOC architecture powerful visual encoders paired with extensive image augmentation and the dramatic scale and diversity of our training data: millions of frames of shortest-path-expert trajectories collected inside approximately 200000 procedurally generated houses containing 40000 unique 3D assets. Our models data training code and newly proposed 10-task benchmarking suite CHORES are available at https://spoc-robot.github.io/.

Chenming Shang, Shiji Zhou, Hengyuan Zhang, Xinzhe Ni, Yujiu Yang, Yuwang Wang

Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the concepts to make predictions enhancing the transparency of the decision-making process. Multimodal pre-trained models can match visual representations with textual concept embeddings allowing for obtaining the interpretable concept bottleneck without the expertise concept annotations. Recent research has focused on the concept bank establishment and the high-quality concept selection. However it is challenging to construct a comprehensive concept bank through humans or large language models which severely limits the performance of CBMs. In this work we propose the Incremental Residual Concept Bottleneck Model (Res-CBM) to address the challenge of concept completeness. Specifically the residual concept bottleneck model employs a set of optimizable vectors to complete missing concepts then the incremental concept discovery module converts the complemented vectors with unclear meanings into potential concepts in the candidate concept bank. Our approach can be applied to any user-defined concept bank as a post-hoc processing method to enhance the performance of any CBMs. Furthermore to measure the descriptive efficiency of CBMs the Concept Utilization Efficiency (CUE) metric is proposed. Experiments show that the Res-CBM outperforms the current state-of-the-art methods in terms of both accuracy and efficiency and achieves comparable performance to black-box models across multiple datasets.

Hanoona Rasheed, Muhammad Maaz, Sahal Shaji, Abdelrahman Shaker, Salman Khan, Hisham Cholakkal, Rao M. Anwer, Eric Xing, Ming-Hsuan Yang, Fahad S. Khan

Large Multimodal Models (LMMs) extend Large Language Models to the vision domain. Initial LMMs used holistic images and text prompts to generate ungrounded textual responses. Recently region-level LMMs have been used to generate visually grounded responses. However they are limited to only referring to a single object category at a time require users to specify the regions or cannot offer dense pixel-wise object grounding. In this work we present Grounding LMM (GLaMM) the first model that can generate natural language responses seamlessly intertwined with corresponding object segmentation masks. GLaMM not only grounds objects appearing in the conversations but is flexible enough to accept both textual and optional visual prompts (region of interest) as input. This empowers users to interact with the model at various levels of granularity both in textual and visual domains. Due to the lack of standard benchmarks for the novel setting of visually Grounded Conversation Generation (GCG) we introduce a comprehensive evaluation protocol with our curated grounded conversations. Our proposed GCG task requires densely grounded concepts in natural scenes at a large-scale. To this end we propose a densely annotated Grounding-anything Dataset (GranD) using our proposed automated annotation pipeline that encompasses 7.5M unique concepts grounded in a total of 810M regions available with segmentation masks. Besides GCG GLaMM also performs effectively on several downstream tasks e.g. referring expression segmentation image and region-level captioning and vision-language conversations.

Xiao Zhang, David Yunis, Michael Maire

We present an approach for analyzing grouping information contained within a neural network's activations permitting extraction of spatial layout and semantic segmentation from the behavior of large pre-trained vision models. Unlike prior work our method conducts a wholistic analysis of a network's activation state leveraging features from all layers and obviating the need to guess which part of the model contains relevant information. Motivated by classic spectral clustering we formulate this analysis in terms of an optimization objective involving a set of affinity matrices each formed by comparing features within a different layer. Solving this optimization problem using gradient descent allows our technique to scale from single images to dataset-level analysis including in the latter both intra- and inter-image relationships. Analyzing a pre-trained generative transformer provides insight into the computational strategy learned by such models. Equating affinity with key-query similarity across attention layers yields eigenvectors encoding scene spatial layout whereas defining affinity by value vector similarity yields eigenvectors encoding object identity. This result suggests that key and query vectors coordinate attentional information flow according to spatial proximity (a `where' pathway) while value vectors refine a semantic category representation (a `what' pathway).

Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Dinh Phung

Federated Class-Incremental Learning (FCIL) is an underexplored yet pivotal issue involving the dynamic addition of new classes in the context of federated learning. In this field Data-Free Knowledge Transfer (DFKT) plays a crucial role in addressing catastrophic forgetting and data privacy problems. However prior approaches lack the crucial synergy between DFKT and the model training phases causing DFKT to encounter difficulties in generating high-quality data from a non-anchored latent space of the old task model. In this paper we introduce LANDER (Label Text Centered Data-Free Knowledge Transfer) to address this issue by utilizing label text embeddings (LTE) produced by pretrained language models. Specifically during the model training phase our approach treats LTE as anchor points and constrains the feature embeddings of corresponding training samples around them enriching the surrounding area with more meaningful information. In the DFKT phase by using these LTE anchors LANDER can synthesize more meaningful samples thereby effectively addressing the forgetting problem. Additionally instead of tightly constraining embeddings toward the anchor the Bounding Loss is introduced to encourage sample embeddings to remain flexible within a defined radius. This approach preserves the natural differences in sample embeddings and mitigates the embedding overlap caused by heterogeneous federated settings. Extensive experiments conducted on CIFAR100 Tiny-ImageNet and ImageNet demonstrate that LANDER significantly outperforms previous methods and achieves state-of-the-art performance in FCIL. The code is available at https://github.com/tmtuan1307/lander.

Yiran Song, Qianyu Zhou, Xiangtai Li, Deng-Ping Fan, Xuequan Lu, Lizhuang Ma

In this paper we address the challenge of image resolution variation for the Segment Anything Model (SAM). SAM known for its zero-shot generalizability exhibits a performance degradation when faced with datasets with varying image sizes. Previous approaches tend to resize the image to a fixed size or adopt structure modifications hindering the preservation of SAM's rich prior knowledge. Besides such task-specific tuning necessitates a complete retraining of the model which is cost-expensive and unacceptable for deployment in the downstream tasks. In this paper we reformulate this challenge as a length extrapolation problem where token sequence length varies while maintaining a consistent patch size for images with different sizes. To this end we propose a Scalable Bias-Mode Attention Mask (BA-SAM) to enhance SAM's adaptability to varying image resolutions while eliminating the need for structure modifications. Firstly we introduce a new scaling factor to ensure consistent magnitude in the attention layer's dot product values when the token sequence length changes. Secondly we present a bias-mode attention mask that allows each token to prioritize neighboring information mitigating the impact of untrained distant information. Our BA-SAM demonstrates efficacy in two scenarios: zero-shot and fine-tuning. Extensive evaluation of diverse datasets including DIS5K DUTS ISIC COD10K and COCO reveals its ability to significantly mitigate performance degradation in the zero-shot setting and achieve state-of-the-art performance with minimal fine-tuning. Furthermore we propose a generalized model and benchmark showcasing BA-SAM's generalizability across all four datasets simultaneously.

Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, Hao Dong

Robot manipulation relies on accurately predicting contact points and end-effector directions to ensure successful operation. However learning-based robot manipulation trained on a limited category within a simulator often struggles to achieve generalizability especially when confronted with extensive categories. Therefore we introduce an innovative approach for robot manipulation that leverages the robust reasoning capabilities of Multimodal Large Language Models (MLLMs) to enhance the stability and generalization of manipulation. By fine-tuning the injected adapters we preserve the inherent common sense and reasoning ability of the MLLMs while equipping them with the ability for manipulation. The fundamental insight lies in the introduced fine-tuning paradigm encompassing object category understanding affordance prior reasoning and object-centric pose prediction to stimulate the reasoning ability of MLLM in manipulation. During inference our approach utilizes an RGB image and text prompt to predict the end effector's pose in chain of thoughts. After the initial contact is established an active impedance adaptation policy is introduced to plan the upcoming waypoints in a closed-loop manner. Moreover in real world we design a test-time adaptation (TTA) strategy for manipulation to enable the model better adapt to the current real-world scene configuration. Experiments in simulator and real-world show the promising performance of ManipLLM. More details and demonstrations can be found at https://sites.google.com/view/manipllm.

Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, Hanwang Zhang

Score distillation sampling (SDS) and its variants have greatly boosted the development of text-to-3D generation but are vulnerable to geometry collapse and poor textures yet. To solve this issue we first deeply analyze the SDS and find that its distillation sampling process indeed corresponds to the trajectory sampling of a stochastic differential equation (SDE): SDS samples along an SDE trajectory to yield a less noisy sample which then serves as a guidance to optimize a 3D model. However the randomness in SDE sampling often leads to a diverse and unpredictable sample which is not always less noisy and thus is not a consistently correct guidance explaining the vulnerability of SDS. Since for any SDE there always exists an ordinary differential equation (ODE) whose trajectory sampling can deterministically and consistently converge to the desired target point as the SDE we propose a novel and effective "Consistent3D" method that explores the ODE deterministic sampling prior for text-to-3D generation. Specifically at each training iteration given a rendered image by a 3D model we first estimate its desired 3D score function by a pre-trained 2D diffusion model and build an ODE for trajectory sampling. Next we design a consistency distillation sampling loss which samples along the ODE trajectory to generate two adjacent samples and uses the less noisy sample to guide another more noisy one for distilling the deterministic prior into the 3D model. Experimental results show the efficacy of our Consistent3D in generating high-fidelity and diverse 3D objects and large-scale scenes as shown in Fig. 1. The codes are available at https://github.com/sail-sg/Consistent3D.