Kejun Lin

Kejun Lin, Zhixiang Wang, Zheng Wang et al.

Person re-identification (re-ID) requires densely distributed cameras. In practice, the person of interest may not be captured by cameras and, therefore, needs to be retrieved using subjective information (e.g., sketches from witnesses). Previous research defines this case using the sketch as sketch re-identification (Sketch re-ID) and focuses on eliminating the domain gap. Actually, subjectivity is another significant challenge. We model and investigate it by posing a new dataset with multi-witness descriptions. It features two aspects. 1) Large-scale. It contains over 4,763 sketches and 32,668 photos, making it the largest Sketch re-ID dataset. 2) Multi-perspective and multi-style. Our dataset offers multiple sketches for each identity. Witnesses' subjective cognition provides multiple perspectives on the same individual, while different artists' drawing styles provide variation in sketch styles. We further have two novel designs to alleviate the challenge of subjectivity. 1) Fusing subjectivity. We propose a non-local (NL) fusion module that gathers sketches from different witnesses for the same identity. 2) Introducing objectivity. An AttrAlign module utilizes attributes as an implicit mask to align cross-domain features. To push forward the advance of Sketch re-ID, we set three benchmarks (large-scale, multi-style, cross-style). Extensive experiments demonstrate our leading performance in these benchmarks. Dataset and Codes are publicly available at: https://github.com/Lin-Kayla/subjectivity-sketch-reid

Qingrong He, Kejun Lin, Shizhe Chen et al.

This work addresses the 3D situated reasoning task which aims to answer questions given egocentric observations in a 3D environment. The task remains challenging as it requires comprehensive 3D perception and complex reasoning skills. End-to-end models trained on supervised data for 3D situated reasoning suffer from data scarcity and generalization ability. Inspired by the recent success of leveraging large language models (LLMs) for visual reasoning, we propose LLM-TPC, a novel framework that leverages the planning, tool usage, and reflection capabilities of LLMs through a ThinkProgram-reCtify loop. The Think phase first decomposes the compositional question into a sequence of steps, and then the Program phase grounds each step to a piece of code and calls carefully designed 3D visual perception modules. Finally, the Rectify phase adjusts the plan and code if the program fails to execute. Experiments and analysis on the SQA3D benchmark demonstrate the effectiveness, interpretability and robustness of our method. Our code is publicly available at https://qingrongh.github.io/LLM-TPC/.

Ye Wang, Ziheng Wang, Boshen Xu et al.

Temporal Video Grounding (TVG), the task of locating specific video segments based on language queries, is a core challenge in long-form video understanding. While recent Large Vision-Language Models (LVLMs) have shown early promise in tackling TVG through supervised fine-tuning (SFT), their abilities to generalize remain limited. To address this, we propose a novel post-training framework that enhances the generalization capabilities of LVLMs via reinforcement learning (RL). Specifically, our contributions span three key directions: (1) Time-R1: we introduce a reasoning-guided post-training framework via RL with verifiable reward to enhance the capabilities of LVLMs on the TVG task. (2) TimeRFT: we explore data-efficient post-training strategies on our curated RL-friendly dataset, which trains the model to progressively comprehend difficult samples, leading to better generalization. (3) TVGBench: we carefully construct a small yet comprehensive benchmark for LVLM evaluation, assessing 11 types of queries and featuring balanced distributions across both videos and queries. Extensive experiments demonstrate that Time-R1 achieves state-of-the-art performance across multiple downstream datasets using only 2.5K training data, while improving its general video understanding capabilities.