mtybilly/Instruct2DS huggingface dataset:
FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models
Authors: Justus Thies, Angela Dai, Matthias Nießner Shivangi Aneja
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce FaceTalk a novel generative approach designed for synthesizing high-fidelity 3D motion sequences of talking human heads from input audio signal. To capture the expressive detailed nature of human heads including hair ears and finer-scale eye movements we propose to couple speech signal with the latent space of neural parametric head models to create high-fidelity temporally coherent motion sequences. We propose a new latent diffusion model for this task operating in the expression space of neural parametric head models to synthesize audio-driven realistic head sequences. In the absence of a dataset with corresponding NPHM expressions to audio we optimize for these correspondences to produce a dataset of temporally-optimized NPHM expressions fit to audio-video recordings of people talking. To the best of our knowledge this is the first work to propose a generative approach for realistic and high-quality motion synthesis of volumetric human heads representing a significant advancement in the field of audio-driven 3D animation. Notably our approach stands out in its ability to generate plausible motion sequences that can produce high-fidelity head animation coupled with the NPHM shape space. Our experimental results substantiate the effectiveness of FaceTalk consistently achieving superior and visually natural motion encompassing diverse facial expressions and styles outperforming existing methods by 75% in perceptual user study evaluation
Mip-Splatting: Alias-free 3D Gaussian Splatting
Authors: Anpei Chen, Binbin Huang, Torsten Sattler, Andreas Geiger Zehao Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently 3D Gaussian Splatting has demonstrated impressive novel view synthesis results reaching high fidelity and efficiency. However strong artifacts can be observed when changing the sampling rate e.g. by changing focal length or camera distance. We find that the source for this phenomenon can be attributed to the lack of 3D frequency constraints and the usage of a 2D dilation filter. To address this problem we introduce a 3D smoothing filter to constrains the size of the 3D Gaussian primitives based on the maximal sampling frequency induced by the input views. It eliminates high-frequency artifacts when zooming in. Moreover replacing 2D dilation with a 2D Mip filter which simulates a 2D box filter effectively mitigates aliasing and dilation issues. Our evaluation including scenarios such a training on single-scale images and testing on multiple scales validates the effectiveness of our approach.
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution
Authors: Juncheng Li, Yingqian Wang, Qingyong Hu, Yulan Guo Longguang Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The difficulty of acquiring high-resolution (HR) and low-resolution (LR) image pairs in real scenarios limits the performance of existing learning-based image super-resolution (SR) methods in the real world. To conduct training on real-world unpaired data current methods focus on synthesizing pseudo LR images to associate unpaired images. However the realness and diversity of pseudo LR images are vulnerable due to the large image space. In this paper we circumvent the difficulty of image generation and propose an alternative to build the connection between unpaired images in a compact proxy space. Specifically we first construct coupled HR and LR dictionaries and then encode HR and LR images into a common latent code space using these dictionaries. In addition we develop an autoencoder-based framework to couple these dictionaries during optimization by reconstructing input HR and LR images. The coupled dictionaries enable our method to employ a shallow network architecture with only 18 layers to achieve efficient image SR. Extensive experiments show that our method (DictSR) can effectively model the LR-to-HR mapping in coupled dictionaries and produces state-of-the-art performance on benchmark datasets.
Template Free Reconstruction of Human-object Interaction with Procedural Interaction Generation
Authors: Bharat Lal Bhatnagar, Jan Eric Lenssen, Gerard Pons-Moll Xianghui Xie
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Reconstructing human-object interaction in 3D from a single RGB image is a challenging task and existing data driven methods do not generalize beyond the objects present in the carefully curated 3D interaction datasets. Capturing large-scale real data to learn strong interaction and 3D shape priors is very expensive due to the combinatorial nature of human-object interactions. In this paper we propose ProciGen (Procedural interaction Generation) a method to procedurally generate datasets with both plausible interaction and diverse object variation. We generate 1M+ human-object interaction pairs in 3D and leverage this large-scale data to train our HDM (Hierarchical Diffusion Model) a novel method to reconstruct interacting human and unseen object instances without any templates. Our HDM is an image-conditioned diffusion model that learns both realistic interaction and highly accurate human and object shapes. Experiments show that our HDM trained with ProciGen significantly outperforms prior methods that require template meshes and our dataset allows training methods with strong generalization ability to unseen object instances. Our code and data are released.
Deep Video Inverse Tone Mapping Based on Temporal Clues
Authors: Ning Zhang, Yang Zhao, Hongbin Cao, Ronggang Wang Yuyao Ye
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Inverse tone mapping (ITM) aims to reconstruct high dynamic range (HDR) radiance from low dynamic range (LDR) content. Although many deep image ITM methods can generate impressive results the field of video ITM is still to be explored. Processing video sequences by image ITM methods may cause temporal inconsistency. Besides they aren't able to exploit the potentially useful information in the temporal domain. In this paper we analyze the process of video filming and then propose a Global Sample and Local Propagate strategy to better find and utilize temporal clues. To better realize the proposed strategy we design a two-stage pipeline which includes modules named Incremental Clue Aggregation Module and Feature and Clue Propagation Module. They can align and fuse frames effectively under the condition of brightness changes and propagate features and temporal clues to all frames efficiently. Our temporal clues based video ITM method can recover realistic and temporal consistent results with high fidelity in over-exposed regions. Qualitative and quantitative experiments on public datasets show that the proposed method has significant advantages over existing methods.
NeRF-HuGS: Improved Neural Radiance Fields in Non-static Scenes Using Heuristics-Guided Segmentation
Authors: Yipeng Qin, Lingjie Liu, Jiangbo Lu, Guanbin Li Jiahao Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Neural Radiance Field (NeRF) has been widely recognized for its excellence in novel view synthesis and 3D scene reconstruction. However their effectiveness is inherently tied to the assumption of static scenes rendering them susceptible to undesirable artifacts when confronted with transient distractors such as moving objects or shadows. In this work we propose a novel paradigm namely "Heuristics-Guided Segmentation" (HuGS) which significantly enhances the separation of static scenes from transient distractors by harmoniously combining the strengths of hand-crafted heuristics and state-of-the-art segmentation models thus significantly transcending the limitations of previous solutions. Furthermore we delve into the meticulous design of heuristics introducing a seamless fusion of Structure-from-Motion (SfM)-based heuristics and color residual heuristics catering to a diverse range of texture profiles. Extensive experiments demonstrate the superiority and robustness of our method in mitigating transient distractors for NeRFs trained in non-static scenes. Project page: https://cnhaox.github.io/NeRF-HuGS/
Addressing Background Context Bias in Few-Shot Segmentation through Iterative Modulation
Authors: Tianrun Chen, Jianxiong Yin, Simon See, Jun Liu Lanyun Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing few-shot segmentation methods usually extract foreground prototypes from support images to guide query image segmentation. However different background contexts of support and query images can cause their foreground features to be misaligned. This phenomenon known as background context bias can hinder the effectiveness of support prototypes in guiding query image segmentation. In this work we propose a novel framework with an iterative structure to address this problem. In each iteration of the framework we first generate a query prediction based on a support foreground feature. Next we extract background context from the query image to modulate the support foreground feature thus eliminating the foreground feature misalignment caused by the different backgrounds. After that we design a confidence-biased attention to eliminate noise and cleanse information. By integrating these components through an iterative structure we create a novel network that can leverage the synergies between different modules to improve their performance in a mutually reinforcing manner. Through these carefully designed components and structures our network can effectively eliminate background context bias in few-shot segmentation thus achieving outstanding performance. We conduct extensive experiments on the PASCAL-5^ i  and COCO-20^ i  datasets and achieve state-of-the-art (SOTA) results which demonstrate the effectiveness of our approach.
Open-Vocabulary Video Anomaly Detection
Authors: Xuerong Zhou, Guansong Pang, Yujia Sun, Jing Liu, Peng Wang, Yanning Zhang Peng Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Current video anomaly detection (VAD) approaches with weak supervisions are inherently limited to a closed-set setting and may struggle in open-world applications where there can be anomaly categories in the test data unseen during training. A few recent studies attempt to tackle a more realistic setting open-set VAD which aims to detect unseen anomalies given seen anomalies and normal videos. However such a setting focuses on predicting frame anomaly scores having no ability to recognize the specific categories of anomalies despite the fact that this ability is essential for building more informed video surveillance systems. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD) in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies. To this end we propose a model that decouples OVVAD into two mutually complementary tasks - class-agnostic detection and class-specific classification - and jointly optimizes both tasks. Particularly we devise a semantic knowledge injection module to introduce semantic knowledge from large language models for the detection task and design a novel anomaly synthesis module to generate pseudo unseen anomaly videos with the help of large vision generation models for the classification task. These semantic knowledge and synthesis anomalies substantially extend our model's capability in detecting and categorizing a variety of seen and unseen anomalies. Extensive experiments on three widely-used benchmarks demonstrate our model achieves state-of-the-art performance on OVVAD task.
ODM: A Text-Image Further Alignment Pre-training Approach for Scene Text Detection and Spotting
Authors: Pei Fu, Shan Guo, Qianyi Jiang, Xiaoming Wei Chen Duan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Abstract In recent years text-image joint pre-training techniques have shown promising results in various tasks. However in Optical Character Recognition (OCR) tasks aligning text instances with their corresponding text regions in images poses a challenge as it requires effective alignment between text and OCR-Text (referring to the text in images as OCR-Text to distinguish from the text in natural language) rather than a holistic understanding of the overall image content. In this paper we propose a new pre-training method called OCR-Text Destylization Modeling (ODM) that transfers diverse styles of text found in images to a uniform style based on the text prompt. With ODM we achieve better alignment between text and OCR-Text and enable pre-trained models to adapt to the complex and diverse styles of scene text detection and spotting tasks. Additionally we have designed a new labeling generation method specifically for ODM and combined it with our proposed Text-Controller module to address the challenge of annotation costs in OCR tasks allowing a larger amount of unlabeled data to participate in pre-training. Extensive experiments on multiple public datasets demonstrate that our method significantly improves performance and outperforms current pre-training methods in scene text detection and spotting tasks. Code is available at https://github.com/PriNing/ODM.
TiNO-Edit: Timestep and Noise Optimization for Robust Diffusion-Based Image Editing
Authors: Yaron Vaxman, Elad Ben Baruch, David Asulin, Aviad Moreshet, Kuo-Chin Lien, Misha Sra, Pradeep Sen Sherry X Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Despite many attempts to leverage pre-trained text-to-image models (T2I) like Stable Diffusion (SD) for controllable image editing producing good predictable results remains a challenge. Previous approaches have focused on either fine-tuning pre-trained T2I models on specific datasets to generate certain kinds of images (e.g. with a specific object or person) or on optimizing the weights text prompts and/or learning features for each input image in an attempt to coax the image generator to produce the desired result. However these approaches all have shortcomings and fail to produce good results in a predictable and controllable manner. To address this problem we present TiNO-Edit an SD-based method that focuses on optimizing the noise patterns and diffusion timesteps during editing something previously unexplored in the literature. With this simple change we are able to generate results that both better align with the original images and reflect the desired result. Furthermore we propose a set of new loss functions that operate in the latent domain of SD greatly speeding up the optimization when compared to prior losses which operate in the pixel domain. Our method can be easily applied to variations of SD including Textual Inversion and DreamBooth that encode new concepts and incorporate them into the edited results. We present a host of image-editing capabilities enabled by our approach. Our code is publicly available at https://github.com/SherryXTChen/TiNO-Edit.
Epistemic Uncertainty Quantification For Pre-Trained Neural Networks
Authors: Qiang Ji Hanjing Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Epistemic uncertainty quantification (UQ) identifies where models lack knowledge. Traditional UQ methods often based on Bayesian neural networks are not suitable for pre-trained non-Bayesian models. Our study addresses quantifying epistemic uncertainty for any pre-trained model which does not need the original training data or model modifications and can ensure broad applicability regardless of network architectures or training techniques. Specifically we propose a gradient-based approach to assess epistemic uncertainty analyzing the gradients of outputs relative to model parameters and thereby indicating necessary model adjustments to accurately represent the inputs. We first explore theoretical guarantees of gradient-based methods for epistemic UQ questioning the view that this uncertainty is only calculable through differences between multiple models. We further improve gradient-driven UQ by using class-specific weights for integrating gradients and emphasizing distinct contributions from neural network layers. Additionally we enhance UQ accuracy by combining gradient and perturbation methods to refine the gradients. We evaluate our approach on out-of-distribution detection uncertainty calibration and active learning demonstrating its superiority over current state-of-the-art UQ methods for pre-trained models.
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving
Authors: Huangyuan Su, Nikolaos Gkanatsios, Tsung-Wei Ke, Ayush Jain, Jeff Schneider, Katerina Fragkiadaki Brian Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Diffusion models excel at modeling complex and multimodal trajectory distributions for decision-making and control. Reward-gradient guided denoising has been recently proposed to generate trajectories that maximize both a differentiable reward function and the likelihood under the data distribution captured by a diffusion model. Reward-gradient guided denoising requires a differentiable reward function fitted to both clean and noised samples limiting its applicability as a general trajectory optimizer. In this paper we propose DiffusionES a method that combines gradient-free optimization with trajectory denoising to optimize black-box non-differentiable objectives while staying in the data manifold. Diffusion-ES samples trajectories during evolutionary search from a diffusion model and scores them using a black-box reward function. It mutates high-scoring trajectories using a truncated diffusion process that applies a small number of noising and denoising steps allowing for much more efficient exploration of the solution space. We show that DiffusionES achieves state-of-the-art performance on nuPlan an established closed-loop planning benchmark for autonomous driving. Diffusion-ES outperforms existing sampling-based planners reactive deterministic or diffusion-based policies and reward-gradient guidance. Additionally we show that unlike prior guidance methods our method can optimize non-differentiable language-shaped reward functions generated by few-shot LLM prompting. When guided by a human teacher that issues instructions to follow our method can generate novel highly complex behaviors such as aggressive lane weaving which are not present in the training data. This allows us to solve the hardest nuPlan scenarios which are beyond the capabilities of existing trajectory optimization methods and driving policies.
AdaShift: Learning Discriminative Self-Gated Neural Feature Activation With an Adaptive Shift Factor
Authors: Sudong Cai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Nonlinearities are decisive in neural representation learning. Traditional Activation (Act) functions impose fixed inductive biases on neural networks with oriented biological intuitions. Recent methods leverage self-gated curves to compensate for the rigid traditional Act paradigms in fitting flexibility. However substantial improvements are still impeded by the norm-induced mismatched feature re-calibrations (see Section 1) i.e. the actual importance of a feature can be inconsistent with its explicit intensity such that violates the basic intention of a direct self-gated feature re-weighting. To address this problem we propose to learn discriminative neural feature Act with a novel prototype namely AdaShift which enhances typical self-gated Act by incorporating an adaptive shift factor into the re-weighting function of Act. AdaShift casts dynamic translations on the inputs of a re-weighting function by exploiting comprehensive feature-filter context cues of different ranges in a simple yet effective manner. We obtain the new intuitions of AdaShift by rethinking the feature-filter relationships from a common Softmax-based classification and by generalizing the new observations to a common learning layer that encodes features with updatable filters. Our practical AdaShifts built upon the new Act prototype demonstrate significant improvements to the popular/SOTA Act functions on different vision benchmarks. By simply replacing ReLU with AdaShifts ResNets can match advanced Transformer counterparts (e.g. ResNet-50 vs. Swin-T) with lower cost and fewer parameters.
SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing
Authors: Chaojie Mao, Yulin Pan, Zhen Han, Jingfeng Zhang Zeyinzi Jiang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Image diffusion models have been utilized in various tasks such as text-to-image generation and controllable image synthesis. Recent research has introduced tuning methods that make subtle adjustments to the original models yielding promising results in specific adaptations of foundational generative diffusion models. Rather than modifying the main backbone of the diffusion model we delve into the role of skip connection in U-Net and reveal that hierarchical features aggregating long-distance information across encoder and decoder make a significant impact on the content and quality of image generation. Based on the observation we propose an efficient generative tuning framework dubbed SCEdit which integrates and edits Skip Connection using a lightweight tuning module named SC-Tuner. Furthermore the proposed framework allows for straightforward extension to controllable image synthesis by injecting different conditions with Controllable SC-Tuner simplifying and unifying the network design for multi-condition inputs. Our SCEdit substantially reduces training parameters memory usage and computational expense due to its lightweight tuners with backward propagation only passing to the decoder blocks. Extensive experiments conducted on text-to-image generation and controllable image synthesis tasks demonstrate the superiority of our method in terms of efficiency and performance. Project page: https://scedit.github.io/.
MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Correlation
Authors: Yafei Mao, Raja Bala, Sunil Hadap Yuelong Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a single-shot approach to determining 6-DoF pose of an object with available 3D computer-aided design (CAD) model from a single RGB image. Our method dubbed MRC-Net comprises two stages. The first performs pose classification and renders the 3D object in the classified pose. The second stage performs regression to predict fine-grained residual pose within class. Connecting the two stages is a novel multi-scale residual correlation (MRC) layer that captures high-and-low level correspondences between the input image and rendering from first stage. MRC-Net employs a Siamese network with shared weights between both stages to learn embeddings for input and rendered images. To mitigate ambiguity when predicting discrete pose class labels on symmetric objects we use soft probabilistic labels to define pose class in the first stage. We demonstrate state-of-the-art accuracy outperforming all competing RGB-based methods on four challenging BOP benchmark datasets: T-LESS LM-O YCB-V and ITODD. Our method is non-iterative and requires no complex post-processing. Our code and pretrained models are available at https://github.com/amzn/mrc-net-6d-pose
MonoCD: Monocular 3D Object Detection with Complementary Depths
Authors: Pei Yan, Shengzhou Xiong, Xuanyu Xiang, Yihua Tan Longfei Yan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Monocular 3D object detection has attracted widespread attention due to its potential to accurately obtain object 3D localization from a single image at a low cost. Depth estimation is an essential but challenging subtask of monocular 3D object detection due to the ill-posedness of 2D to 3D mapping. Many methods explore multiple local depth clues such as object heights and keypoints and then formulate the object depth estimation as an ensemble of multiple depth predictions to mitigate the insufficiency of single-depth information. However the errors of existing multiple depths tend to have the same sign which hinders them from neutralizing each other and limits the overall accuracy of combined depth. To alleviate this problem we propose to increase the complementarity of depths with two novel designs. First we add a new depth prediction branch named complementary depth that utilizes global and efficient depth clues from the entire image rather than the local clues to reduce the correlation of depth predictions. Second we propose to fully exploit the geometric relations between multiple depth clues to achieve complementarity in form. Benefiting from these designs our method achieves higher complementarity. Experiments on the KITTI benchmark demonstrate that our method achieves state-of-the-art performance without introducing extra data. In addition complementary depth can also be a lightweight and plug-and-play module to boost multiple existing monocular 3d object detectors. Code is available at https://github.com/elvintanhust/MonoCD.
ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
Authors: Fei Pan, Junmo Kim, In So Kweon, Chengzhi Mao Chenshuang Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We establish rigorous benchmarks for visual perception robustness. Synthetic images such as ImageNet-C ImageNet-9 and Stylized ImageNet provide specific type of evaluation over synthetic corruptions backgrounds and textures yet those robustness benchmarks are restricted in specified variations and have low synthetic quality. In this work we introduce generative model as a data source for synthesizing hard images that benchmark deep models' robustness. Leveraging diffusion models we are able to generate images with more diversified backgrounds textures and materials than any prior work where we term this benchmark as ImageNet-D. Experimental results show that ImageNet-D results in a significant accuracy drop to a range of vision models from the standard ResNet visual classifier to the latest foundation models like CLIP and MiniGPT-4 significantly reducing their accuracy by up to 60%. Our work suggests that diffusion models can be an effective source to test vision models. The code and dataset are available at https://github.com/chenshuang-zhang/imagenet_d.
Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation with Deterministic Sampling Prior
Authors: Pan Zhou, Xuanyu Yi, Xiaoding Yuan, Hanwang Zhang Zike Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation
Authors: Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, Hao Dong Xiaoqi Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
BA-SAM: Scalable Bias-Mode Attention Mask for Segment Anything Model
Authors: Qianyu Zhou, Xiangtai Li, Deng-Ping Fan, Xuequan Lu, Lizhuang Ma Yiran Song
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Text-Enhanced Data-free Approach for Federated Class-Incremental Learning
Authors: Trung Le, Xuan-May Le, Mehrtash Harandi, Dinh Phung Minh-Tuan Tran
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Deciphering 'What' and 'Where' Visual Pathways from Spectral Clustering of Layer-Distributed Neural Representations
Authors: David Yunis, Michael Maire Xiao Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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).
GLaMM: Pixel Grounding Large Multimodal Model
Authors: Muhammad Maaz, Sahal Shaji, Abdelrahman Shaker, Salman Khan, Hisham Cholakkal, Rao M. Anwer, Eric Xing, Ming-Hsuan Yang, Fahad S. Khan Hanoona Rasheed
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Incremental Residual Concept Bottleneck Models
Authors: Shiji Zhou, Hengyuan Zhang, Xinzhe Ni, Yujiu Yang, Yuwang Wang Chenming Shang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World
Authors: Tanmay Gupta, Rose Hendrix, Jordi Salvador, Luca Weihs, Kuo-Hao Zeng, Kunal Pratap Singh, Yejin Kim, Winson Han, Alvaro Herrasti, Ranjay Krishna, Dustin Schwenk, Eli VanderBilt, Aniruddha Kembhavi Kiana Ehsani
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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/.
Real-Time Exposure Correction via Collaborative Transformations and Adaptive Sampling
Authors: Feng Zhang, Meng Cao, Jinpu Zhang, Yuanjie Shao, Yuehuan Wang, Nong Sang Ziwen Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives
Authors: YuXiang Zhang, Yachao Zhang, Hongwen Zhang, Jie Guo, Yan Zhang, Yebin Liu, Xiu Li Ronghui Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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
UDiFF: Generating Conditional Unsigned Distance Fields with Optimal Wavelet Diffusion
Authors: Weiqi Zhang, Baorui Ma, Kanle Shi, Yu-Shen Liu, Zhizhong Han Junsheng Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
LoCoNet: Long-Short Context Network for Active Speaker Detection
Authors: Feng Cheng, Gedas Bertasius Xizi Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.