GenAI4ELab/papercli-papers huggingface dataset:
TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models
Authors: Haomiao Ni; Bernhard Egger; Suhas Lohit; Anoop Cherian; Ye Wang; Toshiaki Koike-Akino; Sharon X. Huang; Tim K. Marks
Venue: CVPR 2024 (main)

Abstract: Text-conditioned image-to-video generation (TI2V) aims to synthesize a realistic video starting from a given image (e.g. a woman's photo) and a text description (e.g. "a woman is drinking water."). Existing TI2V frameworks often require costly training on video-text datasets and specific model designs for text and image conditioning. In this paper we propose TI2V-Zero a zero-shot tuning-free method that empowers a pretrained text-to-video (T2V) diffusion model to be conditioned on a provided image enabling TI2V generation without any optimization fine-tuning or introducing external modules. Our approach leverages a pretrained T2V diffusion foundation model as the generative prior. To guide video generation with the additional image input we propose a "repeat-and-slide" strategy that modulates the reverse denoising process allowing the frozen diffusion model to synthesize a video frame-by-frame starting from the provided image. To ensure temporal continuity we employ a DDPM inversion strategy to initialize Gaussian noise for each newly synthesized frame and a resampling technique to help preserve visual details. We conduct comprehensive experiments on both domain-specific and open-domain datasets where TI2V-Zero consistently outperforms a recent open-domain TI2V model. Furthermore we show that TI2V-Zero can seamlessly extend to other tasks such as video infilling and prediction when provided with more images. Its autoregressive design also supports long video generation.
Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels
Authors: Tianming Liang; Chaolei Tan; Beihao Xia; Wei-Shi Zheng; Jian-Fang Hu
Venue: CVPR 2024 (main)

Abstract: This paper focuses on open-ended video question answering which aims to find the correct answers from a large answer set in response to a video-related question. This is essentially a multi-label classification task since a question may have multiple answers. However due to annotation costs the labels in existing benchmarks are always extremely insufficient typically one answer per question. As a result existing works tend to directly treat all the unlabeled answers as negative labels leading to limited ability for generalization. In this work we introduce a simple yet effective ranking distillation framework (RADI) to mitigate this problem without additional manual annotation. RADI employs a teacher model trained with incomplete labels to generate rankings for potential answers which contain rich knowledge about label priority as well as label-associated visual cues thereby enriching the insufficient labeling information. To avoid overconfidence in the imperfect teacher model we further present two robust and parameter-free ranking distillation approaches: a pairwise approach which introduces adaptive soft margins to dynamically refine the optimization constraints on various pairwise rankings and a listwise approach which adopts sampling-based partial listwise learning to resist the bias in teacher ranking. Extensive experiments on five popular benchmarks consistently show that both our pairwise and listwise RADIs outperform state-of-the-art methods. Further analysis demonstrates the effectiveness of our methods on the insufficient labeling problem.
GARField: Group Anything with Radiance Fields
Authors: Chung Min Kim; Mingxuan Wu; Justin Kerr; Ken Goldberg; Matthew Tancik; Angjoo Kanazawa
Venue: CVPR 2024 (main)

Abstract: Grouping is inherently ambiguous due to the multiple levels of granularity in which one can decompose a scene --- should the wheels of an excavator be considered separate or part of the whole? We propose Group Anything with Radiance Fields (GARField) an approach for decomposing 3D scenes into a hierarchy of semantically meaningful groups from posed image inputs. To do this we embrace group ambiguity through physical scale: by optimizing a scale-conditioned 3D affinity feature field a point in the world can belong to different groups of different sizes. We optimize this field from a set of 2D masks provided by Segment Anything (SAM) in a way that respects coarse-to-fine hierarchy using scale to consistently fuse conflicting masks from different viewpoints. From this field we can derive a hierarchy of possible groupings via automatic tree construction or user interaction. We evaluate GARField on a variety of in-the-wild scenes and find it effectively extracts groups at many levels: clusters of objects objects and various subparts. GARField inherently represents multi-view consistent groupings and produces higher fidelity groups than the input SAM masks. GARField's hierarchical grouping could have exciting downstream applications such as 3D asset extraction or dynamic scene understanding. Project site: https://www.garfield.studio/
Depth-Aware Concealed Crop Detection in Dense Agricultural Scenes
Authors: Liqiong Wang; Jinyu Yang; Yanfu Zhang; Fangyi Wang; Feng Zheng
Venue: CVPR 2024 (main)

Abstract: 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.
Learning Equi-angular Representations for Online Continual Learning
Authors: Minhyuk Seo; Hyunseo Koh; Wonje Jeung; Minjae Lee; San Kim; Hankook Lee; Sungjun Cho; Sungik Choi; Hyunwoo Kim; Jonghyun Choi
Venue: CVPR 2024 (main)

Abstract: Online continual learning suffers from an underfitted solution due to insufficient training for prompt model updates (e.g. single-epoch training). To address the challenge we propose an efficient online continual learning method using the neural collapse phenomenon. In particular we induce neural collapse to form a simplex equiangular tight frame (ETF) structure in the representation space so that the continuously learned model with a single epoch can better fit to the streamed data by proposing preparatory data training and residual correction in the representation space. With an extensive set of empirical validations using CIFAR-10/100 TinyImageNet ImageNet-200 and ImageNet-1K we show that our proposed method outperforms state-of-the-art methods by a noticeable margin in various online continual learning scenarios such as disjoint and Gaussian scheduled continuous (i.e. boundary-free) data setups.
iToF-flow-based High Frame Rate Depth Imaging
Authors: Yu Meng; Zhou Xue; Xu Chang; Xuemei Hu; Tao Yue
Venue: CVPR 2024 (main)

Abstract: iToF is a prevalent cost-effective technology for 3D perception. While its reliance on multi-measurement commonly leads to reduced performance in dynamic environments. Based on the analysis of the physical iToF imaging process we propose the iToF flow composed of crossmode transformation and uni-mode photometric correction to model the variation of measurements caused by different measurement modes and 3D motion respectively. We propose a local linear transform (LLT) based cross-mode transfer module (LCTM) for mode-varying and pixel shift compensation of cross-mode flow and uni-mode photometric correct module (UPCM) for estimating the depth-wise motion caused photometric residual of uni-mode flow. The iToF flow-based depth extraction network is proposed which could facilitate the estimation of the 4-phase measurements at each individual time for high framerate and accurate depth estimation. Extensive experiments including both simulation and real-world experiments are conducted to demonstrate the effectiveness of the proposed methods. Compared with the SOTA method our approach reduces the computation time by 75% while improving the performance by 38%. The code and database are available at https://github.com/ComputationalPerceptionLab/iToF_flow.
Solving the Catastrophic Forgetting Problem in Generalized Category Discovery
Authors: Xinzi Cao; Xiawu Zheng; Guanhong Wang; Weijiang Yu; Yunhang Shen; Ke Li; Yutong Lu; Yonghong Tian
Venue: CVPR 2024 (main)

Abstract: Generalized Category Discovery (GCD) aims to identify a mix of known and novel categories within unlabeled data sets providing a more realistic setting for image recognition. Essentially GCD needs to remember existing patterns thoroughly to recognize novel categories. Recent state-of-the-art method SimGCD transfers the knowledge from known-class data to the learning of novel classes through debiased learning. However some patterns are catastrophically forgot during adaptation and thus lead to poor performance in novel categories classification. To address this issue we propose a novel learning approach LegoGCD which is seamlessly integrated into previous methods to enhance the discrimination of novel classes while maintaining performance on previously encountered known classes. Specifically we design two types of techniques termed as \underline L ocal \underline E ntropy Re\underline g ularization (LER) and Dual-views Kullback-Leibler divergence c\underline o nstraint (DKL). The LER optimizes the distribution of potential known class samples in unlabeled data thus ensuring the preservation of knowledge related to known categories while learning novel classes. Meanwhile DKL introduces Kullback-Leibler divergence to encourage the model to produce a similar prediction distribution of two view samples from the same image. In this way it successfully avoids mismatched prediction and generates more reliable potential known class samples simultaneously. Extensive experiments validate that the proposed LegoGCD effectively addresses the known category forgetting issue across all datasets e.g. delivering a 7.74% and 2.51% accuracy boost on known and novel classes in CUB respectively. Our code is available at: https://github.com/Cliffia123/LegoGCD.
Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images
Authors: JungEun Kim; Hangyul Yoon; Geondo Park; Kyungsu Kim; Eunho Yang
Venue: CVPR 2024 (main)

Abstract: 4D medical images which represent 3D images with temporal information are crucial in clinical practice for capturing dynamic changes and monitoring long-term disease progression. However acquiring 4D medical images poses challenges due to factors such as radiation exposure and imaging duration necessitating a balance between achieving high temporal resolution and minimizing adverse effects. Given these circumstances not only is data acquisition challenging but increasing the frame rate for each dataset also proves difficult. To address this challenge this paper proposes a simple yet effective Unsupervised Volumetric Interpolation framework UVI-Net. This framework facilitates temporal interpolation without the need for any intermediate frames distinguishing it from the majority of other existing unsupervised methods. Experiments on benchmark datasets demonstrate significant improvements across diverse evaluation metrics compared to unsupervised and supervised baselines. Remarkably our approach achieves this superior performance even when trained with a dataset as small as one highlighting its exceptional robustness and efficiency in scenarios with sparse supervision. This positions UVI-Net as a compelling alternative for 4D medical imaging particularly in settings where data availability is limited. The source code is available at https://github.com/jungeun122333/UVI-Net.
POCE: Primal Policy Optimization with Conservative Estimation for Multi-constraint Offline Reinforcement Learning
Authors: Jiayi Guan; Li Shen; Ao Zhou; Lusong Li; Han Hu; Xiaodong He; Guang Chen; Changjun Jiang
Venue: CVPR 2024 (main)

Abstract: Multi-constraint offline reinforcement learning (RL) promises to learn policies that satisfy both cumulative and state-wise costs from offline datasets. This arrangement provides an effective approach for the widespread application of RL in high-risk scenarios where both cumulative and state-wise costs need to be considered simultaneously. However previously constrained offline RL algorithms are primarily designed to handle single-constraint problems related to cumulative cost which faces challenges when addressing multi-constraint tasks that involve both cumulative and state-wise costs. In this work we propose a novel Primal policy Optimization with Conservative Estimation algorithm (POCE) to address the problem of multi-constraint offline RL. Concretely we reframe the objective of multi-constraint offline RL by introducing the concept of Maximum Markov Decision Processes (MMDP). Subsequently we present a primal policy optimization algorithm to confront the multi-constraint problems which improves the stability and convergence speed of model training. Furthermore we propose a conditional Bellman operator to estimate cumulative and state-wise Q-values reducing the extrapolation error caused by out-of-distribution (OOD) actions. Finally extensive experiments demonstrate that the POCE algorithm achieves competitive performance across multiple experimental tasks particularly outperforming baseline algorithms in terms of safety. Our code is available at \href https://github.com/guanjiayi/poce  github.POCE .
Learning the 3D Fauna of the Web
Authors: Zizhang Li; Dor Litvak; Ruining Li; Yunzhi Zhang; Tomas Jakab; Christian Rupprecht; Shangzhe Wu; Andrea Vedaldi; Jiajun Wu
Venue: CVPR 2024 (main)

Abstract: 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.
Masked Spatial Propagation Network for Sparsity-Adaptive Depth Refinement
Authors: Jinyoung Jun; Jae-Han Lee; Chang-Su Kim
Venue: CVPR 2024 (main)

Abstract: The main function of depth completion is to compensate for an insufficient and unpredictable number of sparse depth measurements of hardware sensors. However existing research on depth completion assumes that the sparsity --- the number of points or LiDAR lines --- is fixed for training and testing. Hence the completion performance drops severely when the number of sparse depths changes significantly. To address this issue we propose the sparsity-adaptive depth refinement (SDR) framework which refines monocular depth estimates using sparse depth points. For SDR we propose the masked spatial propagation network (MSPN) to perform SDR with a varying number of sparse depths effectively by gradually propagating sparse depth information throughout the entire depth map. Experimental results demonstrate that MPSN achieves state-of-the-art performance on both SDR and conventional depth completion scenarios.
LISA: Reasoning Segmentation via Large Language Model
Authors: Xin Lai; Zhuotao Tian; Yukang Chen; Yanwei Li; Yuhui Yuan; Shu Liu; Jiaya Jia
Venue: CVPR 2024 (main)

Abstract: Although perception systems have made remarkable advancements in recent years they still rely on explicit human instruction or pre-defined categories to identify the target objects before executing visual recognition tasks. Such systems cannot actively reason and comprehend implicit user intention. In this work we propose a new segmentation task --- reasoning segmentation. The task is designed to output a segmentation mask given a complex and implicit query text. Furthermore we establish a benchmark comprising over one thousand image-instruction-mask data samples incorporating intricate reasoning and world knowledge for evaluation purposes. Finally we present LISA: large Language Instructed Segmentation Assistant which inherits the language generation capabilities of multimodal Large Language Models (LLMs) while also possessing the ability to produce segmentation masks. We expand the original vocabulary with a <SEG> token and propose the embedding-as-mask paradigm to unlock the segmentation capability. Remarkably LISA can handle cases involving complex reasoning and world knowledge. Also it demonstrates robust zero-shot capability when trained exclusively on reasoning-free datasets. In addition fine-tuning the model with merely 239 reasoning segmentation data samples results in further performance enhancement. Both quantitative and qualitative experiments show our method effectively unlocks new reasoning segmentation capabilities for multimodal LLMs. Code models and data are available at github.com/dvlab-research/LISA.
Relightful Harmonization: Lighting-aware Portrait Background Replacement
Authors: Mengwei Ren; Wei Xiong; Jae Shin Yoon; Zhixin Shu; Jianming Zhang; HyunJoon Jung; Guido Gerig; He Zhang
Venue: CVPR 2024 (main)

Abstract: 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.
Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight Detection
Authors: Yicheng Xiao; Zhuoyan Luo; Yong Liu; Yue Ma; Hengwei Bian; Yatai Ji; Yujiu Yang; Xiu Li
Venue: CVPR 2024 (main)

Abstract: Video Moment Retrieval (MR) and Highlight Detection (HD) have attracted significant attention due to the growing demand for video analysis. Recent approaches treat MR and HD as similar video grounding problems and address them together with transformer-based architecture. However we observe that the emphasis of MR and HD differs with one necessitating the perception of local relationships and the other prioritizing the understanding of global contexts. Consequently the lack of task-specific design will inevitably lead to limitations in associating the intrinsic specialty of two tasks. To tackle the issue we propose a Unified Video COMprehension framework (UVCOM) to bridge the gap and jointly solve MR and HD effectively. By performing progressive integration on intra and inter-modality across multi-granularity UVCOM achieves the comprehensive understanding in processing a video. Moreover we present multi-aspect contrastive learning to consolidate the local relation modeling and global knowledge accumulation via well aligned multi-modal space. Extensive experiments on QVHighlights Charades-STA TACoS YouTube Highlights and TVSum datasets demonstrate the effectiveness and rationality of UVCOM which outperforms the state-of-the-art methods by a remarkable margin.
MuseChat: A Conversational Music Recommendation System for Videos
Authors: Zhikang Dong; Xiulong Liu; Bin Chen; Pawel Polak; Peng Zhang
Venue: CVPR 2024 (main)

Abstract: Music recommendation for videos attracts growing interest in multi-modal research. However existing systems focus primarily on content compatibility often ignoring the users' preferences. Their inability to interact with users for further refinements or to provide explanations leads to a less satisfying experience. We address these issues with MuseChat a first-of-its-kind dialogue-based recommendation system that personalizes music suggestions for videos. Our system consists of two key functionalities with associated modules: recommendation and reasoning. The recommendation module takes a video along with optional information including previous suggested music and user's preference as inputs and retrieves an appropriate music matching the context. The reasoning module equipped with the power of Large Language Model (Vicuna-7B) and extended to multi-modal inputs is able to provide reasonable explanation for the recommended music. To evaluate the effectiveness of MuseChat we build a large-scale dataset conversational music recommendation for videos that simulates a two-turn interaction between a user and a recommender based on accurate music track information. Experiment results show that MuseChat achieves significant improvements over existing video-based music retrieval methods as well as offers strong interpretability and interactability. The dataset of this work is available at https://dongzhikang.github.io/musechat.