63 citations · 149 across the 13 of their papers we have counts for
13 papers
Ego-InBetween: Generating Object State Transitions in Ego-Centric Videos
Mengmeng Ge, Takashi Isobe, Xu Jia +7
Understanding physical transformation processes is crucial for both human cognition and artificial intelligence systems, particularly from an egocentric perspective, which serves a…
Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios
Xiaomin Li, Tala Wang, Zichen Zhong +7
Daily scenarios are characterized by visual richness, requiring Multimodal Large Language Models (MLLMs) to filter noise and identify decisive visual clues for accurate reasoning.…
Layout-Conditioned Autoregressive Text-to-Image Generation via Structured Masking
Zirui Zheng, Takashi Isobe, Tong Shen +12
Although autoregressive (AR) models have demonstrated remarkable success in image generation, extending these models to layout-conditioned generation remains challenging due to the…
AMD-Hummingbird: Towards an Efficient Text-to-Video Model
Takashi Isobe, He Cui, Dong Zhou +3
Text-to-Video (T2V) generation has attracted significant attention for its ability to synthesize realistic videos from textual descriptions. However, existing models struggle to ba…
ReNeg: Learning Negative Embedding with Reward Guidance
Xiaomin Li, Yixuan Liu, Takashi Isobe +8
In text-to-image (T2I) generation applications, negative embeddings have proven to be a simple yet effective approach for enhancing generation quality. Typically, these negative em…
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling
Takashi Isobe, Xu Jia, Xin Tao +6
Temporal modeling is crucial for video super-resolution. Most of the video super-resolution methods adopt the optical flow or deformable convolution for explicitly motion compensat…
List-Mode PET Image Reconstruction Using Deep Image Prior
Kibo Ote, Fumio Hashimoto, Yuya Onishi +2
List-mode positron emission tomography (PET) image reconstruction is an important tool for PET scanners with many lines-of-response and additional information such as time-of-fligh…
Towards Discriminative Representation Learning for Unsupervised Person Re-identification
Takashi Isobe, Dong Li, Lu Tian +3
In this work, we address the problem of unsupervised domain adaptation for person re-ID where annotations are available for the source domain but not for target. Previous methods t…
Multi-Target Domain Adaptation with Collaborative Consistency Learning
Takashi Isobe, Xu Jia, Shuaijun Chen +5
Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. However…
Revisiting Temporal Modeling for Video Super-resolution
Takashi Isobe, Fang Zhu, Xu Jia +1
Video super-resolution plays an important role in surveillance video analysis and ultra-high-definition video display, which has drawn much attention in both the research and indus…
Video Super-Resolution with Recurrent Structure-Detail Network
Takashi Isobe, Xu Jia, Shuhang Gu +3
Most video super-resolution methods super-resolve a single reference frame with the help of neighboring frames in a temporal sliding window. They are less efficient compared to the…
Video Super-resolution with Temporal Group Attention
Takashi Isobe, Songjiang Li, Xu Jia +6
Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention. In this work, we pro…
Intra-clip Aggregation for Video Person Re-identification
Takashi Isobe, Jian Han, Fang Zhu +2
Video-based person re-identification has drawn massive attention in recent years due to its extensive applications in video surveillance. While deep learning-based methods have led…